Horticulture

For example, Long et al. explored the barriers to the adoption and diffusion of technological innovation for climate-related smart agriculture in Europe and specifically in the Netherlands, France, Switzerland and Italy. Carrer et al. investigated the factors influencing the adoption of farm management information systems by Brazilian citrus farmers. Morris et al. , focusing on pastoral farmers across Wales, analysed the interconnectedness between farm diversification and technology adoption, with farm strategy as the central focus. Furthermore, Caffaro and Cavallo considered the roles of objective and subjective factors in the adoption of smart farming technologies in a sample of Italian farmers from the Piedmont region . Finally, Gittins et al. investigated the benefits and challenges associated with the adoption of new farm management technologies and software adoption in the UK livestock sector. Overall, these prior studies have suggested that the rate of adoption and perceived barriers relate to a specific innovation, to the farmer himself/herself and to the specific context . Factors depending on the specific type of innovation are related to the financial costs and economic benefits expected from the adoption of the new technology; the risks connected with this adoption; the advantages in terms of prestige, convenience and satisfaction; its consistency with the needs of adopters; the difficulties and complexity connected to the understanding and maintenance of the technology; and the observability of the innovation results . Factors related to the farmer are connected to his/her sociodemographic characteristics, hydroponic nft channel with the most important of which affecting the adoption of new technologies being farmers’ age, educational level, income, farm size, technological skills and actual use of technologies .

Finally, the adoption of new technologies is influenced by context-specific factors, such as political and social pressures, the national context and the regulatory environment . This study was conducted in the mountainous rural area of Valtellina, Valchiavenna and Alto Lario in the Lombardy region, close to the border with Switzerland . The area is characterized by a specific geographical conformation: the altitude varies from approximately 198 to 4000 m above sea level, and this area has a relevant east-west extension covering 3,212 km2 . More than 70% of the provincial territory is located 1500 m above sea level, and only a small portion is urbanized. Most of the area is composed of natural and forest areas , while the agricultural area covers only 7.4% of the total area . Nonetheless, farming activities represent an important economic source of income in this area. In 2016, 17% of the companies belonged to the agricultural sector, second only to the commercial sector, which accounted for 21% . The agricultural sector is also important for youth employment: 11% of young entrepreneurs operate a business in the agricultural sector, second only to tertiary companies.Agriculture is mainly based on traditional products of animal origin, such as the Bresaola of Valtellina GPI, Bitto PDO and Casera PDO cheeses, which are marketed in national and international markets . According to the last national census, livestock farms represented 44% of all farms, a percentage that seems in line with the average regional value .

Despite their economic importance, the number of livestock farms decreased from 1982 to 2010, on average accounting for 35%, one of the highest rates in the Lombardy region compared to the other provinces. In this case study, among livestock farms, dairy farms showed the smallest decrease , and unlike sheep, pig and poultry farms, the decrease in the number of dairy farms has been partially accompanied by a decrease in the number of animals , showing a general increase in farm dimensions and the withstanding of the dairy farming system . According to estimates, livestock products in the area account for approximately 63% of gross products sold, confirming the importance of bovine dairy farms for the local rural economy. As is typical in the alpine landscape, farming activities are performed in valleys, which are characterized by the presence of stable meadows interspersed with cultivated fields, particularly those containing corn for fodder production, and at higher altitudes, the farming system is based on pastures. The integration of valley agriculture and mountain pastures based on dairy farming systems is still of economic value and of important cultural and identity significance for the whole territory, representing a significant environmental and naturalistic heritage . Compared to other areas in the Lombardy region, only approximately 6% of farms have declared, according to the last national census, that they use computers and ICT devices for farm management, while the regional average is approximately 17%. Farms tend to use ICTs more to manage administrative services and crop systems than to manage herd production . In contrast, internet usage by farmers in the area is in line with the average regional value of 3% . With respect to farm size, more than half of the sample had less than 50 cows under lactation , 27.0% had from 50 to 100 cows, and only 8.0% of the sample had more than 100 cows. As shown in Table 1, the farmers from the cooperative seem to be younger and better educated than those farmers localized in other mountain areas. When asked, the cooperative managers confirmed that there had been important generational turnover in the last 10 years.

