Horticulture

The effective field capacity averaged 0.1 ha/h with minimum and maximum of 0.09 ha/h and 1.12 ha/h, respectively. This indicates that it requires about 10 hours to weed one hectare of a maize field as opposed to 80 man–hours required when done manually as reported by and . Therefore, weeding using the developed cultivator can reduce time and labor requirements in farms .Reference evaluated five-tined and three-tined animal drawn cultivators and found their effective field capacity to be 0.08 ha/h and 0.05 ha/h, respectively.Hence this new developed cultivator is more efficient than both five and three-tined cultivator developed by . This study has demonstrated that it is technically feasible to integrate planting and cultivation units onto an existing mould board plough beam to develop an integrated tool suitable for use in small-scale maize farming.

This approach to development of farm tools has potential to reduce ownership costs of equipment which may contribute their increased adoption. This would be in line with current strategies for agricultural transformation in Africa whereby mechanization has been identified among the critical inputs needed to reduce extreme drudgery while improving productivity and production. Results of performance testing for the prototype indicate that its level of efficiency, effectiveness and reliability for planting and weeding operations is superior to manual operations currently used by small-scale farmers in the county and is comparable to similar but single-unit prototypes developed in other parts of the world. This demonstrates that the prototype has potential to transform mechanization on smallholder farms which in turn can result in improved productivity and production on such farms. This would lead to improved food security and reduction in drudgery associated with hand tools thereby making agricultural work more decent.

It is recommended to make further improvement on the prototype prior to adoption especially perfecting the seed metering devices to reduce seed damage and to develop supplementary metering devices such that the planter can be used as a multi-crop planter to sow other crops like soy beans and groundnut. In addition, an economic and ergonomic evaluation for the new developed prototype needs to assess suitability. Further, more rigorous testing in farmers’ fields with different soil types and conditions should be carried out to generalize field performance of the prototype. In Benin, bananas and plantains’ value chain is gradually developing as an important economic opportunity to stakeholders. Many food products derived from bananas and plantains are found in local and urban markets and are sold better than before . However, these include improved plantain chips, banana cakes and banana wine.

Despite the food and economic importance of this value chain,bananas and plantains are less available and scarce at given times of the year on the markets. This unavailability would undoubtedly be linked to the various production and marketing constraints.Production and marketing are the main constraints undermining the development of banana and plantain value chain. Concerning production constraints,the most important is the premature fall of plants under violent wind,diseases and pests damages. These constraints considerably reduce more than half of production. To solve these problems, research actions are essential to develop systems that are both inexpensive and effective to protect cultivated plants against climatic hazards. Natural repellents, made with local products such as neem seeds, have kept diseases and pests away from cultivated plants .

It is also fundamental to relay the results of previous studies, going beyond analysis in terms of returns. It is essential to adopt a more comprehensive approach to the economic and social benefits of agroecology with an increase in household income linked to savings achieved through the abandonment of chemical inputs and the diversification of production. Several agricultural practices are used by the farmers to reduce these constraints’ effects .Farmers use agricultural practices such as self-production of seeds, crop associations,crop rotation, irrigation, use of animal droppings to fertilize soils, and use of bio pesticides to control pests and diseases. Irrigation is essential for the survival of bananas .

The information collected covered organic practices adopted, societal and demographics of operators; characteristics of farms; farmers’ beliefs, attitudes, and perceptions related to organic farming practices, and so forth. A large sample of farmers from a population of total 4818farms in Georgia were randomly selected and interviewed. The instruments used in the interview were designed by faculty members of Fort Valley State University and the field work of the survey was administered by the Burruss Institute of Kennesaw State University. Farmers who respond to the screening question, “Do you produce fruits and vegetables?” with a “yes” were retained in the sample.About 2404 farmers were contacted, with 456 farmers going through the survey, which gave a return rate of 18.9%.

The instruments encompass abroad spectrum of questions pertinent to production practices, social demographics,individual attitude, beliefs, perceptions, as well as the characteristics of farms. The organic production in the survey may take the form of the USDA certified, certification exempt, or transitioning farms. The interview were conducted by trained personnel following the well-established procedures, which insures the veracity of data collected. However, it is also in evidence that some self-selection biases occurred due to the fact that the higher level of education were associated with organic producers and they were inclined to finish the survey retained in the sample, hydroponic dutch buckets which make the organic operations in the sample high than the overall percentage in Georgia farms in 2012 Census of Agriculture.The defect may constrain the effort to reach a general extrapolation beyond the survey data. In Table 1, the variables covered in the survey and the corresponding preliminary statistics were reported to provide a profile of small farmers in the Southern region of states.

