The proportion of retail commodities sold at market prices has kept rising

The Great Recession reduced weekly hours by 1.3 hours in the hotel sector but not in the other sectors. Thus, for most employed workers in these three sectors, weekly hours remained constant during recessions. This result contrasts with that in the agricultural sector where weekly hours rose substantially during recessions.China’s economic liberalization and structural change have proceeded for several decades. Since the economic reforms were initiated in the late 1978, China’s economy has grown substantially. For example, the annual growth rate of GDP was 8.5% in 1979-84 and 9.7% in 1985-95 . Moreover, despite the Asian financial crisis, China’s economy continued to grow at 8.2% annually between 1996 and 2000. Foreign trade has been expanding even more rapidly. China’s trade to GDP ratio increased from 13% in 1980 to 44% in 2000 . Although reform has penetrated throughout the whole economy since the early 1980s, most of the successive transformations began and in some way depended on growth in agricultural sector . After 1978, decollectivization, price increases, and the relaxation of local trade restrictions on most agricultural products accompanied the take off of China’s agricultural economy in 1978-84. Grain production increased by 4.7% per year. Even higher growth was enjoyed in horticulture, livestock and aquatic products . Although agricultural growth decelerated after 1985 after the one-off efficiency gains from the decollectivization, the country still enjoyed agricultural growth rates that have outpaced the rise in population . Despite the healthy expansion of agriculture,vetical aquaponics system the even faster growth of the industrial and service sector during the reform era has begun to transform the rural economy, from agriculture to industry and from rural to urban.

During this process, the share of agriculture in national economy has declined significantly. Whereas agriculture contributed more than 30% of GDP before 1980, it fell to 16% in 2000 . During this same time, agriculture’s share of employment fell from 81% in 1970 to only 50% in 2000. The rapid economic growth, urbanization and food market development have boosted demand for meats, fruits and other non-staple foods, changes that have stimulated sharp shifts in the structure of agriculture . For example, the share of livestock output value more than doubled from 14% to 30% in 1970 to 2000 . Aquatic products rose at an even more rapid rate. One of the most significant signs of structural changes in the agricultural sector is that the share of cropping in total agricultural output fell from 82% to 56%. Moreover, the most significant declines in crop-specific growth rates have been experienced in the grain sector . Changes in the external economy for agricultural commodities have paralleled those in domestic markets. Whereas the share of primary products in total exports was over 50% in 1980, it fell to only 10% in 2000 . Over the same period, the share of food exports in total exports fell from 17% to 5%. The share of food imports fell from 15% to 2%. Disaggregated, crop-specific trade trends show equally sharp shifts and suggest that exports and imports increasingly are moving in a direction that trend toward products in which China has a comparative advantage . The net exports of land-intensive bulk commodities, such as grains, oil seeds and sugar crops, have fallen; exports of higher valued, more labor-intensive products, such as horticultural and animal products, have risen.

The proportion of grain exports, which was only around 20% of total agricultural exports in the 1990s, is less than half of what it was in the early 1980s. By the late 1990s horticultural products and animal and aquatic products accounted for about 80% of agricultural exports . These trends are even more evident when reorganizing the trade data grouping them on the basis of factor intensity.Taken as a whole, we believe the trends of China’s economic structure and agricultural trade over the past two decades reveals that the changes that are expected to be experienced as a result of WTO are not new. Changes in the structure of economy and agricultural production and trade suggest that China was already moving towards a point that was more consistent with its domestic resource endowments. To the extent that the new trade agreements reduce barriers to allow more land-intensive products into the domestic market and the fall in restrictions overseas stimulates the export of labor-intensive crops, WTO main impact will be to push forward trends that were already happening on their own. The commitments that China provided in its WTO Protocol of Accession are largely consistent with the nation’s long-term reform plan. Despite the continuity with the past, few can dispute that the terms of China’s WTO accession agreement pose new challenges to the agricultural sector. In some cases, there will likely be large impacts on rural households, and will undoubtedly elicit a sharp behavioral response . However, the nature and severity of the impacts will not only depend on how households respond. Perhaps of even greater importance will be how China’s agricultural policy makers will manage their sector as the new trade regime takes effect. To examine this set of issues more carefully, in this section we first review agricultural policy during the reform era. In the next section we will then see how WTO measures will change the environment in rural China.

While government expenditures in most areas of agriculture have increased gradually during the reform period, the ratio of agricultural investment to agricultural gross domestic product has monotonically declined since the late 1970s. In 1978, officials invested 7.6 percent of AGDP . By 1995, the proportion of AGDP committed to investment fell to 3.6 percent. Exceptions were only recent years in the late 1990s when this ratio rose. Moreover, a significant capital outflow from agriculture to industry and rural to urban has occurred during the last two decades through the financial system and government agricultural procurement . China’s policies governing the external economy have played a highly influential role in shaping the growth and structure of agriculture for many decades. During the entire Socialist Period , the overvaluation of China’s domestic currency destroyed incentives to export effectively isolating China from international exporting opportunities.After the reforms were initiated, however, officials allowed the real exchange rate to depreciate by 400% between 1978 and 1994. Except for during the past few years when the exchange rate has experienced a slightly re-appreciation, adjustments in the exchange rates throughout most of the reform period have increased export competitiveness and contributed to China’s export growth record. These, in turn, have helped the overall expansion of the national economy. Perhaps more than anything, China’s open door policy, including its exchange rate policy, has contributed to the rapid growth in the importance of the external economy.The shift of labor from the rural sector to the urban sector lies at the heart of a country’s modernization effort and China has been experiencing this primarily two ways: by the absorption of labor into rural firms and by movement of massive amounts of labor into the off farm sector in cities. Rural industrialization has played a vital role in generating employment for rural labor, raising agricultural labor productivity, and farmer’s income. The share of rural enterprises in GDP rose significantly from less than 4% in the 1970s to more than 30% by 1999.

REs have dominated the export sector throughout the 1990s . And, perhaps most importantly, REs employ 35% of the rural labor that works off the farm. In addition to formal wage earning jobs in rural areas, a large and rising part of the rural labor force also works in the self-employed sector. At the same time, although China’s factor markets still contain a number of structural imperfections, such as employment priority for local workers, housing shortages,farming vertical and the urban household registration system, labor has poured into the cities during the last 20 years and labor markets emergence are transforming the economy.According to a nearly national representative survey of 1200 households across China, it is found that more than 100 million rural workers found employment in the urban sector in the late 1990s . In fact, to an extent never found before, China’s labor markets have allowed migration to become the dominant form of off-farm activity; been increasingly dominated by young and better educated workers; expanded fastest in economies or areas that are relatively well-off; and recently begun to draw workers from portions of the population, such as women, that earlier had been excluded from participation. According to the work on some researchers, if China continues to change at the pace it has in the past 20 years, and other provinces experience the same changes that have already occurred in the richest provinces, China’s economy will continue to follow a healthy development path and be on the road to modernization.China has a strong agriculture research system that has generated technologies adopted by millions of farmers to meet the increasing demand of food and agricultural products in the most populous country in the world . All previous studies consistently show that research-led technological change is the main engine of agricultural growth.Technology produced by China’s agricultural research system accounts for most of the rise in the cropping sector’s total factor productivity between 1980 and the late-1990s . Despite this past record, China faces considerable challenges. Although as a publicly funded agricultural research system, it functioned well and addressed many important problems, its expenditures have been tied to public budgets. Falling fiscal support has taken its toll. Currently, there is much concern that agriculture research investment intensity has declined since the early 1980s and reached a dangerously low level, only 0.44 in 1999 . At the same time, the increasing evidence of overlapping, inefficiency, over-staffing, and inappropriate technology make fundamental reform of the current research system an essential task.Price and market reforms were key components of China’s policy shifts from a socialist to a market-oriented economy. The reforms associated with China’s policy reforms, however, began slowly and have proceeded gradually. Market liberalization began with non-strategic commodities such as vegetables, fruit, fish, livestock, and oil and sugar crops. Little effort was made on the major crops. And, although the aims of the early reforms were to raise farm level prices and gradually deregulate the market, most of the significant early reforms were done by administrative measures .

However, as the rights to private trading were expanded in the early 1980s, and official allowed traders the to buy and sell the surplus output of almost all categories of agricultural products, the foundations of the state marketing system began to be undermined. Since the mid-1980s, market reforms have continued though only in a stop and start way. For example, after record growth in agricultural production in 1984 and 1985, a second stage of price and market reforms was announced in 1985 aimed at radically limiting the scope of government price and market interventions and further enlarging the role of market allocation. Because of the sharp drop in the growth of agricultural production and food price inflation in the late 1980s, however, implementation of the new policy stalled. Mandatory procurement of grains, oil crops, and cotton continued. After agricultural production and prices stabilized in 1990-92, another attempt was made in early 1993 to abolish the grain compulsory quota system and the sale at low prices to consumers. The state distribution and procurement systems were substantially liberalized, but the policy was reversed when food price inflation reappeared in 1994: government grain procurement once again became compulsory. As well, a provincial governors’ grain responsibility system was introduced in 1994-95, aimed at encouraging greater grain self-sufficiency at the provincial level. Further retrenchments followed; in 1998 the central government initiated a controversial policy change prohibiting individuals and private companies from procuring grain from farmers . Grain quota procurement prices were set more than 20% higher than market prices, which meant a transfer in favor of those farmers able to sell at that price . Not surprisingly, stocks started to accumulate and procurement and market prices had to come down relative to international prices in 2000. Despite these periodic cycles in the reform process, markets have gradually emerged in rural China.According to Lardy , the share for agriculture was just 6% in 1978 but had risen to 40% by 1985, 79% by 1995 and 83% by 1999. Moreover, the state’s intervention was unable to halt the flow of grain across provincial boundaries. Huang and Rozelle find that agricultural prices for all major commodities, including rice, wheat, and especially for maize and soybeans have moved together across far reaching localities within China.

There is also a literature that challenges the dominant role of agricultural growth for poverty reduction

From the point of view of our exercise, this greater variance produces a ‘bias’ in the resulting estimates of the connection between agricultural income and welfare, since we are interested not in the short-run effect of things like weather shocks on expenditures but on the longer-run effects of things like improvements in agricultural productivity. We are also concerned about the related issue of endogeneity; even the simplest general equilibrium models with investment imply simultaneity in the determination of income and expenditures. We address these issues using a simple instrumental variables strategy, using averages of neighboring countries’ sectoral income growth as instruments for own-income growth . Fifth, even after controlling for time, continent, and decile fixed effects in growth, we are concerned that there may be heterogeneity across countries in the way agricultural income growth affects households in different parts of the expenditure distribution. We explore this possible heterogeneity by interacting various fixed or pre-determined country characteristics with income growth from different sectors, reporting those results in Section 5.4. We summarize our main results. First, poorer households’ expenditures grow more in response to growth from agriculture than do the expenditures of wealthier households, and this holds across all deciles. We call this result monotonicity, and it is both very robust and important.

Monotonicity also holds for growth from non-agricultural sources, but in the opposite direction, vertical farming aeroponics with wealthier households’ expenditures responding more than poorer households’. Second, it is not just across deciles that we see an effect: within poorer deciles, households benefit significantly more from growth in agriculture than they do from growth in other sectors. Third and finally, the connection between expenditure and sectoral income growth is importantly and significantly different across different groups of countries. In particular, it is the poorest households in the poorer countries for whom agricultural income growth is most important.From a theoretical standpoint, a long tradition of dual economy models that aggregate the economy into two sectors—agriculture and non-agriculture—has served to identify the transmission mechanisms of an exogenous agricultural productivity increase on welfare . Transmission mechanisms include employment, food prices, real wages, and the demand for non-tradable goods produced in the rural non-farm economy. The tradition in the dual economy literature is to assume that consumption expenditures are equal to real income and that labor income is the source of expenditures while capital income is saved and invested. An increase in growth in one sector would affect the welfare of only the part of the population actually employed in that sector. If expenditures are distributed differently across households in the two sectors, then an increase in employment in one sector will have an effect on the aggregate distribution of expenditures. If, for example, households employed in the agricultural sector tend to be poorer, an increase in agricultural employment will have an equalizing effect on the entire distribution of expenditures .