At the beginning of the interview, a brief explanation of the purpose of the study was provided to participants. The questionnaire was structured into two sections. The first section included questions on farmers’ characteristics, such as their gender, age, educational level, farm size in terms of the number of cows, professional use of a smartphone and expectations for the future of their farm. To explore farmers’ use of smartphones for professional duties, participants were first asked whether they owned a smartphone and for how long. Then, we provided a list of potential reasons for using a smartphone for farm management purposes and asked participants to indicate for which reason they were using their smartphones. For each reason, respondents were asked to express their frequency of use using a scale ranging from 1 = never to 5 = very often. To avoid any bias, there was also the possibility for them to mention any other reason not included in the list. Furthermore, to assess farmers’ expectations, participants were asked to rate how they see the future of their farms on a 5-point interval scale ranging from “very pessimistically” to “very optimistically”. The second section of the questionnaire sought to highlight the psychological measures that are expected to delineate farmers’ attitudes towards the use of technological devices, such as technophobia and technophilia, perceived obstacles and motivations for use. Farmers’ technophobia and technophilia were assessed by developing a specific scale considering the extant literature , which combines several statements of the “Technophobia and Technophilia Questionnaire – TTQ” proposed by Martínez-C´ orcoles et al. . The scale used in the current study was based on six items concerning farmers’ technophobia and technophilia towards new technologies. A five-point Likert scale with responses ranging from 1 to 5 was used to record participants’ responses. To investigate the perceived obstacles to the use of technological devices,based on the previous literature , we developed a ten-item scale including the constraints that hamper the adoption of new equipment and technologies on farms. Participants were asked to express their agreement using a scale ranging from 1 = strongly disagree to 5 = strongly agree for each item. Finally, following the extant literature , farmers’ motivations to use technological devices were investigated by developing a scale with ten items concerning the most important benefits that farmers perceive from the use of technologies in their daily work and that may drive the farmers’ adoption of new equipment and technologies.

The results showed that attitudes towards new technologies are affected by age, educational level, farm size, actual smartphone usage for professional duties and optimistic expectations for the future of the farm. Educational level, farm size, smartphone usage and expectations for the future increased significantly across clusters from the first cluster of technophobes to the third cluster of technophiles. Moreover, age significantly decreased from the first cluster to the other clusters. Therefore, our findings suggest that older farmers with lower educational levels, smaller farms, less frequent smartphone usage for professional duties, nft growing system and more pessimistic feelings regarding the future of their farm are less willing to adopt new technologies. These farmers do not have the knowledge and confidence to understand the benefits related to the use of technologies for breeding. To overcome these issues, considering that “information is the key to the diffusion of innovations” , new forms of presentation and learning may be developed by service providers and policy makers to address the needs of these adverse technophobe farmers, who constitute a relevant part of the population. This innovation process is of great importance since technophilia, or the propensity to use new technologies, plays a fundamental role in the sustainable development of mountain farming and breeding. Although our results cannot be generalized to all mountain areas and to the whole mountain area itself, we found several elements that are in line with the previous literature . More specifically, in terms of age, our findings corroborate previous evidence showing the existence of a negative relationship between age and the adoption of new technologies, probably because older farmers have shorter career horizons than do younger farmers and, therefore, are less motivated to innovate.With reference to educational level, the results are consistent with the previous literature reporting that less educated farmers are less confident and less inclined to use new technologies . Considering farm size, we found that technophobe farmers have smaller herds, as reported in the literature, probably because smaller farms do not create adequate economies of scale and incentives for the adoption of new technologies for farm management . The owners of larger farms are more able to absorb the associated costs and risks. With respect to smartphone usage, technophobe farmers had the lowest frequency of use of smartphones for professional duties, meaning that farmers who are less confident in using technological devices have fewer technological skills and are more likely to have a negative attitude towards new technologies and be more reluctant to innovate .