We approached farmers’ choice of organic farming and potential factors of influence with the help of the logit regression model. After comparative study on the logit, the probit, and the linear probability models, being alike in ways in analyzing categorical data , we first exclude the linear probability model for its bias and inefficiency. The logit and probit models are arguably equivalent, only many investigators prefer the former for easy interpretation of parameters. For the sake of comparability, we used the logit  model hereof. Since rich documents related to the logit model are readily available in the literature, the authors just present a model brief in the context of this investigation, rather than a thoroughgoing model discussion in the coming section. As usual, the most challenging part of modelling is associated with model selection among many alternatives. In this study, we adopted the approach of Purposeful Selection of Variables , which usually retains important confounding variables and result potentially in a slightly richer model. We beganour model fitting by a univariate analysis of all variable relevant. Any variable with a significant univariate test at 0.25 level was selected as a candidate for the multivariate analysis. In an iterative process, covariates are removed from the model if they are non-significant and not a confounder.

Significance is evaluated at the 0.1 level and confounding as a change in any remaining parameter estimate greater than 15% as compared to the full model. A change in a parameter estimate above the 15% indicates that the excluded variable was important in the sense of providing a needed adjustment for one or more of the variables remaining in the model. At the end of this iterative process of deleting, refitting,and verifying, the model contains significant covariates and confounders. Then,we took into account of any variable not selected for the original multivariate model and added them back one at a time, with all significant covariates and confounders retained earlier. In such a way, other variables which, by themselves,were not significantly related to the outcome but became an important contributor in the presence of other variables will be included in the final model.

After livestock and field crops, piece work is the next most important income source, followed by a group of activities that we have grouped into the category “other”.It is important to note that the contribution of livestock to income is largest among food secure households with an even larger prominence in the non-hotspot district. Piece work, on the other hand, is clearly most prominent among the extremely food insecure and most visible in the hotspot districts. Compared to all other food security groups, food insecure households appeared to have the most diversified set of income sources, with a significantly larger weight on numerous other minor sources collectively referred to as ‘other’ than any other group of households.

This study uses qualitative research techniques to identify the livelihood activities and to determine the position of livestock in the hierarchy of those activities among households in livestock-rearing communities of Southern Zambia. Seasonality analysis was also done to identify the shifts in relative importance of the activities across the different times of the year. The results indicate that, when livelihood is broadly defined, nft hydroponic livestock rearing is second only to field crop production both in terms of prevalence and relative importance . However, livestock and livestock-related products and services are by far the most important source of income. Livestock as a source of income is especially important among the food secure households, who earn more than half of their income through sales of livestock, livestock products and livestock-related services , compared to less than 40 percent among other food security groups.

That livestock is important as sources of livelihoods and,especially, income identifies the need to identify and implement interventions that could enhance livestock ownership and productivity. While food secure households are more commercial in approach, less food secure households have their sustenance and subsistence in mind. This inherent dichotomy in approach to livelihood needs to be reflected in intervention design and implementation. The peculiar geography of Cameroon makes it called “Africa in miniature”; the country has an important ecological diversity to find within it all the environmental varieties of the African continent. A study of the operating model of Cameroonian agriculture reveals the existence of diverse farming techniques and agricultural typologies that perfectly match this environmental richness.This diversity of Cameroon’s agriculture gives the agricultural sector enormous economic potential, which makes it a particularly important sector in the promotion of development.

In fact, the agricultural sector in Cameroon represents about 60% of the active population, with a contribution of about 30% of the GDP and about 16% of the budget revenues of the country.This sector has always been considered as the main provider of wealth, which is why the speeches of the various Cameroonian political authorities have always been considered as a priority in the country’s economy. But the fact is, in general,a relative stagnation of the agricultural performance of the country was noticeable.On the external level, this can be explained by constraints related to the international environment, particularly via the volatility of the prices of export products, whereas internally it depends fundamentally on the policy measures implemented, their coherence, and their phasing, as much as their deadlines.The crises of the mid-1980s and recently the 2008, sparked renewed interest in the predominant role played by the agricultural sector in developing countries.Cameroon has agricultural crops. These are divided between perennial agricultural crops and food crops for local consumption and, to a lesser extent, for export. In the category of perennial crops, the main products selected according to their economic importance are: cocoa,coffee, rubber,banana, cotton, tea; food production is more diversified; Without pretending to be exhaustive, we can mention in this category: groundnuts, plantains, tubers,fruits, maize, potatoes and other tropical products.

The analysis of the role of agriculture in economic activity is of major interest,because of its contribution to economic growth and the consideration of the influence of internal, external and institutional factors stemming from the in classical literature, the architecture and credibility of institutions are also a major determinant of agent incentives and therefore economic performance. Indeed,the main objectives of agricultural progress are known; increasing yields by increasing the volume of production and increasing productivity.