For a country with a closed economy , an increase in agricultural productivity induces a decrease in food prices. All consumers benefit from lower food prices, but most particularly the poor, who typically spend a larger share of their income on food . If there is surplus labor and wages are tied to the cost of living to secure a fixed real subsistence wage, lower food prices can induce a decrease in the nominal wage, fostering employment and growth in the non-agricultural sector . When workers are mobile and wages are equated across sectors, differences in the rate of growth of different sectors can result in changes in the distribution of expenditures through the employment effect. For example, Loayza and Raddatz formulate a model in which expenditures of the poor are equal to the prevailing wage, while non-poor households can borrow or lend to smooth away the effects of variation in labor income on expenditures . The model shows that the effects of sectoral growth on real wages are larger for sectors with larger employment and a lower elasticity of demand for labor, namely agriculture and services. Another strand of literature is based on a three-sector aggregation of the economy, with a non-tradable sector in addition to the agricultural and, say, manufacturing sectors. A key determinant of the overall effect of an initial growth impetus in agriculture is the linkages created in fostering demand for the non-tradable sector products . To the extent that labor is not fully mobile, then in addition to asymmetric effects on the functional sources of income any growth that originates in the rural economy stands to have a more direct impact on the rural population, where many of the poor live. Much of the empirical support to the claim that agricultural growth is good for aggregate growth, employment, and welfare is based on simulation models that rely on demand and supply elasticities that are not estimated.

Thorbecke and Jung use social accounting with postulated elasticities applied to Indonesia, thus finding that agriculture and services contribute more to poverty reduction that the industrial sectors. Within-country or within-region studies arguably offer the best evidence we have on the connection between aggregate agricultural income growth and household welfare, perhaps because in these contexts one can construct a proper panel dataset. In an important series of papers Datt and Ravallion use panel data for states in India and show a systematic and relatively uniform association between agricultural growth and poverty reduction, but a very heterogeneous relationship between non-agricultural growth and poverty change. With province-level panel data for China over the period 1985–1996, Fan et al. find that agricultural growth is associated with a reduction of rural poverty while non-agricultural growth is associated with an increase in rural poverty. With provincial data for 1983–2001, Montalvo and Ravallion show that the primary sector was the driving force behind the spectacular decrease in poverty in China. Suryahadi et al. conduct an exercise similar to that of Ravallion and Datt but for Indonesia, and are able to distinguish between the rural and urban poor. They find that growth in services is good for both the rural and urban poor, with the effects of agricultural growth focused more specifically on the rural poor. In a similar spirit, Warr uses national data from four Asian countries from the 1960s to 1999 in a panel analysis and finds similar results, in that growth in agriculture and services were associated with a decrease in poverty, with the estimated coefficient on agriculture substantially smaller than the coefficient on services, and the coefficient on manufacturing not significantly different from 0. Looking at the 25 countries with the greatest success at reducing extreme poverty under the period of the Millennium Development Goals, Cervantes-Godoy and Dewbre find that while economic growth was a key determinant, growth in agricultural incomes was especially important. Bresciani and Valdes provide evidence of the role of agricultural growth on poverty reduction through rural labor markets, farm incomes, food prices, and economy-wide multipliers in different country case studies.

Other studies have resorted more systematically to cross-sectional country-level time series data, thus looking for average effects across a large set of countries and hence economic structures. Using data from 80 countries spanning 1980 to 2002, Christiaensen et al. find a stronger association between overall poverty decrease and growth originating in agriculture than growth originating in either of the other two sectors. With higher participation, slower growth of agriculture may still deliver more poverty reduction than the growth of non-agriculture. In contrast, using a slightly different method, Bravo-Ortega and Lederman find that in Latin America, it is the non-agricultural sector that has the strongest effect in reducing poverty. Focusing on the role of the unskilled labor market, Loayza and Raddatz find evidence that growth in income from sectors with high unskilled labor shares has a disproportionate effect in reducing poverty rates. In a somewhat different specification, Dollar et al. regress growth rates in incomes of the poorest 20 percent on growth in average income and on changes in the share of agriculture in GDP. The significance of the coefficient on the agricultural variable suggests that, even controlling for aggregate growth, faster growth in agriculture is likely to disproportionately benefit the poor. Lanjouw et al. for example argue that it is the non-agricultural sector in the rural areas that is both more dynamic and more pro-poor,vertical indoor hydroponic system and hence the most important contributor to poverty reduction in rural India. Collier and Dercon note that productivity in agriculture, and especially in the smallholder sector, is so low that economic development and poverty alleviation in Africa will have to come from a radical transformation of the agricultural sector and massive exodus from agriculture. They also cite works on the role of migration in the reduction of poverty in rural areas. Most of the literature that cautions against the importance given to agriculture for poverty alleviation however relates to a different argument: while the relatively strong poverty impact of agricultural growth seems to be a fairly robust result, the cost of investing to obtain a given growth is far higher in agriculture than in other sectors, making it an inefficient instrument for growth and welfare . Our paper does not address this issue at all, but aims at contributing to the literature on the sectoral growth-poverty linkage. An issue in almost all of the studies we have discussed is simultaneity between sectoral growth and the welfare indicator used in the analysis. A contribution of this paper is to tackle this issue by using an instrumental variable approach to try to measure the effect of an exogenous increase in sectoral growth on welfare.

We use the same database collected by the World Bank as do other cross country analyses, although we only select the countries for which welfare is measured by consumption expenditures.2 We also use data on all deciles, rather than only on e.g., poverty rates, as in Christiaensen et al. and other studies described above. When using cross-country evidence on changes in the distribution of income or expenditures one has to make an early choice regarding whether it is better to consider the distribution of these welfare measures within countries or across countries. The former choice leads to an empirical strategy that groups together different welfare quantiles across countries, so that for example, one imagines that the poorest 10 percent of households in Tanzania are similarly positioned to the poorest 10 percent of households in China, despite the substantial differences in the level of real expenditures of the quantile across these two countries. The latter choice construes distribution as a global phenomenon, with the result that the poorest 10 percent of all households globally may all be located in a very small number of countries. If what we want to measure is the global distribution of welfare one also logically ought to weight countries by their populations in any cross-country analysis. different researchers have made different choices.3 In this paper we take the country focused approach, and analyze the relationship between welfare and sectoral growth of all deciles of the distribution within countries, rather than on a measure of poverty level or distribution across countries.4 Over the last several decades, the World Bank has accumulated a large number of datasets from a large number of developing countries which are based on household-level surveys, statistically representative of the populations of those countries, and which include data on non-durable goods expenditures. Though the micro-data from these surveys are not generally available, the World Bank provides data on aggregate expenditures by decile for many of these countries. Our sample is restricted to the countries and years for which we have information on expenditures data for at least three points in time . The sample covers 62 countries, with variable numbers of observations over 1978 to 2011, totaling 310 surveys. This sample of countries and years is not a random sample of the countries of the world. Instead, it is a sample of countries where household expenditure surveys have been conducted . It has however a large coverage, including 81% of the population in low and middle-income countries in 2000. In terms of continents, the sample includes 97% of the population of South Asia, 70% of Sub-Saharan Africa, and 20% of Latin America and Caribbean.There is no clear bias in this sampling of developing countries except for the obvious and egregious absence of all but one Latin American countries.

The process of carbon sequestration can be accelerated by coconut plantation and inter crop management

Based on a field study, Kumar et al.reported that the presence or absence of over-canopy had little effect on the rhizome yield of galangal , a medicinal plant, implying its shade tolerance.Being a shade-tolerant crop, galangal yield remained steady across a wide range of light availability conditions, from full light to a photosynthetic photon flux density as low as 18% of that in the open.Woody perennials such as cacao , cinnamon , clove , coffee and nutmeg , and vines and creepers such as sweet potato and vanilla can also tolerate shade to varying degrees.Species are also grouped into obligate or facultative shade plants and obligate or facultative sun plants based on their light requirements.Nonetheless, rigorous studies on the nature, mechanisms, inheritance, and management of shade adaptability of understory species in CBFS are lacking.Being a single-stemmed woody perennial with oucambium, the palm’s main stem does not develop radially with age.The crowns are likewise rather narrow, measuring 5 to 6 m in breadth.This unique growth form of the coconut palm allows significant light infiltration into the understory in an even-aged stand.A related aspect is the uniform spatial arrangement of the palms.In Kerala, coconut palms are typically planted at 7.6–9 m apart, with a population density of 120 to 170 palms per hectare.Likewise, an average density of 148 trees per hectare was reported from Melanesia’s smallholder coconut plantations.Although designed to meet the growth requirements of mature palms, this wide spacing typically results in inefficient use of site resources and a lack of full site occupancy by the main crop throughout the majority of its life cycle.

In the field study mentioned above, Kumar and Kumar found that understory light transmittance for mixed coconut+multipurpose tree stand ranged between 6 and 75% of that in the open, depending on the time of the day, tree species involved and planting geometry.According to Thomas et al.,4x8ft rolling benches only around25% of the land is properly utilized, when monocropping is practiced in coconut gardens.Furthermore, the grower receives little or no returns from the palms throughout their immature stage, which can last up to 10 years, while the intercrops provide some returns.As a result, mixed species agroforestry systems aimed at increasing spatial and/or temporal complementarities in resource utilization, as well as providing additional returns, have become a unique feature of the coconut-growing regions in the tropics.What explains the functioning of such sophisticated agroecological models is perhaps the “Niche-complementarity hypothesis”.It implies that a bigger suite of species occupying a site may lead to better resource partitioning and utilization making the system more productive than systems involving fewer number of species.Consistent with this, Liyanage and Dassanayake reported increased nut yields when pasture species , black pepper and coffee were inter cropped with coconut.Such beneficial effects of inter cropping have been attributed to improved nutrition of the palm through complementary resource sharing, better retention of soil moisture, reduced weed competition and improved soil quality.

Competition for site resources between coconuts and the associated plants, however, could be a potential problem.Such interactions may be either above ground or below ground.Section 5.1.4 describes the below ground interactions.Furthermore, the nature of inter specific interactions will vary depending on the stage of coconut stand development.A synthesis of the published reports, nevertheless, indicates that growing trees in the inter spaces does not have a strong adverse impact on the yield of coconut palms, except in situations where such trees impede light availability of the palms.Species mixtures generally ensure spatial complementarity in resource use as the components occupy different niches, although the tree-crop interactions may change with time and planting geometry.Although coconut-based polycultural systems are ubiquitous, below ground interactions of woody perennials in such mixed-species systems are rarely studied due to methodological challenges.Furthermore, results from the available studies are also not consistent, implying that the interactions may be either complementary or competitive.Nelliat et al.reported horizontal and vertical stratification of coarse roots in “adequately and separately fertilized multi-storied combination of coconut, cacao and pineapple”.Conversely, Pandey et al.found that the coconut root systems were at close proximity to the intercrops in well fertilized polycultural systems involving three 20-year-old tree species,implying competitive nutrient withdrawal by the coconut palms.Using the 32P soil injection technique, Kumar et al.and Gowda and Kumar investigated root competition in the coconut + dicot MPT agroforestry system.According to Kumar et al., 32P uptake by coconut palm in a species mixture was higher than that of a sole coconut stand, owing to increased subsoil root activity in the former, implying that the coconut root system may grow deeper in mixed-species systems compared to sole coconut systems.Gowda and Kumarexamined root interactions between coconut and dicot trees along a soil fertility gradient.

Notwithstanding major differences in the nutrient status along the gradient as well as dicot tree root characters, uptake of 32P by the coconut palms was not substantially different, signifying non-competitiveness of the associated dicot tree components for P.Nevertheless, the interplanted dicot trees captured significant quantities of the radio-label supplied to the coconut palm, implying a “scavenging effect” by these trees that, in turn, minimizes the potential for lower leaching of nutrient elements.Coconut-based farming systems often involve mixtures of trees that occupy different soil strata and this may entail a certain degree of spatial complementarity in resource use.Occurrence of two or more woody species in mixtures also favors diminished lateral spread and/or facilitates deeper root penetration of the components.In the coconut+dicot tree system investigated by Gowda and Kumar mentioned above, the interplanted dicot trees absorbed considerable quantities of the radio-label applied to the palm, which declined log-linearly with distance from the palms, signifying a substantial potential for “capturing” the lower leaching nutrients, at proximal distances.Proximity of the associated tree component, therefore, is a strong determinant of such plastic responses in tree root distribution.Gowda and Kumar also reported that some dicot species in the coconut+dicot tree mixture developed deeper root systems , while others produced increasingly spreading root systems , denoting that root architecture of mixed tree plantations is species dependent.Thus, there is a need for proper selection of the component crops and their manipulation to optimize productivity in coconut ecosystems.An array of ecosystem services such as provisioning, regulating, supporting, and cultural services are provided by the coconut based multi-strata, multi-species ecosystems.This includes crop species yielding food, fiber, fuel, fodder, timber, medicine, and other basic necessities , besides cash returns.The diverse range of crops integrated into CBFS producing fruits, nuts, drinks , edible oils and cakes, fiber, foliage, timber, bio-fuels, vegetables, spices, and medicinal plants justifies the sobriquet “coconut-based food forests”.The coconut palm also yields organic coconut water, virgin coconut oil, functional foods and health drinks like neera , coconut sugar, cosmeceuticals, oleochemicals, and bio-lubricants and is a popular ingredient in the cuisines of many countries in South and Southeast Asia.Furthermore, the coconut palm produces edible copra for the extraction of coconut oil, as well as desiccated coconut powder, fermented sap, and sap jaggery, among other culinary items.