Although our findings related to age, educational level and farm size have been analysed in previous papers , some novelties of our study are worth emphasizing. First, we focus on a sample of mountain dairy farmers. Second, we propose a clustering analysis of the farmers based on three attitudinal determinants: technophobia and technophilia, perceived obstacles, and motivations to use. To date, this approach has not been applied to farming system analysis, even if understanding the underlying factors that affect the adoption of technologies is imperative to allow policy makers to develop more effective and targeted policies. Farming systems in Europe are experiencing multiple adverse shocks and stresses, such as weather extremes, price fluctuations and changes in policies and regulations. Under these multiple shocks and stresses, improving or even maintaining generally mediocre levels of sustainability of farming systems is increasingly challenged . The presence of critical thresholds adds dynamic complexity for farming system actors and policy makers. This is because beyond such thresholds, drastic system transformations may occur that are difficult to anticipate and to manage. For instance, the speed and scale of system processes after exceeding a critical threshold may be incompatible with the adaptation capacities of current institutions . Exceeding a critical threshold is most often undesirable as it generally leads to lower sustainability levels, e.g. a decline in biodiversity and human well-being .Moreover, this state with lower sustainability levels may be more persistent resulting in reduced options to improve sustainability. Timely knowledge on critical thresholds is therefore needed to prevent exceeding them , but it is often difficult to anticipate the exceedance of a critical threshold . In absence of clear knowledge on thresholds, Walker and Salt propose to work with thresholds of potential concern that inform management goals that aim to avoid those thresholds, without knowing exactly where they lie. In either case, the threshold level being known exactly or being a TPC, Monitoring is needed in order to detect the closing in on a critical threshold.

The concentration of TA was correlated with the CO2 flux in both months , suggesting the impact of CaCO3 dissolution/ precipitation. This situation was complex when we found that the correlations were positive in March but negative in April. The rapid increase in TA and decrease in pH with time in March , along with DO consumption and other GHG concentrations, may imply carbonate dissolution in the water column or on the sediment surface. If this assumption is correct, the fluctuation of TA concentration in the shellfish pond can be well explained by the balance of CaCO3 precipitation , which could decrease TA and carbonate dissolution, which could increase TA. In March, the carbonate dissolution may overwhelm the precipitation, and the TA and free CO2 can be enhanced simultaneously, along with the increase in DIC and decrease in pH. When the precipitation dominates the TA concentration in April , 1 mol CaCO3 precipitation would decrease 2 mol TA and increase 1 mol free CO2 , resulting in a negative correlation between TA and CO2 flux. Hence, shell formation may play an important role in budgeting CO2 emissions from shellfish farming systems. The inclusion of CO2 released during shell formation in the carbon trading system remains debated. A previous study estimated that the GHG footprint of oyster farming is 0.13 kg CO2-eq kg/protein, with the major source being N2O the oyster, and no emissions from fodder production and sediment release . Although we did not acquire biomass production from the constricted tagelus we studied, ebb flow trays the significant emission of non-CO2 GHG indicates that the estimation is much more complex than that in a non-fed culture.