Further, the responses of species and ecosystems services to increasing intensification levels were discussed . We here present a conceptual model to understand the ecological and ecosystems consequences of different land-use dynamics associated to agriculture intensification.Upper diagram in Figure 2 shows nine landscapes composed of different proportions of natural habitat and farming area, as well as different degrees of agriculture intensification on the agriculture part. Landscapes can change in all directions according to changes in natural habitat proportions and agriculture intensification levels.Agroecological conversion changes landscapes in the downward direction, while agriculture intensification is the upward opposite trend .

From left to right, decrease in NH characterizes “natural habitat’ contraction, and the right to left change is the “natural habitat” expansion or Forest Transition. A is a landscape totally occupied by “natural habitat”, B consist of 80% of habitat cover proportion and agriculture intensification index of farming area is 0.2, B’ has 0.8 of habitat cover and agriculture intensification index is 0.5 and so on. Here we use the term “natural habitat” in the operational sense, ebb and flow table defining it as areas subjected to very low levels of human intervention, rather than in the philosophical sense which refers to it as untouched or untamed nature.Additionally, the available data of remote sensing about wood cover, as well as theoretical approaches to estimate populations sizes, uses this term, which will simplify our analyses, enabling to address regional issues, as will be further developed.Farming systems in which AI is equals to 1 are characterized by high input, low heterogeneity and high levels of human disturbance and low planned and associated diversity.

Traditional and poly culture,home gardens and low shade agroforestry are considered having agriculture intensification equals to 0.5. Finally, we assume that agroforestry and extractives systems locate at 0.2 on the intensification scale. On the other hand, theoretical and empirical studies show that fragmentation affects population inhabiting these patchy landscapes in a non-linear association. Particularly, population sizes falls steeply below 30% of habitat cover proportion, which represented by the dashed line in the upper diagram of Figure 2. Although we assumed a linear area-density relationship in the bottom right graphic, total population size in landscapes with less than 30% of habitat proportion is overestimated for not accounting for decrease in habitat population pool. Because species response to fragmentations depend on several factors such as timing since fragmentation, species specific time-lags in the response to habitat contraction,and differential functional response given to sensitive to human disturbs , making precise estimations rather uncertain.

To make our model simple and reliable, we did not incorporate this non-linear aspects assuming that density is fixed regardless of habitat area. Therefore, values in dashed boxes may be overestimated, which is of further conservation concern as will be further discussed.According to LSP proponents, moving upward in the diagram will reflect in changes in the left direction, inducing habitat expansion . Jevons Paradox, on the other hand, concern increasing yields inducing habitat reduction.As we show in the land-use and economic session, although forest transition fostered by yield increase occurs by some extent, the Jevons Paradox is much more frequent.As intensification increases farm area and decrease biodiversity in agriculture portion, as well as in the habitat portion , landscapes will change in D or E direction, tending to very low associated landscape biodiversity. Due to the fact that actual population sizes must be smaller than presented lower right graphic in Figure 2 as above mentioned, these landscapes with small habitat proportion, which are patchily distributed and embedded in intensive matrixes, may harbor very few species, specially sometime after fragmentation and intensification. Decrease on population sizes of once common species in the European farmlands show the extreme effects of agriculture intensification at long term.

Unfortunately, tropical landscapes undergoing to agriculture intensification may face similar problems in the future.Most of the tropical high bio diverse regions , such as the Brazilian Cerrado, Atlantic Forest, Meso-America, Western-Ghats and and Siri Lanka are around or below the 30% habitat cover and simultaneously undergoing agriculture intensification. This multiple effects reduce populations sizes in the matrix, as well as biological fluxes among habitat patches.This combination of increasing deforestation and matrix agriculture intensification is predicted to cause local and global extinctions at long term. On the other hand, if intermediate levels of agriculture are maintained as assumed by LSH hypothesis,and habitat loss is controlled, significant amount of biodiversity is maintained although at lower levels then the“original” non-managed landscape.

Confined to limited access to chain retailers, small farmers turned to the “thin” farmers markets for their products, in which finding reliable buyers turns out to be difficult and full of uncertainty. Farmers held a perception of lower yield of organic farming is 11.92% less likely to convert to organic farming. The low yield of organic farming is not just perception,but a fact in many areas, due to the lack of high-quality seed, organic fertilizer, and effective pests and weeds sprays. The perception of lower yield could easily explain farmers’ behavior of withholding converting to organic farming. There isn’t much evidence to verify the veracity of the claim of high liability, but the perception came up with a distinct adverse impact.