Also available in both domestic and international markets are a variety of value-added products from coconut oil such as soap, body oil and perfumed hair oil, and kernel-based products such as coconut chips, coconut cream, coconut milk powder, white soft coconut cheese, coconut yoghurt and so on.Tender coconut water is a healthier alternative to many carbonated beverages due to its nutritious properties.Apart from being an important dietary component, the coconut palm and the associated species yield diverse range of aesthetic and artisanal products.Coconut wood is an excellent structural material that is used in the construction of buildings, furniture, flooring, and paneling, and the fabrication of high-end products like handcrafted, biodegradable,flood and drain table and sustainable coconut bowls, as well as for the making of charcoal, chemicals, pulp, and paper.In experimental studies, the mechanical properties of coconut wood compared quite well with those of other structural timbers such as teak , wild jack and the like.Coconut wood thus supplements the supply of raw materials for the wood industry and provides low-cost and durable construction materials.Because of its availability and renewability, coconut’s sustainability can add value to this construction material and thus help to conserve the remaining natural forests, by offsetting the pressure on them.Additionally, CBFS provides byproducts such as coconut shells and fibre, which are presumably underutilized but constitute vital raw materials for cottage enterprises.Coconut shell is a useful bio-fuel as well, despite its relevance as an alternative fuel in homes and small businesses.In addition to offering an alternative and better source of fuel than fuel wood and other traditional fuels, using coconut shell as a fuel reduces CO2 emissions and sanitizes the environment of the harmful hard shell.The husk usually forms 35–45% of the weight of the whole nut when ripe.About 30% of the husk is fibre and 70% is coir dust.The industry uses just about 35% of the total husk available, while there is scope for economically utilizing at least 50% of the husk produced.Coir fiber and coir pith are two important products made from coconut husk.The fibers are used for spinning into yarn for manufacturing mats and mattings, ropes, twines, etc.Pith, which is usually mixed with short fifibers and contains mainly lignin, cellulose, and hemicellulose, is used as a manure and has a variety of industrial applications too.Agrobiodiversity being the critical feature of NbS, CBFS offers innumerable opportunities for integrating diverse forms of crops in the same land management system.

Such systems have provided sustenance, nourishment and livelihood security to large segments of Kerala’s rural and peri-urban populations for millennia, as in other parts of South and Southeast Asia.As described in Table 2 and Section 5, many functional groups of plants, such as food crops , permanent plantation crops, medicinal plants, multipurpose trees, and others, are associated with CBFS, implying their potential to conserve biodiversity in managed ecosystems.Such integrated farming systems generally outperform mono specific production systems in all major aspects of multifunctional agriculture, including food security, environmental functions, economic functions, and social functions.The coconut palm is also very resilient as it can withstand natural calamities like typhoons and flooding.In general, woody perennial-based mixed-species land use systems have the potential to address natural calamities such as droughts, floods, and high temperatures as a consequence of climate change.Improvements in soil organic matter status and water holding capacity, and the resultant yield gains, are also integral features of the coconut-based ecosystems.Osei-Bonsu et al.observed higher soil moisture retention in cacao + coconut mixture in Ghana compared to cacao + Gliricidia sepium system.From Sri Lanka, Arachchi and Liyanage also reported improved soil organic matter status, bulk density, aeration, and water content in the soil profiles of acacia and gliricidia interplanted plots compared to that of sole coconut and Calliandra calothyrsus and L.leucocephala intercropped plots.Although global warming and the consequential faster soil organic matter turnover may exacerbate the deterioration of nutrient-poor tropical soils, such obstacles are less likely in coconut-based multi-strata production systems than in mono specific stands, emphasizing the CBFS’s sustainability.Another major characteristic of CBFS is enhanced carbon capture and storage in soil-crop systems, which has the potential to minimize CO2 emissions.This includes carbon sequestration in soil and biomass , as well as the substitution of fossil fuels with bio-diesel made from biomass or coconut oil.However, only few studies have characterized the carbon sequestration potential of coconut-based ecosystems.The available reports suggest that tree plantations signify remarkable carbon pools as trees hold much more carbon per unit land area than other categories of vegetation, and CBFS has huge potential as a carbon sink.Consistent with this, Navarro et al.reported that coconut plantations exhibit high productivity typical of the tropical humid evergreen forest ecosystems.Ranasinghe and Thimothias estimated that the ecosystem carbon stock of CBFS in Sri Lanka ranged from 32 to 72 Mg C ha–1, while the net carbon balance ranged from 0.4 to 1.9 Mg C ha–1 month–1 under various growth conditions.Carbon storage by coconut palms in mixed stands is clearly greater than that of sole stands, especially when the species-mix involves trees.For instance, in a system involving different inter cropped fruit trees such as guava , litchi , sapota and custard apple grown in association with coconut, Manna et al. reported higher soil carbon sequestration for mixed-species systems than sole coconut.Nutrient management of CBFS is yet another important determinant of soil carbon sequestration, and improved nutrient management may augment the carbon sequestration potential.

Most stakeholders interviewed in this study share a demand for applying AI to agriculture

Agricultural farms are extremely pressed to get a rewarding return on their investments which leads to, at times of the year with high workload, farmers not getting much sleep at all.This is confirmed by a farmer who says that since he works so much, some hours are nearly unpaid.Implementing AI in agriculture could potentially mitigate these intense periods of large workloads somewhat, which would give social values back to the farmers.Another dimension of investments and implementation of new technology in agriculture, is that investments in smart farming are not always viewed as necessary by farmers but rather something neat and trendy.Thus, such investments are described to be paid by the “amusement account”.This is confirmed by a farmer that states that most of the technological investments made on his farm are motivated by his interest and fascination with technology.Respondent C4 says that a lot of farmers gladly spend money on new and exciting tools and machines, for instance new tractors.From this, it seems like many farmers think that the charm of running an agricultural business is to be able to tailor and adapt the farm according to one’s liking.While some respondents like doing things very manually others like to develop their way of working consistently with new types of technology.To summarize the results of this interview study, the themes and topics are divided into what appears to be the demands or opportunities for AI in agriculture, as well as the barriers or hurdles that hinder the use of it.Furthermore, based on the contrastive responses and views of different groups of respondents,hydroponic farming the demands and barriers are differentiated by the respondent groups that all have distinct roles in the agricultural sector.

Table 2 shows an overview of the most important points from the interviews, divided over the different respondent groups.To begin with, the responses from farmer respondents show that there are many opportunities linked to the usage of AI and smart farming technologies in agriculture.Most importantly, according to them, new smart farming technologies have the potential of increasing their profitability, either by contributing to higher revenues or freeing time spent on some tedious tasks.On the other hand, the large initial costs to set up the technologies are identified as a barrier.However, if economical means allow for investing in such solutions, farmers believe that the investments will pay off in terms of profitability and competitiveness.Other factors that act as demands for smart farming technologies are their potential to be more sustainable and that they make farming more fun.Further barriers according to farmers are the complex solutions and lack of interoperability, as well as the poor prerequisites and opportunities of continuous education regarding technology in agriculture.Also, the fickle market makes smart farming risky to invest in for farmers.From a commercial enterprise point of view, there are many opportunities connected to smart farming, but also some critical barriers to overcome.The respondents of this group see potential in increased cooperation between companies as well as with farmers, business cases in providing Software as a Service and additionally to streamline logistics connected to agriculture.Nevertheless, data sharing and cybersecurity are seen as large hurdles to the use of these technologies.Respondents from research institutes also express a positive view on accelerated use of AI in agriculture.They believe such a development would result in more data collected by the farmers, which would decrease the time researchers themselves spend on gathering data.This would, according to the researcher respondents, lead to a faster and better research on agriculture.However, data sharing hinders, once again, the scientific development since high-paced research is hard to conduct without proper access to data from different sources.An additional identified barrier for smart farming is the mistrust from farmers that the scientifically developed solutions mirror a real agricultural demand and are not just developed for the sake of technology.

Finally, the respondents from governmental agencies claim that there is a great interest and demand for propagating smart farming technologies for national competitiveness as well as other economic reasons.Still, they are not sure how to position themselves in this transition, which slows down the process of digitizing the agricultural sector.This respondent group also views cybersecurity and data sharing as critical barriers to overcome.This paper provides a review of the main opportunities and hurdles for applying AI to agricultural businesses.By conducting a structured literature review and an interview study with 21 respondents from various parts of the agricultural industry, data has been gathered to get a holistic view on the use of smart technology in agriculture.The scope of the thesis is deliberately wide, focusing on three agricultural sectors: arable farming, milk production and beef production.Furthermore, the respondents are categorized by their role in the sector, ranging from governmental authorities, commercial enterprises, researchers as well as farmers.This broad view allows to acquire knowledge that ranges over several production sectors, as well as over several kinds of organizations with different views on the agricultural sector.Driving the farmers towards smart farming technologies are the needs for increased profitability, reduced workload and often a genuine curiosity for new technology.Surprisingly, all these aspects are not completely captured in the literature review.For example, there are studies about the impact smart farming can have on the relation between humans and animals on a farm, but they did not show in the literature review search.On the contrary, some expected drives for smart farming were not expressed by the respondents, such as the advantageous impact that smart farming can have on the environment through less nutrient loss.Instead, profitability stands out as the most influential factor which makes a clear business case an essential requirement connected to the propagation of smart farming technologies.

Since more and more agricultural products become available in the form of SaaS, allowing for sharing and renting equipment, the business case is changing for both farmers and machine producers, opening new possibilities.Nevertheless, for smart farming to really transform the agricultural sector, governmental agencies and commercial enterprises might need to take a more active role in the transition.Such aspirations are especially important to ensure that the governmental and societal demand for reduced emissions and increased sustainability is met in the technological shift.For the transformation to be successful, it is essential that the structures, allowing farmers to apply the smart farming technologies, are modern.One key requirement is that farmers have continuous and easy ways to acquire up-to-date knowledge of how to apply smart farming.Therefore, there is a need to ensure technical, agricultural education which is easily accessible through for example flexible, on-demand courses.Additionally, the smart farming techniques need to be modifiable to match the varying transparency and adaptability demands that different farmers have.Regarding how implementation and propagation of AI in agriculture might be hindered, this study identifies some factors that act as barriers.The most prominent one is how data is managed, which can be further specified to data sharing and ownership as well as cybersecurity.This is a complex question that as of now does not have a clear solution, neither technically nor legally.Here lays an important role for research institutes as well as authorities.However, there is a consensus among respondents that to transition the agricultural sector into a more data-driven and digital environment, the technical infrastructure must be secure.The solution must be able to guarantee that sensitive data is not available for intruders while at the same time guaranteeing access for the intended users.Furthermore, for the end users to be able to benefit from the digitalizing transition of the sector, the data models require a high degree of flexibility.This stems from the wide variety of machinery at farms as well as the varying level of technological interest and knowledge among the farmers.Moreover, an important aspect that slows down the process of implementing smart farming technologies and AI in agriculture is the economical dimension expressed by the respondents.

A large part of this are of course the high investment costs, but other economic aspects also play a part in this barrier.For example, the fickle market demands, the general low profitability in agriculture as well as the trend towards consolidation of farms all contribute to making investments full of risk.Other identified barriers that hinder the spread of AI in agriculture are some social factors, for example the concerns about technological over-dependency and insufficient end user trust towards technology.The lacking trust seems to stem from over-selling from developers of technology as well as a gap between the technology that is developed and the real market demands.As for the technical solutions that could potentially solve the demand for AI and smart farming technologies, there are many possible ways.In this study, findings show that a lot of the data and sensors types already exist.The problem that remains to be solved is to connect the input data to the output data by developing the datasets, and thereby closing the data cycle.Today, the dairy sector generally holds a closed and elaborate data cycle whereas generally the meat and arable sector have less developed data gathering and therefore less precise decision support tools.This is highlighted in both the interviews and the literature review, as high-resolution data allows for more precise and detailed decision support.Although, after a thorough process of data gathering from input to output, one can build models and evaluate which one of them performs best with some specified evaluating metrics.Additionally, a general problem and difficulty in building machine learning models is that models tend to take too many variables at the same time.The results show the importance of ‘starting small’ when building the models, i.e.using few input variables to begin with and then tune the model adding only one more variable at a time.It is also found that all possible use cases and technical solutions demand a high precision for classification model output as well as low prediction errors for regression models.Decision support in agriculture manages and affects core parts of the agricultural business, and therefore it is important that estimations and predictions are accurate.Interestingly, respondents from the arable sector express that they, as of now,hydroponic equipment accept higher levels of total error in the model.However, for future purposes and solutions with increased complexity, the total error must decrease which is likely to affect the bias- variance trade-off.A requirement for achieving precise supervised machine learning models, adapted to the local farm, will be easy pre-processing of the data.Thus, the data labeling process must either be simplified by developers or offered to the farmers as a service by consultants.