We combined the results from March and April and found a positive correlation between POC concentration and DMS flux . Because we did not measure the phytoplankton biomass in the ponds, we assumed that phytoplankton abundance was directly proportional to the POC concentrations. Much more DMS was released in April than in March, suggesting that more microphytoplankton developed and subsequently dissociated in April, because the DMS was most likely derived from algae by viral infections, planktonic exudations, and sloppy feeding . This assumption is reasonable because solar radiation is stronger in April, which leads to stronger photosynthesis than that in March. However, more field studies in aquaculture systems are necessary to improve quantify DMS flux and its controlling factors. The variability of GHG emissions and nutrient concentrations between March and April indicates temporal uncertainty during the period of constricted tagelus aquaculture. We expected a reasonable effect on GHG emissions by the stocking density and survival rate , which influences the disturbance of sediment and biogeochemical processes . Our observations may represent a mediating result because the samplings were not conducted at the beginning of farming in winter, when there were few nutrient loadings and small individuals, nor were they conducted at the end of summer, when there was mature biomass and strong respiration. Taken together, changes in environmental parameters may regulate the spatial-temporal distribution of GHGs in constricted tagelus farming ponds. We deduced that farming mode-induced GHG release, such as nutrient loading, shell formation, and sediment perturbation by tagelus activity or wastewater draining, were the determining factors responsible for the higher GHG emissions than in the natural environment or traditional bivalve culture . Shellfish mariculture provides benefits, particularly in solving food security and promoting economic growth , or the filtration of particles, promoting nutrient recycling . However, the rapid expansion of production and industrialization has raised environmental concerns . In our cases, other than CO2, large quantities of non-CO2 GHG were observed during the constricted tagelus cultivation period, indicating the uncertainties of GHG emissions associated with fed shelled-mollusk aquaculture.

The additional feed and seawater input increased primary production in the microalgae pond and therefore amplified the potential for GHG production in the culture tanks. Additionally, nutrient fertilization can cause severe eutrophication in ponds, enriching sediments with organic matter. Another important benefit of shellfish aquaculture is that the formation and growth of calcareous shells are used as a CO2 sink; thus, the burial of sinking particles and the formation of shells from mariculture may have the potential to increase the storage of “blue carbon” . However, before entering into carbon trading schemes and planning an expansion of production, the carbon biogeochemical cycles and life cycle assessment in shellfish mariculture should be qualified and quantified because the accompanying respiration, fertilization, and changes in carbonate chemistry during shellfish farming would lead to a considerable source of atmospheric CO2 , which is usually not intuitive. Another environmental concern has been raised by the over application of fertilizers that introduce considerable nutrients into ponds, leading to nutrient enrichment and species loss in aquaculture and other ecosystems . These unutilized nutrients are rapidly transported to nearby estuarine sea waters via routine water exchange or accumulate in the soil, causing further GHG emissions . Thus, considering the significant GHG emissions from our study, the net harvest of tagelus mariculture can be an important food provision but is not a reasonable result to be included in the carbon trading system, even if it locks away carbon in solid mineral form. Future work should focus on consolidating the potential of GHG emissions and reducing nutrient loading in ponds and nearby coastal waters. Non-fed aquaculture is likely the primary problem-solving method to achieve these goals because it can avoid destroying natural harbors and attenuating nutrient excess in coastal waters. For example, mollusk aquaculture is a simple culture technique that is ecologically beneficial to the surrounding environment : it releases negligible GHGs from sediments without the influence of protein production , suggesting an environmentally friendly farming pathway.Monoculture production increases the risk of nutrient waste, whereas IMTA significantly increases the sustainability of recycling waste nutrients and is a greening system for high productivity rates, nutrient removal, and the production of a marketable product.

Additionally, IMTA may also benefit from improving acidification and deoxygenation and achieving ocean negative carbon emissions . Although this goal is unrealistic in the short term, studies should be conducted on expanding shellfish aquaculture worldwide . In our study, the disturbance of the sediment surface caused by water draining or tagelus activity contributed the largest part of non-CO2 GHG emissions and therefore should be considered in fed shelled-mollusk cultivation. Gentle water exchange modes or manual interventions are recommended to ameliorate the potential release of buried GHGs into atmosphere. By adopting moderate farming modes, mariculture can produce high-quality protein and provide low GHG emissions . Finally, non-CO2 GHGs, other than CO2, should be considered in the calculation of the potential effects of GHG emissions and the evaluation of the side effects of carbon sequestration and food production in mariculture. With a deep understanding of GHG emissions in aquaculture, incorporating shellfish cultivation into carbon trading schemes is possible, allowing the system to become a potential element of “blue carbon” systems. Tillage is a soil management practice that reduces production costs by facilitating various cropping tasks such as seeding, planting, fertilization and weed control; therefore, it remains one of the most common agricultural tasks worldwide . Against these advantages, unreasonable tillage presents a major environmental problem by accelerating erosion processes, being especially dangerous in sloping farmland . Tillage erosion is triggered during the production process and sometimes also acts as an aggravating factor for other land erosion events . Mechanized tillage generates alternative upward and downward soil movements that cause the alteration of the soil structure and make the soil more susceptible to degradation and fertility loss . In this sense, tillage direction is a decisive factor for assessing the impact of plowing on the soil profile , together with terrain features such as the slope gradient and other aspects inherent to agricultural practices such as the type of tools used, operation speed and depth of tillage . In addition, tillage direction is a critical value to determine input variables in models that study tillage erosion and water soil erosion, flood and drain tray such as the tillage transport coefficient and support practice factor.