Technologically, the agricultural sector has developed for decades, but the shift towards smart farming techniques and data-driven agriculture might be one of the greatest transitions.Applied AI in agriculture has the potential to optimize and streamline agricultural activities in all sectors in agriculture.By data-driven decision support, and even tasks performed completely automatically, farmers hope to improve their output both in terms of quantity and quality, mitigate carbon emissions, decrease work time, and increase profits.For commercial enterprises and governmental agencies, the transition allows for updated supply chains and planning models, improving the agricultural industry on a macro-level.Still, several challenges remain unsolved, jeopardizing the speed of the transition.Here, there are important tasks for companies, authorities and research institutes.Nevertheless, with such strong incentives, the long-term trend towards increased usage of AI in agriculture is clear.The question is no longer if smart farming will continue to develop, but how the hurdles will be resolved, and which stakeholders will benefit from its radical transformative effects.Growing urban populations and the reduction of arable land, increase the need for productive, efficient, and environmentally friendly ways of agricultural production.For more than three decades, agriculture changed towards an increasing degree of automation.Today numerous digital solutions already exist to support farmers’ everyday lives.Examples can be found within the monitoring of crops and soils as well as for data analysis and storage including decision support.Most solutions today need a connection to farm external cloud systems where data and information are being received from and transferred to.Farmers are motivated to actively use this information technology to benefit from increases in farm input efficiencies, from decreases in negative environmental impacts as well as from automated operation documentation.However, farmers in Europe are diverse in their farm produces and many digital solutions only cover partly the activities within farms.This leads to the problem that farmers experience the lack of interoperability of different digital products.

Race and ethnicity issues are rarely mentioned in sustainability discourse

Answering these types of questions will help us clarify the root causes of sustainability problems in agriculture.The general vision of scientists and activists for sustainable agriculture is one which reduces environmental degradation, preserves or restores the family farm, and removes contaminants from human consumption. For example, the goals of the California- based Committee for Sustainable Agriculture are: “To achieve a safe food supply and a cleaner environment . . . [so that] . . . family farms and rural communities may thrive, toxic byproducts be eliminated, and agricultural employees and consumers may be reassured about this major sector of their lives.” This vision is usually considered achievable within our current socioeconomic systems. For example, in the National Research Council’s report on alternative agriculture, “alternative” refers to biological and technological alternatives, but does not address alter- native social or economic arrangements.The authors state that, “Successful alternative farmers do what all good managers do – they apply management skills and information to reduce costs, improve efficiency, and maintain production levels.” For organic food producers and distributors the vision is larger market shares and profits necessary in order to participate in the agricultural industry under current economic conditions. Thus we find that the visions currently prominent in sustainability discourse are primarily concerned with techniques to achieve resource conservation, food safety, and profitability rather than including broader social visions. One sustainable agriculture leader stated, “The fundamental social responsibility of organic agriculture is improving the health of the soil. . . .” Those focused on the global context, however, present a broader vision of agricultural sustainability.

One version of a universal definition for sustainable agriculture is “an agriculture that can evolve indefinitely toward greater human utility, greater efficiency of resource use, and a balance with the environment that is favorable both to humans and to most other species.” The FAO of the United Nations states that “sustainable agriculture should involve the successful management of resources for agriculture to satisfy changing human needs while maintaining or enhancing the quality of the environment and conserving natural resources.” Clearly,rolling bench in envisioning a sustainable agriculture it makes all of the difference whether the goal is to sustain the current world economic order, an individual nation’s agricultural economy, a middle-class American’s life, a farm family’s right to retain owner- ship of their land and other means of production, or an Ethiopian woman’s life. Unless we clearly specify who or what we want to sustain, sustainability advocates risk prescribing future visions that do not consider social inequities and therefore reproduce domination based on class, gender, and race. But how do workers, women, and people of color fit into dominant sustainable agriculture visions?Dominant sustainability discourses generally do not analyze the different interests and classes that participate in the food and agriculture system. An example can be found in the first challenge set forth in the Asilomar Declaration for Sustainable Agriculture*, which is to “promote and sustain healthy rural communities.” Justification for the promotion of rural communities is: “Healthy rural communities are attractive and equitable for farmers, farm workers, and their families. The continuation of traditional values and farming wisdom depends on a stable, multi-generational population.” Thus, although the Asilomar Declaration recognizes corporate land ownership as problematic, it does not address the different interests of farmers and farm workers in general.

It recognizes no inherent problem with an economy based upon land owners who hire landless laborers, and advocates maintaining the existing structure of land tenure. This statement also implies that current rural values, which include the patriarchal family and Christian religious beliefs, are ideals we should advocate and preserve. Similar perspectives are reflected by the National Research Council and the U.S. federal sustainable agriculture research program, Sustainable Agriculture Research and Education . For example, where the National Research Council discusses labor on alternative farms, labor is viewed only as a cost of production. There is no discussion of who the workers are, their working conditions, or their wages. In the same vein, SARE addresses socioeconomic issues primarily in terms of the economic viability of farms, and largely avoids discussion of antagonisms between corporate agriculture, family farms, and farm labor. Those focused on food safety, however, show greater interest in the welfare of the farm worker when they point out that pesticide use in agriculture poses a greater risk to field workers than it does to consumers. Still, where food issues are discussed in the context of sustainability, they usually focus on safety and pay little attention to accessibility. Yet Bill Liebhardt, director of the University of California’s Sustainable Agriculture Research and Education Program, points out that we must “. . . eliminate the very idea of hunger in a state where agriculture is still the largest industry.”This is true in the larger scope as well: worldwide at least 500 million people do not have regular access to sufficient food.Kate Clancy, a professor long active in sustainable agriculture, asks, “Is agriculture sustainable if it doesn’t encompass issues of social justice like hunger?” Yet visions that include factors central to workers and the poor, such as who should have a right to eat or access to land, are not presented in dominant sustainability discourse.

In most cases, traditional gender roles are assumed in discussions of sustainable agriculture, whether women are included or simply overlooked. Populist visions of sustainable agriculture see the family farm as the ideal organizational structure for sustainable agriculture, but generally do not discuss gender roles within the farm family. An exception is Berry, who explicitly discusses differences between men and women on farms and suggests that both women and men suffer when nurturing is the sole purview of women.However, he advocates a return to traditional values associated with the home without questioning the patriarchal privilege that underlies many of these values. The fact that family farms are based on patriarchal relations is not regarded as a problem by the sustain- able agriculture movement, yet on the majority of family farms men control land, capital, and women’s labor.While farm women are resisting their roles as “farm wives” and insisting on wider decision-making roles and access to land, in most sustainability discourse, women’s demands for change have not been incorporated. Often farmers continue to be referred to using masculine pronouns, which fails to acknowledge women’s roles in agricultural production, except as they support the male farmer. In the food safety movement women are often targeted specifically for the part they can play in developing this aspect of agricultural sustainability. In this way traditional gender roles are not questioned, since women are appealed to in their capacity as food purchasers and child care providers and men are excluded. In general, dominant visions for agricultural sustainability do not correct the problem of gender inequities.To some extent, this results from the absence of people of color from decision- making positions in Western agriculture. People of color have been integral to the functioning of American agriculture, but in subordinate roles. African- Americans, Hispanics, and Asian-Americans have historically and currently provide much of the labor in U.S. agriculture, but are much less likely than European-Americans to be farm owners. Even in California,roll bench an extremely ethnically diverse state, only 9.2 percent of farm operators are ethnic minorities; this proportion is inverted among farm workers, 75 percent of whom are ethnic minorities.Farm workers have received few of the benefits of profitable and abundant agriculture; compared to farm owners, they have much lower incomes, live under worse conditions, have less control over the production process, are more often exposed to pesticides, and have higher incidences of health problems related to pesticide use.

It is significant that the impetus for low-input agriculture was generated in part by the level of public distress about farmers losing their land during the 1980s, when the crisis affected mostly European- American farmers and affluent customers. In contrast, little concern has been raised in sustainability discourse about the nearly complete separation of African-American farmers from their land. In 1920 one in seven U.S. farms was black-operated, but in this century the number of farms owned by blacks has declined 94 percent.In addition, the call for a return to traditional rural values fails to challenge racist attitudes historically prevalent in much of the rural U.S. The dominant vision of sustainability in the U.S. does not address racial inequalities prevalent in agriculture.Strategies suggested for achieving sustainability are, of course, intimately linked with the problems perceived, causes attributed, and visions projected. For the Committee for Sustainable Agriculture, sustainable agriculture can be achieved “ . . . through dissemination of information about farming, food processing and marketing techniques that conserve and replenish soil resources, and decrease the use of toxic and synthetic chemicals. By working toward these ecologically benign technologies [sustainability will result].” This statement describes the major strategies employed in the effort toward sustainable agriculture – providing more information to farmers and consumers through better communication, gathering more knowledge about agroecological processes, and developing better technology. Less often, but occasionally mentioned are establishing policy reforms to increase pesticide regulations or limit corporate farming, developing bio-regional communities to localize food production and consumption, and reinvigorating traditional values. While some of these we consider antithetical to sustainability or unrealistic , others are no doubt essential components in the move to sustainable agriculture. We argue not with their inclusion in a package of strategies for sustainability, but with the emphasis placed upon them to the exclusion of other strategies. For example, a primary emphasis has been placed on developing profitable alternative production techniques and systems through science. This is seen both in Western agriculture and in development programs for impoverished countries. Historically in the U.S., agricultural science has been called upon to resolve major socioeconomic and ecological crises in agriculture, such as with the scientifically based land- grant colleges, the Cooperative Extension Service and the Soil Conservation Service. In these instances, science has sanctioned the highly capitalized, chemical-intensive agricultural system in the U.S. and is being uncritically called upon to sanction low-input systems as well.

It is clear, however, that neither science nor new technologies can by themselves solve larger food and agriculture problems, as witnessed by the problems associated with the scientifically based Green Revolution. In addition to the universal sustainability issue of how agricultural products are produced, one scientist proposes that we also address the questions of what and for whom agricultural products are produced.Yet dominant sustainability discourses tend to rely on technology as the solution – that if the right technologies were developed, sustainability would result. For example, the Asilomar Declaration for Sustainable Agriculture states that, “Given scientifically validated techniques, farmers will adopt sustain- able agriculture practices.” In this perspective, an agricultural production system that is both profitable and environmentally sound will be achieved as less environmentally damaging technologies are developed and substituted for existing chemical technologies. This does not examine the overarching structural forces that have contributed to the adoption of re- source-intensive farming practices. Technologies and social relations are inseparably linked, both in terms of their inspiration and their consequences. In agricultural research universities we do not have the Baconian model of the atomistic scientist pursuing “pure knowledge.” Instead, research is often driven by economics and politics; entrepreneurs demand marketable technologies and these are in turn produced. The development of chemical vs. cultural pest management techniques, for example, is not accidental. If agricultural requirements can be responded to profitably, they will be.But maximizing profits depends upon repeated sales of inputs, not products that can be reproduced by the farmers or are self-reproducing under proper environmental conditions.In our view, this vision’s perspective is too partial and fragmented. If we do not go farther in challenging the structures and assumptions that have led to sustainability problems, we place ourselves at the risk of reproducing these problems and generating only very marginal improvements. Achieving a truly sustainable food and agricultural system requires a broader vision and new strategies for both analysis and implementation. Developing this system will require concentrated thinking, innovative actions, and a deep commitment on the part of many people. As a start, we suggest several ways in which we can begin down the path toward a sustainable agricultural system.

The values presented in this study represent values in irrigation and food production

From this perspective, economists often propose that farmers should pay for the real value of the water they use for irrigation. Because of the presence of subsidies, the real value of water in agriculture is different from the price paid by farmers.It is also different from the cost of delivery, which does not account for the value of water itself. However, what is the real value of water? In the absence of a market and associated market prices, the answer to this question is not straightforward.This paper developed an approach to determine the value generated by irrigation water as a factor of production in agriculture; using biophysical models we estimate the increase in crop production afforded by irrigation with respect to baseline rain-fed conditions. These estimates are then used to determine the maximum price that farmers would accept to pay for irrigation water.They may be excessive when they do not account for output subsidies or cost of investments in irrigation technology, the operating costs of irrigation due to additional labor and energy, and other inputs . In some cases, the annualized cost of irrigation infrastructure,square plastic pot and the maintenance, and operation costs, can be so high that they exceed the water values we have determined .

However, these costs are typically subsidized by the government and farmers only pay for part of them . Thus, our approach would still determine the value of water to the farmers and provide a maximum reference value at which farmers might accept to sell or relinquish their water rights or water allocations to other businesses. The cost of irrigation infrastructures, their maintenance and operation are in general difficult to estimate on a global scale because of lack of data. Whether the assumption ΔPC ≈ 0 is justifiable likely depends on a number of factors, including crop type and value, farm size, irrigation technology, and irrigation water source and its distance to the field. Global datasets for a worldwide validation of this assumption are not available. However, in Australia, the Bureau of Statistics reports the total cost of irrigation by region, including the costs of equipment, infrastructure, water license, and operation, in addition to data on irrigated areas and agricultural water withdrawals . A recent analysis of these data has provided annual water provision costs per unit area of irrigated land. We can use these results to estimate the annual cost of water provision per unit volume of irrigation water and compare them to our estimates of the value of water. We find that provision costs typically range between US$0.01 and $0.02/m3 . While in the case of some staple crops such as wheat or maize the value generated by irrigation is comparable , these water provision costs are overall negligible with respect to the average value of water in agriculture in Australia, which is here estimated at ∼$0.27/m3 , in agreement with estimates based on irrigation and production data from the Australian Bureau of Statistics . As noted below, these values fall within the interval of water prices reported by the Murray– Darling water market, suggesting that in the case of Australia the assumption of negligible provision costs can be overall justified. The values of water found by this study fall within the range of those reported for water markets. In fact, we find median global values of $0.13/m3 , while it has been reported that in 2012 Colorado farmers typically pay $0.02 to $0.08/m3 for irrigation water .

However, in the presence of competitors from the oil industry, farmers are outbid in water market transactions at prices ranging from $0.81 to $1.62/m3 in periods of water scarcity . This suggests that, when demand from another industry that is willing to pay a higher price for water comes into play, farmers sell their water rights if the price exceeds the value of water in agriculture. Our study provides an estimate of such a value. For instance, in 2012 in the case of maize, the value of water in the United States is here estimated at $0.25/m3 , in general agreement with water trade data. For instance, in the Permian Basin in Texas, a computer application has been developed that connects owners of water rights to oil companies and allows them perform water transactions. While farmers would normally pay $0.05/m3 , in periods of water scarcity, competition with shale oil and gas companies have brought the price to up to $2.50/m3 . Overall, in the presence of water markets and demand from oil companies, water prices may increase from $0.03–$0.1/m3 to $2.3–$3.1/m3 . Thus, in the presence of competition with other sectors capable of providing a more efficient use of water the market price of water increases and farmers are outbid by oil companies at prices exceeding the agricultural water use efficiency determined by our study. In the Murray–Darling’s water market the median water price reported between 1998 and 2015 varied between $0.05/m3 and $0.50//m3 in periods of scarcity . These values are of the same order of magnitude as the value of water in agriculture we have estimated for the Murray– Darling Basin . Thus, in this study we calculate the value produced by water in its current use. The general pattern that is observed in the analysis of the economic water efficiency of water consumption is that with the current crop distribution the value of water in agriculture does not necessarily correspond to the “best use” of water because it does not exhibit the maximum water value and is at least one order of magnitude less than that in other sectors . Therefore, in the presence of a water market water consumption is expected to shift from irrigation to the activity that maximizes revenue generation.

However, the focus here is on the estimation of water values in agriculture not on how different uses can compete with one another. In the case of water, such a competition is often limited by the lack of a market as a result of institutional or physical factors . While water markets give a direct assessment of the value of water resulting from the complex interactions among different sectors, their existence is limited to those cases in which suitable trade able property rights have been established. For the rest of the world the value of water can still be determined through its ability to produce value in different economic activities. Our results provide a first global estimate of the value of irrigation water worldwide.Fossil fuel combustion and land use change are contributing to alterations in global climate. Atmospheric [CO2] has recently increased at an average rate of 2.0 μmol mol−1 year−1, which is higher than any measurement period to date over the past 800 000 years . Barring substantial reductions in emissions, atmospheric [CO2] may exceed 900 μmol mol−1 by the end of the 21st century 8.5; IPCC 2013). Rising atmospheric [CO2] has contributed to significant atmospheric warming, and global mean surface temperatures could increase 3 to 4 °C by the middle of the century . Embedded in this climatic warming trend is an increased frequency of extreme temperature events . Atmospheric warming has also been implicated in more frequent and extreme precipitation and drought events , and net declines in soil moisture in many, but not all, regions. Temperature, water availability and atmospheric [CO2] are each important regulators of plant growth, function and development. Thus, climate change will likely influence the ability of agricultural systems to meet a growing global population’s demands for food and fibre. It is expected that food production must increase 70 to 100% by 2050 to meet growing demands . Troublingly, recent trends suggest that yields are not increasing rapidly enough , and climate change and extreme weather events may already be reducing crop yields in some areas . For instance, Australia suffered enormous losses in wheat yield during historic drought in the early 21st century and higher than normal temperatures have contributed to reductions in corn and soybean yields in the United States between 1982 and 1998 . Even with high precipitation, higher temperatures can increase evaporative demand and reduce soil moisture resulting in greater incidence of drought . A recent analysis of maize and soybean yields in the Midwestern United States showed that although field-scale yields are increasing, they have become increasingly sensitive to drought . Alternatively, experimental manipulations have demonstrated that elevated [CO2] can stimulate C3 crop yield ,tall pot stand yet the magnitude of this increase is uncertain under field conditions where temperature and precipitation can influence the CO2 fertilization effect .

Climate change also threatens the ability of forests to meet global demands for wood products . Although forest plantations only account for 5% of global forest cover,they supply roughly 35% of global round wood, with future wood production expected to increase in plantations compared to native forests . Concentrating wood production to smaller areas promotes greater forest protection and mitigation on non-plantation lands , but necessitates sustained productivity over time, which will become more difficult under extreme climatic conditions. Experimental studies have shown that forest trees and plantations may increase productivity under eCO2 , although variability in the growth stimulation is dependent upon variation in soil fertility, temperature and precipitation . Atmospheric warming is generally expected to increase tree and forest growth in cool climates, but have no effect or reduce growth in warm climates . In addition, there is mounting evidence that more frequent and intense heatwaves and drought are leading to lower tree growth rates and increased tree mortality in some regions worldwide . Although fast-growing mono-specific plantations have high rates of C accumulation , lower stand-level genetic diversity may increase their susceptibility to heatwaves and drought stress . Utilization of intraspecific variation in agricultural and forest species responses to climate change may bolster productivity and aid development of greater stress tolerance or resilience . Genotypes of a given species often show markedly different physiological, growth and developmental responses to eCO2, temperature variation and soil water availability, exemplified by genotype-by-environment interactions . Careful examination of genotypes’ plastic responses may reveal individuals that can both increase productivity under optimal conditions, and, in part, sustain production under stressful conditions . Despite the potential utility of intraspecific variation in agricultural and forest species responses to climate change, an integrative understanding of the physiological and genetic factors influencing G × E is lacking, and relationships between genotype plasticity and productivity have rarely been tested in the context of agriculture or forestry. The goal of this paper is to link aspects of plant physiology and genetics that may influence intraspecific variation in agricultural and forest species responses to climate change. In particular, we: conceptualize the importance of intraspecific variation in agricultural and forest species phenotypic plasticity within the context of plant breeding and climate change; highlight some physiological mechanisms underpinning intraspecific variation in agricultural and forest species responses to drought, warming and eCO2; discuss the genetic factors influencing intraspecific variation in phenotypic plasticity;and discuss future directions in G × E climate change research.G × E can take different forms including plasticity arising from changes in variance among genotypes across environments, and plasticity resulting from genotype rank changes among environments . Genotypes may also show variable linear or non-linear responses to continuous environmental variation , which may be important for identifying response thresholds to environmental drivers . A genotype’s plasticity is often indexed based on the slope of its ‘reaction norm’ across an environmental gradient , where steeper slopes represent higher plasticity. A long-standing focus in plant breeding has been to measure and utilize information gained from G × E. Typically, the goal in plant breeding is to produce genotypes that are productive across a range of environments or management conditions . Significant G × E, especially in the context of increasing variability of E, hinders selection of stable genotypes . Plant breeders have therefore attempted to limit G × E by prioritizing genotype stability across environments . Alternatively, breeders have also focused on selecting genotypes with improved productivity or stress tolerance in particular environments . From an ecological and physiological perspective, however, and in the context of climate change, careful examination of G × E and its underlying physiological and genetic mechanisms could be important for identifying genotypes suitable for increased climatic variability.

The history of American agricultural extension dates back more than 100 years

If households are homogeneous in the sense that they engage in a similar income generation and face a similar economic problem, the estimated production function provides quantitative insights on the effects of the mortality on income generation and the decomposition of the effects into productivity and each productive asset. Since productivity includes all heterogeneities among households except those in the number of household members, land and livestock, if we included more heterogeneities in input, productivity would become less ambiguous in what it includes. However, we do not think it is our primal objective. The definition of land Kjt is the size of land owned and land rented-in. Land Kjt includes all of the four types of land: owned and used owned and fallowed, owned and rented-out, and not-owned and rented-in. 85% of land is type and the most of the remaining 15% is type . Agricultural income Yjt is net agricultural rent, that is, we subtract paying rent from and add receiving rent to agricultural output/sale. Thus, we control the heterogeneity in land in ownership and renting. Manure from livestock is important for agriculture in Kagera. Smith documents that farmers use manure sparingly and efficiently, they mix ash, mulch and composted manure into the holes in which coffee and banana trees are planted and farmers who optimize their use of manure can produce yield up to five times higher than their neighbors who cannot afford cows . In his data, three-fifths of male farmers use manure and all the farmers interviewed wanted to buy a cow to increase their herd in order to improve farm productivity .

The importance of manure is due to the fact that most of Kagera farmer do not use fertilizer. For example, in original KHDS data, only 5.3% and 3.2% households use fertilizer in wave 1 and wave 5 ,fodder system respectively. Complementarity between crop production and livestock is mainly due to manure since households do not use cattle for plowing. Complementarity between land and livestock is weak since a household uses communal land for grazing instead of its own household’s private land. Complementarity between land and the number of household members is also weak since households use a cattle owner association called omukondo which has twenty or so member households, pasture area, and a herd manager and each household does not have to use its own household member for herding. In our constructed data with total 401 households, there are 160, 119, and 138 households who have zero monetary value of livestock in 1991, 1992 and 2003, respectively. In order to accommodate these household into our analysis, we define livestock Sjt is the real monetary value of livestock plus one.In this subsection, we will show the descriptive statistics in each productive asset and agricultural income in 1991 for households with and without prime-age adult mortality in order to check how these two groups of households are different in 1991 and whether the data support us in taking prime-age adult mortality between 1990 and 2003 as an exogenous shock. Then, we will show the difference-in-difference estimates of the change in each variables from 1991 to 2003. Table 4 show the mean of each variable in 1991 for households with and without prime-age adult mortality between 1990 and 2003.

We divide households simply into households with mortality and those without mortality. The table shows that there is not clear difference in productive assets and agricultural income in 1991 between households with and without prime age adult mortality. We test the null hypothesis that the mean of each variable for households without death is the same as one for households with death and we cannot reject any of that hypotheses even with 10% significance level. These results support us in taking prime-age adult mortality as an exogenous shock.Apparently, households without mortality accumulate total assets and increase total agricultural income more than households with mortality. We test the null hypothesis that average change in each variable for household without death is the same as one for households with death against the alternative hypothesis that the former is larger than the latter. The test for each variable rejects the null hypothesis at 5% significance level . However, there are not clear differences in change of per capita land, livestock, and agricultural income between households with and without mortality. We cannot reject the null hypothesis that change in per capita land and agricultural income and rent for households without death is the same as one for households with death. In order to check whether our observation in Table 5 is robust, we make figures of distribution of change in each variable for households with and without mortality and the figures confirm our observation. These results shows the possibility that households hit by mortality endogenously respond to the negative shock and adjust productive asset level in order to improve efficiency. Thus, it is interesting to ask whether there is the difference in productivity growth between households with and without mortality.