The reason for this is that tillage oriented along the land slope can generate preferential runoff paths and cause an increase in water erosion . In contrast, contour farming, defined as plowing to constant elevations that are perpendicular to the normal flow direction of runoff , would allow us to control the harmful impacts of tillage on land modification and is considered a soil conservation practice . Therefore, the quality of the results provided by these soil erosion models depends largely on determining the tillage direction values and, as a result, defining the contour farming area with higher accuracy, which is viewed with great uncertainty due to the lack of precise and robust methodologies designed for this purpose . Remote sensing is a versatile technology with multiple applications in agricultural and environmental scenarios , such as those aimed at monitoring tillage or soil conservation practices , soil erosion phenomena associated with agricultural land uses and natural or artificial landscape features, e.g., terraces and physical obstacles, which allow control of run-off . Research specifically focused on the characterization and mapping of tillage metrics is further supported by geographic information systems , although real implementation of the proposed procedures presents certain limitations because they rely on strict hypotheses or have a very local scope. For example, Drzewiecki used available digital spatial data and a GIS environment to define parcel boundaries and applied a criterion of perpendicular coincidence between the main directions of the longest parcel edge and slope aspect to identify the parcels with contour farming in an upland area in southern Poland. This procedure was later automated for application to larger regions through the application of object-based image analysis  and implemented in a diversity of soil types and land forms by using light detection and ranging data and GIS tools . This method is valid only under the assumption that the plots are cultivated along their longest edge, which is not necessarily true in the case of mechanized plowing, and prevents its application in square plots with all edges of similar length . By applying other criteria, Panagos et al. estimated contour farming areas at the European level using the 25 m resolution digital elevation model and assuming that farmers had correctly implemented the good agricultural and environmental conditions defined in the common agricultural policy aiming to achieve sustainable agriculture.

However, this approach has the weakness of ignoring the fact that the effective control and monitoring of GAEC compliance is often hampered by technical and methodological problems . Despite the cited research efforts, to date, there are no remote sensing applications to accurately assess critical tillage features such as tillage direction and contour farming in cultivated plots, mainly due to the constraints inherent to the spatial resolution of piloted aircraft and satellite images , respectively that prevent the clear observation of the narrow tillage marks caused by machinery. Alternatively, unmanned aerial vehicles or drones now offer a viable option that has not yet been explored for this purpose. UAVs capture ultrahigh spatial resolution and on-demand aerial images that allow the detection of small geometrical patterns in the terrain, which is not possible with images from other conventional remotely-sensed platforms . The main difficulty with these UHR images in complex agricultural scenarios lies in the development of efficient analysis algorithms with the capability to identify tillage features and determine their main metrics. Tillage labour generally follows a fluctuating trajectory depending on parcel orography and the farmer’s arbitrary decisions on the time and manner of tilling, and the tillage marks appear in the images as linear objects that are affected by other elements of the scene, such as trees, shadows, cover crops, weeds, stones, etc., which break their linear structure and greatly complicate the image classification processes. This image analysis challenge can be met with the OBIA paradigm, which offers tools that are not available in traditional pixel-based methods . OBIA integrates the spectral, morphological, contextual and hierarchical characteristics of the segmented objects into the analysis, which leads to a high level of robustness and automation and a significant improvement in results compared with pixel-based methods .