Note that we can reject the null hypothesis that change in per capita livestock for households without death is the same as one for households with death in favor of the alternative hypothesis that the former is larger than the latter. These results imply that households hit by adult death kept per capita land but per capita livestock to improve or keep per capita agricultural income.As we have already seen in Table 5, the table also shows that average agricultural income growth from 1991 to 2003 is negative for both types of households and households hit by prime age households mortality experienced more severe decrease in agricultural income than house-holds without the mortality. The table shows how much productivity growth and accumulation of each productive asset contribute to this negative agricultural income growth. The decomposition of average agricultural income growth for the households without prime age adult mortality shows that the decrease is mostly due to the decrease of productivity rather than the decrease in productive assets. The percentage of contribution of the decrease in productivity is 93%. The decomposition of income growth for the households hit by prime-age adult mortality shows that the households with mortality increase less every component of the decomposition than the households without the mortality. The percentage of the contribution of the decrease in productivity for the households with mortality is 80%, which is smaller than one for the households without mortality . These results imply that on average, households without mortality could kept their productive assets but the households hit by mortality could not. The third row shows that a half of the difference in agricultural income growth between households with and without prime-age adult mortality is due to the difference in productivity growth. The percentage of how much the difference in productivity growth explains the difference in agricultural income growth is 60% . The third row also shows that how much the difference in the accumulation of each productive asset consists of the difference in agricultural income growth between households with and without prime-age adult mortality.

The difference in the accumulation of household members consists the most and those of land and livestock follows. The difference in the accumulation of household members explain more than a half of the difference in accumulation of all three productive assets; the number is 60% . This is reasonable since prime-age adult mortality decreases the accumulation of household members directly and may decrease accumulation of land and livestock indirectly. We could interpret that the difference in the accumulation of household members is direct negative effects of prime-age adult mortality on agricultural production. Note that although we call it as direct negative effects, we do not mean it excludes households’ endogenous response to prime-age adult mortality. “Direct” means just that adult death directly decreases the number of household members. On the other hand, differences in productivity growth and the accumulation of land and livestock are indirect negative effects. Surprisingly, the results show that direct effects do not count for the largest part in the difference in agricultural income growth between households with and without prime-age adult mortality. Instead, fodder system for sale the difference in productivity growth plays the largest role to explain the difference in agricultural income growth. The percentage for the difference in productivity growth is 60% as we mentioned above while the percentage for the difference in accumulation of household members is 24% . These results imply that households hit by prime-age adult mortality could not cope with it and not accumulate not only household members but also land and livestock as much as households without death could. Furthermore, households with the mortality could not increase productivity as much as the other households could. Surprisingly, the negative effects on productivity growth are larger than negative effects on productive asset accumulation. The fourth row shows whether each variable for households without death is statistically significantly larger than one for households with death. Productivity growth for households without death is statistically significantly larger than one for households with death. The increase in income generating power due to accumulation of all productive assets as a whole and household members only for households without death is statistically significantly larger than one for households with death.

We can say that households hit by prime-age adult mortality could not increase income generating power in every factor among productivity growth and the accumulation of each productive asset as much as households without mortality could. A surprising result is that the difference in the accumulation of household members between households with and without mortality is not the largest factor in explaining the difference in agricultural income growth. This result implies the following two things: First, households hit by mortality could not increase or keep productivity and productive assets, land and livestock as much as households without mortality could. Thus, mortality destroys not only household human capital but also land, livestock and productivity indirectly. Second, a household hit by mortality responds to and mitigates the decrease in household members due to mortality somehow. We may think that the household tries to increase its household members or at least try to keep them by accommodating a new member through marriage or keeping current members who would move out of the household if there was no mortality. A households hit by mortality adjusts its amount of each productive asset after mortality in order to improve productivity. However, the results show that the magnitude of negative effects of prime-age adult mortality is so large that we can observe the differences in productivity growth and accumulation of each productive asset between households with and without mortality even in a long term of 13 years.The Morrill Act of 1862 established land-grant universities across the country with the purpose of educating the citizens about agriculture, home economics, and other practical professions.1 According to the Act, each state had to set aside acreage of federal land, the income from which would have to support a college or university for teaching ‘mechanical arts’ . Twenty-five years later, in 1887, the Hatch Act was passed, which established the allocation of federal funds to state agricultural experiment stations. The Smith-Lever Act of 1914 formalized the cooperative extension through the creation of a partnership between the land-grant research universities and the U.S Department of Agriculture. The Congress clearly stated the purpose of Extension: ‘to aid in diffusing among the people of the U.S. useful and practical information on subjects related to agriculture and home economics, and to encourage the application of the same’ . Funding for the Cooperative Extension would come from the Congress to the United States Department of Agriculture, which would then distribute it among the land-grant universities, matching the amount to the state- and county-level expenditures.2 The formula designed for allocation of funding for Cooperative Extensions mandated that the federal and state contribution would each amount to 40 percent, with county contributions amounting to 20 percent of the total . In this paper, we do not distinguish between the 1914 Act and the Hatch Act, as both provide funding for research and dissemination activities within Cooperative Extension.

Several studies were conducted on watermelon farming at home and abroad

The results from our IV estimations indicate that both shock types significantly and positively impact pesticide use.Notably, farmers who experience shocks are more likely to use up to 30% more pesticides than non-shock households.Furthermore, pests and diseases also have a significant and positive impact on fertilizer use with the same magnitude.In other words, these types of shocks are forcing farmers to use more these inputs.Therefore, stronger support from public services such as more efficient weather forecasts and local extensions in crop production are important to reduce the uncertainties in rural regions.In addition, providing a mechanism of crop production insurance to prevent adverse impacts of shocks might discourage farmers from overusing chemical inputs.The IV fixed-effects estimations also show that households belonging to the Thai majority appear to use more inputs than minorities.This finding is in line with a case study in Vietnam that differences in ethnic groups are more likely to affect the application of production inputs due to their different farming practices and levels of wealth.Further, the results show that farmers having more agricultural equipment and transportation vehicles such as sprayers, motorcycles, and trucks tend to use more pesticides, which may be due to affordability to purchase or the ability to transport the inputs.

For fertilizers, farmers with a higher education level, a longer distance to their land plots, dutch buckets more motorcycles appear to use more fertilizers, while those with a higher dependency ratio and larger farmland tend to use less fertilizers.The variable of asset poor shows an insignificant effect on input use.To acquire the farming efficiency, we estimate the translog true random-effects stochastic production frontier function with Mundlak’s adjustments.Table 4 stacks the brief results of the estimation.Most of the mean variables of CRE show a statistical significance implying the presence of time-invariant unobservable characteristic effects.Only five variables of inputs show a significant effect.This indicates the less intensive level of rice production in Thailand, compared with some competing countries such as Vietnam.The results also indicate that fertilizer is the most important input.Fig.2 shows the distribution of predicted farming efficiency scores.The mean score was 0.64 in 2013 and 0.70 in 2017, the vast majority of the households have a farming efficiency score higher than 0.50, and less than 3% of the households have an efficiency score higher than 0.90.The mean efficiency score of our estimation is slightly higher than the score of rice farmers in Thailand, lower than Vietnam and of 0.85 from Huy and Nguyen, and higher than the scores of rice farmers in Cambodia  and in Bangladesh.In our result, the mean score of shock households appears to be lower than that of households in the non-shock group in 2013 and 2017.Table 5 presents the effects of farmers’ risk attitudes and shocks on technical efficiency in rice production and shows that farmers’ willingness to take risks has a positive and significant effect on farming efficiency.This finding further suggests that higher risk-averse farmers are applying more fertilizers and pesticides, and this inefficient use of inputs causes farming inefficiency in their production.In addition, the result from IV fixed-effects estimations by groups of farmers’ risk attitudes shows that households belonging to the risk-averse group appear to have lower farming efficiency.This confirms that more risk-averse farmers are inefficiently using chemical inputs, and this improper application leads to lower farming efficiency.Our findings support the conclusion that rural households’ behavior under risk might explain low agricultural productivity and vicious cycles of poverty in developing countries because these inputs account for a high proportion of production costs.

We run additional estimations with lagged values of attitudes as robustness checks.The results remain consistent.Unsurprisingly, weather shocks significantly and negatively affect rice technical efficiency, while pests and diseases show an insignificant influence in all IV fixed-effects estimations.Regarding the weather shocks, the result is related to the findings of Mishra et al.and Mishra et al.that weather shocks are a major reason affecting agricultural inefficiency in Cambodia and Bangladesh, respectively.This emphasizes the impacts of weather shocks on agricultural production in developing countries and urges governments to support rural households to cope with weather shocks, especially in the context of climate change that causes more frequent extreme weather events.The extensive and improper use of chemical inputs in agriculture has triggered various non-point source pollution and accelerated carbon emissions.This has been deteriorating the ecological environment and endangering public physical and mental health.Abundant use of inorganic fertilizers during farming is linked with the accumulation of contaminants in agricultural soils, including arsenic, cadmium, fluorine, lead, and mercury.Pesticides, fungicides and weedicides are frequently found in the stream water in agricultural areas.These chemicals are also detected in the air of residential environments.Agrochemicals were traced even in human blood and adipose tissue.Various short-term or long-term health casualties are associated with agrochemicals, including dizziness, nausea, diarrhoea, skin, eye irritation, cancer, endocrine disruption, birth defects,etc..Inorganic inputs reduction and replacement with organic inputs with soil protection measures, crop rotation, intercropping, and waste resource utilization are the effective ways to solve the problem.However, farmers use these agrochemicals primarily due to economic benefits.The application of fertilizers and crop protection chemicals has been instrumental in increasing agricultural production, while pesticides, fungicides, and weedicides also reduce the cost of production from diseases, insect pests, and weeds.Organic agriculture practice involving the application of a set of cultural, biological, and mechanical practices is the best alternative that supports the cycling of on-farm resources, promotes ecological balance, and conserves biodiversity.Maintaining or enhancing soil and water quality; conserving wetlands, woodlands, and wildlife with avoiding the use of synthetic fertilizers, sewage sludge, irradiation, and genetic engineering are included in these practices.

Due to the lower cropping intensity under organic farming and the poor socioeconomic status of the farmers, the use of organic inputs is still very negligible in Bangladesh.Approximately only 0.1 % of the country’s total cultivable agricultural land is under organic agriculture.This has left an enormous vacuity to promote the use of organic inputs in agriculture.In addition to cereal and other cash crops, the use of organic inputs must be promoted in various fruit farming.Watermelon is one of the important fruits cultivated in Bangladesh besides mango, jackfruit, papaya, banana, etc.In 2020, around 12,251 ha of land was devoted to watermelon farming, where 254814 MT production was obtained.Because of high profitability and environmental suitability, it is grown extensively in the southern districts of Bangladesh.However, watermelon farmers extensively use different agrochemicals, i.e., fertilizers, pesticides, fungicides, weedicides and even some growth promoters.Since watermelon is a very demanded fruit in Bangladesh, assessing the farmers’ agrochemicals use is necessary.Besides, they are reluctant to use organic inputs for expected lower profit.Their willingness to adopt organic inputs need to be evaluated for ensuring sustainable agriculture in Bangladesh.Rabbany et al.conducted research on the cost of production analysis of watermelon.Yusuf et al.also reported profitability and adoption of watermelon technologies by farmers.Ibrahim et al.explored technical efficiency and its determinants in watermelon production.However, very little is known about agrochemicals usage by watermelon farmers.On the other hand, various studies were conducted on the use of organic inputs in various crops farming.Tur-Cardona et al.explored the acceptance of bio-based fertilizers in European countries.Salam et al.evaluated the impact of organic fertilizer on the yield and efficiency of rice.Rana et al.examined the organic vegetable cultivation attitude of the farmers.Again, very limited information was found that assessed the watermelon farmers’ willingness to adopt organic inputs.Hence, the present study was conducted to contribute to the literature by evaluating the watermelon farmers’ agrochemicals usage and assessing their willingness to adopt organic inputs.The objectives of the study are twofold.First, it assessed the agrochemical usage by the watermelon farmers.Second, it evaluated the farmers’ willingness to adopt organic inputs.The structure of this paper is as follows.The next section provides information on data and methods used to achieve the objectives.Section three presents the results and discusses the obtained findings.The last section provides conclusions with possible recommendations based on the findings.

Farmers in this study use various agrochemicals in the form of fertilizers, pesticides, fungicides, weedicides, growth promoters, etc.Fig.2 illustrates the farmers’ extent of using chemical fertilizers in watermelon farming.Maintaining soil fertility and soil nutrients is crucial for sustainable plant growth,grow bucket and it is usually done through applying fertilizers.Farmers in this study mainly used Urea, TSP, MoP, DAP, Gypsum, Boron, and Zinc fertilizers for watermelon.Urea provides necessary nitrogen to plants that help vegetative growth and aids the photosynthesis process.In watermelon farming, the farmers use four split applications of Urea during tillage, ten days before planting, 10-15 days after planting, and during and at the time of first flowering.Results revealed that about 72.5 % of farmers applied Urea above recommended dose while only about 5 % followed recommended dose.Similarly, most of the farmers used the above recommended doses of TSP.TSP is a popular source of phosphorus, and it helps with the growth and the development of the root system.Two doses of TSP are applied in watermelon farming, i.e., during tillage and ten days before planting.Urea and TSP are two of the three most used fertilizers by the farmers in Bangladesh.This could happen due to the negative effect of notable price reduction of fertilizers by following a universal subsidy policy in the country over the decade.Islam and Hossain also reported farmers tendency to overuse these fertilizers.MoP provides potassium to the plants, assisting the growth of strong stems and helping build the plants’ disease resistance.In the case of MoP, watermelon farmers apply it in four splits with Urea.It was found that most of the farmers were below recommended doses of MoP, while 38.3 % used above recommended doses.On the other hand, Diammonium Phosphate usage was under dose for most of them , while 35.8 % used its overdose.DAP is highly soluble and provides temporary alkalization of pH of the soil solution around the fertilizer granule, which aids better phosphorus uptake.Although about 31.7 % of them applied the recommended doses of Gypsum, the majority were overdosing.Contrarily, more than half of them did not apply Boron and Zinc, while most of the farmers using them were following the recommended dose.Farmers apply plant protection chemicals, i.e., pesticides, fungicides, weedicides, etc.in order to prevent diseases, infestation, and weeds in expectation of increased production.The usage extent of these chemicals by watermelon farmers in this study is elicited in Fig.3.It was observed that most of them used overdoses of pesticide following 35.8 % using below recommended dose.In the case of fungicide, about 70.8 % of farmers applied above recommended doses.Although most farmers did not use weedicide as they manage weed manually, about 12.5 and 23.3 % applied recommended and below recommended doses, respectively.Besides, farmers used growth promoters for sweeter and bigger-sized fruits.It was found that the majority had been using it in overdoses.Overall, farmers are overusing chemical inputs, which can be devastating for the public health, environment and especially their own health.Farmers in Bangladesh usually seek advice on pesticide use from dealers or retailers in their local market, who mostly have superficial knowledge on different inputs because of easier accessibility.Another plausible reason could be the increase in the availability of several brands of chemical inputs in the market, which was also reported in the study by Rahman.The easily availed different chemical inputs at local markets with misleading advertising might confuse the farmers who are mostly illiterate.The factors influencing the adoption of agrochemicals by the watermelon farmers were identified using a Tobit regression model.The results indicate that the education of the farmers exerted a significant and positive influence over the adoption of agrochemicals.It implies that farmers who are more educated use the chemicals better than their counterparts.Farmers with higher years of schooling have better access to information and analytical capabilities, enabling them to use the chemicals more sensibly.The finding confirms the results of Yigezu et al.and Prodhan and Khan.Farming experiences of the watermelon farmers influenced their use of chemicals significantly and positively.Experienced farmers tend to have better knowledge about the crops’ required doses that prevent the overdoseof chemicals.The same echoes were found in the studies of Nnadi and Akwiwu and Rahman and Haque.However, farm size held by the farmers illustrates a significant but negative effect on their use of agrochemicals indicating that small farms used these chemicals better than large ones.

Smart farming also has the potential to reduce the risk of crop loss and failure due to climate change

Managing water quality in river and ground water ecosystems is another shared challenge for sustainable agriculture in both the U.S.and South Korea.Water quality is intrinsically tied to water storage levels, stream flow and climate change.When estimating future life-cycle eutrophication, Lee et al.found that eutrophication in the Midwest U.S.stays relatively steady when using the Representative Concentration Pathways , developed by the Intergovernmental Panel on Climate Change , except in the scenario where GHG emissions are high.High levels of GHG emissions from corn production in the Midwest coupled with ambient temperatures and precipitation suggest a sharp increase in eutrophication in the region by 2022 for a four-year period and then again in 2057.In South Korea, recent economic activity and the influx of pollutants have increased, therefore, as preventive methods, standard fertility prescription, non-point pollutant control, organic farming with low energy, and livestock manure cycling have been implemented.The Rural Community Corporation, which supplies the right amount of high-quality water required for farming in a timely manner by managing agricultural reservoirs, pumping stations, and water canals, has been monitoring water quality in real-time through automatic water quality measurement devices, hydroponic nft channel predicting water quality changes through big data and artificial intelligence analysis and conducting preventive water quality management.In 2020, the corona virus significantly disrupted the supply and demand cycle for agricultural products and disrupted agricultural distribution systems in both countries.

The decline in food demand by restaurants and hotels coupled with reduced demand for bio-fuels as travel decreased had an immediate and severe impact on U.S.farmers and resulted in lower crop and livestock yields and a disturbance in the food supply chain.In South Korea, sales for in-person walk-in food markets dropped by 19.6%, and online sales increased by 46 %.In both countries, food and horticulture exports were down due to global cancellation of events.Food service providers, food catering companies and farmers were severely impacted from school and restaurant closures.The overall projection by the Organisation for Economic Co-operation and Development is that the impact of the COVID-19 pandemic will have ongoing effects throughout the next decade caused by a decline in consumer demand, and disruption in agro-food trading and the downstream food processing industry.The demographics of farmers in both countries indicate an aging workforce and a shrinking rural population.The average age of U.S.producers in 2017 was 59.4 years , with only 9.4% of producers being 35 years old or less.Prior to the COVID-19 pandemic, unemployment in non-metro areas had begun to decline and there was a slight increase in rural populations.The upturn was due in part to better labor market conditions and recovering real estate markets in rural areas.Nonetheless, more than 82% of the nation’s population continues to be concentrated in big cities.South Korea has a similar situation with most producers being 65 years or older.South Korea is also experiencing a population decline in rural areas.The rural population in 2018 was 18.54 % of the total population , which represents a decrease of 84.4% as compared with its rural population in 1970.The aging and decrease in population are due in part to urbanization and most younger citizens leaving for cities where the living standards are higher ,and agricultural mechanization.

According to Yoon et al., in addition to the problems of an aging farmer population and reduction in farmland, the free trade agreements with the European Union, China, and the U.S.have weakened the competitiveness of domestic agriculture.A summary of challenges to agricultural production are listed in Table 2.The U.S.and South Korea are known for their innovative technologies , which carry over to the agricultural sector.Historically, both the U.S.and South Korea were dominated by an agrarian culture, but now both have mixed economies.American agriculture began to experience a significant change in the early 1900’s transforming from a labor-intensive sector to highly efficient mechanized operations.South Korea quickly transformed to a leading economy in a single generation , in part due to comprehensive five-year economic plans developed by the government and investment in social overhead capital in the technology sector.The high degree of innovation and embracement of advanced technologies, serves both countries well in their quest towards smart solutions.Currently, both the U.S.and South Korea are working towards the development of smart farming systems or elements of smart farming to adapt to and mitigate the challenges posed by limited resources, climate change and environmental impacts.The U.S.passes legislation every five years, commonly known as the “Farm Bill”, to address national agricultural and food policy.The current farm law applies through 2023.Policies are carried out through a variety of programs including nutrition, crop insurance, commodity support and land conservation.While the farm bill authorizes and pays for mandatory expenditures and establishes limitations for discretionary programs, a national American approach to develop a smart farming system does not exist.Rather, advances in agricultural technologies and information systems that constitute elements in smart farming systems have been or are being developed mainly by the private sector, although public non-profit companies, and university institutions have had a role in agricultural innovations.

In more recent years, corporations that invest in agricultural R&D are prone to mergers and acquisition.Smart farming solutions designed in the U.S.are mostly hardware or software products that can operate independently or in combination to provide farm management processes.Examples are GPS-guided tractors, yield monitors, variable rate sprayers for pest control, planters and variable rate fertilizer implements.All these technologies have been widely adopted in the U.S., mainly because this equipment allows farmers to manage large-size farms more efficiently and optimize more precisely the inputs with no additional human labor.Currently, in the U.S., smart system products developed by private industry are available to farmers on the retail market.Universities and the Agricultural Research Service are also involved in developing smart farming solutions for precision irrigation management in collaboration with private industry or with state cooperative extension specialists.specific smart system solutions include automation and equipment control , optimization of machine operations , or provision of decision support tools for irrigation scheduling, forecasting precipitation, or developing variable rate application maps for fertilizer or irrigation.The market for smart hardware also addresses the need to reduce the time that a grower spends monitoring and making agronomic decisions for large-size fields or for multiple fields.Decision support algorithms are data driven and typically based on any one or a combination of in-situ sensors, image sensors, imagery from UAVs or satellite systems in combination with edge or cloud computing and machine learning algorithms.Information is acquired by farmers using mobile phone apps or web-based computer sites.In many cases irrigation companies are working with software firms and tech companies that offer geo information services to provide a whole package solution.The shortfall of these smart hardware and software solutions are that they often use unique algorithms with proprietary platforms to limit their compatibility between manufacturers.Non-profit groups also play an indirect role in driving the development of concepts and elements of smart farming in the U.S.

Examples include the Council for Agricultural Science and Technology , a nonprofit organization, that provides information to policy makers, the media, private industries and the public.The CAST group developed a position statement on Climate Smart Agriculture that emphasizes the role that agriculture can play in helping address climate change while creating jobs and economic opportunities.In addition, Ag Gateway, a global non-profit organization is helping to frame smart farming on a national scale in the U.S.Its mission is to develop resources and relationships that drive digital connectivity in global agriculture and related industries.In working with the American Society of Agricultural and Biological Engineers , AG Gateway pushed for the development of data exchange standards for transaction and electronic data compatibility.This initiative was meant to standardize language and improve data exchange across multiple hardware and software platforms to enable interoperability among sensors and equipment used in precision irrigation technologies.Use of the standard by manufacturers and industry members is voluntary.A summary of the main smart farm concepts for the U.S.is listed in Table 4.In South Korea, concepts for smart farming solutions are more holistic.The Korean national innovation system was implemented to develop regional economies based on technological innovation.The system emphasizes the role of government in leading collaborative research and development to promote technological capabilities and is perpetrated in the agricultural sector with the dominant purpose being rural economic development.In the arena of smart farming, the Korean government aims to improve productivity and quality by enhancing ICT utilization through education,nft growing system consulting, and follow-up management.The Korean government views smart farming as a system to help guarantee the generational sustainability of agriculture, it is determined to change the national agricultural structure to meet the trends and demands of the times, such as digitization and low-carbon conversion.The Korean government also envisions smart agriculture as a mean to continue to regenerate rural areas as the core idea of the Korean version of the New Deal.Smart farming, which combines ICT and robot science technology such as big data, artificial intelligence, and the Internet of Things , is spreading and disseminating to respond to the devastation to the agricultural environment caused by climate change and solve the agricultural problems.

As part of these efforts, the Ministry of Agriculture, Food and Rural Affairs has been promoting agriculture for the purpose of upgrading agriculture, responding to the aging of farmers and nurturing young farmers.MAFRA has set an expansion target by 2022 and is promoting ICT convergence projects in agriculture , development of Korean smart farm models, and R&D support projects 2019.The goal was to enable 7,000 ha of farms and orchards, and 5,750 barns to operate as smart farms and smart operations, respectively, by 2022.Since 2018, for the spread and advancement of smart farms, the creation of a youth startup ecosystem, establishment of industrial infrastructure, and creation of a smart farm innovation valley are being promoted as major policy tasks.The Rural Development Administration of the Republic of Korea has been concentrating its research capabilities on securing key elements and source based technologies to develop the world’s best Korean-style smart farm model, and to make the entire process of the perch production system smart.The Korean smart farm project is a long-term project to secure independent agricultural production technology that can compete with advanced agricultural countries by developing technology suitable for agricultural environment and field conditions without importing, applying, or simply imitating foreign advanced technology.This Korean smart farm prototype follows a technology model with various levels : 1st generation-improved convenience with remote monitoring and control, the 2nd generation-improved productivity through intelligent precision growth management, and the 3rd generation-export of smart farm integrated system such as energy optimization and robot automation of the technology are developed and put into practical use.The project plans to reduce the use of labor and agricultural materials, link it with farm household income through productivity and quality improvement, and further solve the difficulties in the farming field and related industries at the same time.Currently, because the ICT devices being distributed are not compatible with each other due to the different product specifications of each company, the integrated management and maintenance of smart farms is difficult.Accordingly, ICT equipment standardization and other standardization work are underway to unify the format and communication method into one common standard for various sensors and controllers used in horticulture and livestock.While South Korea emphasizes smart farming communities, the government also embraces discrete smart farming solutions in the form of smart agriculture equipment blended with the idea of digital agriculture which combines ‘precision agriculture’ technology with intelligent network and data management and utilizes big data and artificial intelligence for decision support.Smart farm applications are currently being used in greenhouse production and field production.The main smart farming concepts for Sourth Korea are summarized in Table 4.Smart farming has the potential to reduce labor and increase efficiency of agricultural inputs and time management for producers, this would benefit both countries.Reduced inputs with limited reduction in quantity and quality of yield could translate into profitability.Sector growth is envisioned as long as the ICT system affords data strategies providing intelligent information and services to farmers such as potential buyers for their products and predictions for future demands.The global market for smart agricultural goods was estimated at 6.34 billion USD in 2017; this market is projected to reach 13.50 billion USD by 2023.

Agricultural ES and EDS related to arthropods mainly concerned regulating services

Arthropod functions and processes in SHF agrosystems were mostly related to pests, either their damage or their control.For instance, articles reported arthropod-related damages concerning herbivory on plants and consumption of stored grains , while potential of pest control by natural enemies was studied through parasitism and predation processes.Besides, pollination and hive-related products represented 15.35% and 6.14% of the investigated functions, respectively.These were studied to illustrate changes in knowledge and practices and potential benefits from pollinators.The remaining ES include education, medicinal, cultural or heritage services, and a lower proportion of services related to soil processes , bioindicator species, handcraft manufacturing or direct selling.These ES were considered through educational purposes and to examine the links between farmers’ knowledge and decision-making.Jointly studied functions were mainly “crop pest and pest control” and, to a lesser extent, “pollinator-related services and educational and cultural services”.A low proportion of the reviewed literature assessed more than three functions together, often associated to cultural services or crossroads between cultural and regulating services.Three main categories accounted for the most studies on crop damage, pest control and pollination.

The most studied taxa belonged to the hymenoptera , either as natural enemies , pollinators or pests.Furthermore, many arthropod taxa were studied in intercropping systems,ebb flow trays stressing the key part of landscape heterogeneity in smallholder farming.Only 4.9% of all articles assessed the management of both pollinators and natural enemies and/or pests.Except for integrated pest management , options that represented combined forms of different arthropod management were rarely evaluated in the same study.Regarding management practices, farmers’ strategies to improve an ES or counter an EDS mostly concerned chemical, organic and cultural practices for pest and habitat management.Most of them were related to pest control and implied pesticide applications.Nevertheless, several management strategies sought to improve environmental quality of agroecosystems.Arthropod management included traditional practices mostly by habitat management.Other common strategies were related to storage facilities and pest control.Overall, 40.2% of articles addressed farmers’ perceptions, actions and/or knowledge related to arthropods in SHF.Research covering farmers’ knowledge or perceptions was mainly carried on cultural services , especially for pollinator-related services.In contrast, farmers’ knowledge or perception was not prevalent in common agricultural services like pollination or pest-related functions.Very few studies addressed farmers’ point of view on processes related to soil and to species as bioindicators.Overall, farmers’ opinion and knowledge was poorly considered as 73.8% of all articles had a participation index scoring 0 or 1.In particular, farmers were poorly involved in the identification or survey processes: 78.2% of the 1264 taxa registered in the 122 articles were studied without local stakeholders.Farmers’ involvement was mainly passive through surveys or on-field sampling, and mostly aimed at collecting agronomic data, without consideration of their viewpoint in research questions or methods definitions.

Furthermore, 17.2% of articles did not report any or not explicitly mention farmer’s involvement within the research process.In this review, we evaluated current literature on arthropod-related services and disservices in smallholder systems.Despite an increasing number of studies focusing on insect-related services in the last decades , we found that only 9.0% of the arthropod literature concerned agricultural systems.Even more challenging, only 0.34% of the search outputs referred to SHF, albeit 84% of the world’s farms are small-holding, operating on about 12% of the world’s land.These results are in line with recent findings pointing that agricultural ES research is strongly biased towards large scale intensive farming landscapes and temperate biomes in HICs.This review is subject to limitations inherent to the chosen scope and focus on recent literature.In addition, it is likely that some SHF studies from L&MIC may not be published in indexed peer-reviewed journals but rather in technical reports or local academic canals, keeping several potentially relevant documents out of our scope.Similarly, despite a multilingual search, we might have omitted several references, particularly from the Asian continent, which is a limitation commonly reported in the literature.Most studied functions concerned pest, which reflects the longstanding negative view of arthropod roles in agroecosystems.In most cases, arthropods were studied only as pests or pest antagonists with no consideration of other ecological roles they could play.However, as smallholders’ actions may be driven mainly by EDS reduction , the negative impacts of these actions on ES supply should also be taken into account.

A few studies assessed floral visitors as potential enhancers of yield but almost none considered both pest control strategies and the maintenance of beneficial insects.Moving in that direction, Integrated Pest Management strategies could be adjusted for pollinator protection practices along with other beneficial arthropods for the agroecosystem.This relatively new paradigm of integrated pest and pollinator management merges both the welfare of all pollinators into the crop pest protection programs and benefits of alternative pollinators into crop production.IPPM can fit smallholder farming sustainable objectives as it intends to minimize trade-offs between ES and EDS, and to maximize co-benefits and synergies from insect management.However, any application of these principles calls for extensive transdisciplinary research among scientists, farmers and stakeholders in order to develop collectively onfield trials and monitoring instruments, but also to co-design decision support tools and evaluation of IPPM adoption.In the reviewed literature, arthropods were mainly studied separately along the food production process.However, agricultural ES and EDS require a wider consideration of the different crop stages, including management of harvested products as well as crop and non-crop habitats.This is especially important for ES and EDS related to arthropods whose life cycles often encompass both cultivated and natural habitats.The lack of a landscape level consideration may hamper farmers’ actions and proper management strategies.Indeed, the majority of reviewed papers presented pest management through chemical pesticide applications in the different crop system components while more sustainable management of traditional SHF requires a multidimensional view of the system.

Farmers aware of the role of the entomofauna at the landscape level could lower pesticide use, even if their awareness is oriented towards phenomena they observe in their fields or storages.Indeed, various articles raised the importance of increasing the entomological literacy of farmers, for example through training programs on pollinators , to achieve sustainable management actions in SHF.Arthropods also support social practices and cultural values by enabling the identification and analysis of changes in intergenerational transmission of knowledge.We found few studies focusing on how farmers’ knowledge is linked to arthropod-related ES.A similar trend was documented by Rawluk & Saunders who pointed at the scarcity of documentation of farmers’ knowledge on beneficial insects’ biology or ecology in agroecosystems.Farmer’s knowledge or perception mainly concerned pest-related functions because of the strong interdependence of smallholder farming on pest threats and risks.This makes control techniques essential to increase productivity while dealing with harsh environmental conditions.The few articles directly engaging emic local knowledge systems on arthropod-related ES dealt primarily with bees’ handling.These practices cover a broad range of cultural, medicinal or educational services that contribute to empowering bio-cultural diversity and endogenous development.These relationships would be worth studying further and together with other services or disservices to assess potential trade offs and synergies in the agricultural system.The objectives of most articles were either to identify and/or study the biology/ecology of arthropod species providing specific ES.However,4×8 flood tray several articles reported farmers being unable to recognize or identify arthropods and/ or their functions correctly , leading to inappropriate arthropod management.Furthermore, local beliefs in spontaneous generation can substitute concepts of insect reproduction and metamorphosis cycles.

These statements illustrate the mismatch between scientific and local knowledge that can be detrimental to cope with agronomic problems.Most farmers have a remarkable experiential knowledge of several elements in their agricultural landscapes resulting from long-term human-agrobiodiversity interactions.However, certain aspects might be difficult or impossible to observe such as the morphological differences between immature stages of two different pest species or the predatory behavior of small parasitic wasps of crop herbivores.This may affect farmers’ understanding of pest damage and bio-control.For example, farmers can easily observe that insect pests may be preyed upon by vertebrates but not by other insects or microorganisms.Likewise, farmers might over-react to certain pests that cause sub-economic damages or may perceive non-pest species as threatening.Misidentification remains the main issue reported in the literature, either for species names or for their ecological functions.On the other hand, even professional entomologists may have a limited knowledge on the taxonomy and ecology of many arthropods living in tropical SHS.It is therefore mandatory to reinforce transdisciplinary research by fostering the complementarity between local and scientific knowledge for arthropod management in SHF.The recognition of local classifications could be an opportunity to build synergies between knowledge systems and generate a common vision of arthropod communities.In the reviewed literature, scientists made the vast majority of taxonomic identifications, asking farmers subsequently to recognize them and then evaluate/validate their knowledge.Very few studies proceeded to recognize local categories and how arthropods were locally classified or named.This perspective widens the gaps between scientific and farmer knowledge, potentially affecting the effective implementation of more sustainable agriculture practices.Among the great diversity of insect species, farmers may name a set of organisms by a single term, even when they are not related species.Ethnoentomological studies have shown that a lack of name designation does not always reflect a missing category, as when a combination of words or concepts encompasses adjacent categories.Folk entomological classifications include cultural, social and ecological dimensions to differentiate life-forms based on morphologic, biological, behavior, utilitarian and psycho-emotional criteria.Thus, involving folk and farmers’ knowledge systems that differ from the taxonomic systems may allow broadening the scope of research in the direction of knowledge co-construction through.This may be achieved through the development of collective referential categories between scientific and folk knowledge systems or through a monitoring of knowledge changes.Including emic knowledge and intrinsic value of entomofauna in SHF may also help to better understand their socio-ecological roles in the agroecosystem, as proposed for pollinators and natural enemies.

While several authors recognized the importance of including farmers in agroecosystem ES and EDS studies, our review shows that questions related to local knowledge remained of limited interest for researchers.Poor participation of farmers and local people is a persistent problem in agricultural ES research and may have long-term implications to link different types of experience around a common problem.Applying transdisciplinary research concepts and methods may address this issue by favoring the initial co-design and co-creation of collaboration frameworks and research questions, the bidirectional information fluxes between scientists and farmers and the building of a solution-oriented knowledge.In our review, only three studies out of 122 actively engaged farmers.These works documented the successful application of participatory approaches.For example by improving pest control networks Landis et al. report on capacity building on IPM practices for wheat, providing a common learning process for farmers, crop advisors, and students.Also Smith et al. proposed a coordinated pollinator management plan integrating both local and scientific knowledge while Christmann et al. investigated human values regarding friendly actions for pollinator protection by a participatory approach focusing on farmers decision making.Such initiatives may not only trigger large system change and achieve broader systemic impact on SHF but also catalyze sustainable agriculture transition process as it combines both knowledge and social processes among actors.Coronavirus disease 2019 is a highly contagious infectious disease threating global public health and has declared as a pandemic crisis around the world.The COVID-19 is caused by the most recently discovered coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 which is under the family of Coronaviridae a large family of enveloped, positive-sense RNA viruses that are important pathogens of humans and other mammals.In 2003 and 2012, two deadly human Coronavirus , namely SARS-CoV and MERS-CoV, have emerged respectively.Recently, the SARS-CoV-2 is a third new type of CoV, which is even more pathogenic, is straightening across the world in an unparalleled manner.In Bangladesh, the first-ever confirmed case was reported on March 8, 2020.In these contrast, several strategies have been executing to control the COVID-19, some of them concerning to the social distancing, hand washing, lockdown measures and etc..To combat against the COVID-19, it is essential to boost up the body immunity and animal originated protein and fiber enriched foods play a crucial role for this perseverance.In Bangladesh, about 37% of all animal protein meat consumption comes from poultry.Particularly, about 65–70 thousand commercial poultry farms are currently operating all over the country.Moreover, poultry rearing by women is common practice in almost all families in villages and plays a crucial role in self-employed and livelihood advancement of the poor women.