Farmers concerned about the higher liability in organic farming is 10.57% less likely to convert to organic farming. The conception likely came from the yield uncertainty due to damage from pests and/or weeds, which easily results in default on loan and other financial responsibility that must be cushioned with more debts. Consequently,the perception of high liability discouraged farmers to steer their production to organic farming. The size of a farm matters and negatively impacts the conversion. As the production scale increases, the chance of conversion become smaller. In this sample,the chance of conversion for large farms is 20.71% lower than their smaller counterparts. some previous studies that organic production poses great managerial challenge due to the intensive labor demand in dealing with diseases,pests, applying fertilizer, and handling marketing , and constraints on substitution of capital for labor. As a result, production scale was deemed as an uncontentious barrier to the organic conversion.

In the conversion to organic, the age of producers is another barrier. Table 3 shows that the probability of the conversion dropped substantially as producers become older. With the base group of 30 or young, the age group 31 – 60 has a probability of 20% lower in adopting organic farming, and the group 61 or older has a drop of 24%. It makes sense that the shorter planning horizons for older farmers offered less time to recapture investment costs and capture the long-term benefits. In addition, it was claimed that as one ages, the avoidance of risk becomes more important than expected future higher returns. The conversion to organic farming also demands the time and efforts to assimilate organic knowledge and methods, and likely expose farmers to lower yields and non-premium price in the transition period, and these surely weigh in on farmers decision on organic farming. Education wedged its influence in organic farming in a complex way.

The chance of conversion is 13% higher for farmers who had education of technical school or higher than farmers with just high-school diploma or lower. This conformed a previous claim that farmers with some college education had higher odds of adopting organic farming. However, the impact of education was not observed in conventional farmers and those with mixed enterprises. The lack of observations on mixed enterprise group may be the root of the result. While some results of this study bear resemblances to previous studies, there are some noteworthy differences identified. The impact of off-farm job is one of them.Our model was not in supportive of the claim that there exists an inverse relationship between working off-farm and the adoption of organic farming because off-farm job reduce the availability of labor and hence impedes organic farming practices. On the contrary, the impact of off-farm jobs in our model lead to an increased likelihood of organic conversion, which may reflect the influence of the enhanced risk tolerance due to extra income from off-farm jobs. The absence of off-farm job variable led to substantial changes in parameters of other variables in the model, so it, though less significant in statistics, was retained in the model. The further clarification hinges on the future studies with more observations and elaborated instruments related off-farm job. Analysis of the large survey in the Southern states came up with a few contributing factors in farmers’ choice of conversion to organic production. The pool of factors comprise barriers and stimuli, each of them have been discussed in detail in the previous section.

In view of potential impacts and the efficient way of improve the adoption of organic farming, the four factors of risk aversion, the age of operators, and the size of farms, and marketing channels deserve further elaboration.Organic farming is still at its early stage, there exist tremendous uncertainty in both production and marketing process. For most risk version farmers, an acceptable way to follow was doing by the top dog. Nevertheless the exemplars available at this stage are quite limited. A way to increase farmers’ expose to and know those successful frontrunners is to organize more workshops and training.

Plants generally have an increased demand for N at various stages of development, particularly during leaf development and flowering;therefore, this element is frequently applied as a mineral fertilizer.In our study, vegetables cultivated in the conventional way were supplied with this nutrient several times during the growing season, mainly in the form of ammonium sulphate. Nevertheless, our results indicate that the concentration of this element is higher in organic crops which correspond with the higher concentration of N in the soil.Na is considered to be of secondary importance in the soil and its uptake depends mainly on the plant species as well as the K level of the soil, rather than the concentration of Na extractable from the soil.

Colla et al.  in a two-year study demonstrated that the soil Na content did not significantly differ among organic, conventional and low input farming systems. The same authors observed that the Na content in the crops changes over time and one year they recorded a significantly higher level of this element in conventionally grown tomatoes but the following year there were no differences among farming systems.The meta-analysis by Worthington  showed that organic crops on average have about 20% more Na than conventional ones. In our study, no important differences in the soil Na content between two growing systems were observed.However, the concentration of Na in organic vegetables was significantly higher in 4 out of 8 vegetables analysed.

The total content of K in soil ranges from 8000 to 25,000 mg∙kg−1 and it depends on the clay fraction and soil mineral content. The highest concentration of K is found in heavy soils, where the clay content represents more than 3%. In loose sandy soils, K concentration usually does not exceed 0.4% . The monitoring of the soils, plants, agricultural products and foodstuffs in 2000  reported that the average concentration of available K in the soils of Poland was184 mg∙kg−1. The average for the south-west region, where both farms are located is, however, much higher and represents 270 mg∙kg−1. Generally, it was reported that mineral fertilizers increase the content of K in the soil to a larger extent than organic ones. Though, it is not entirely true in our study, where the content of available K in the soil was slightly higher in the organic samples  compared to conventional ones.

A lower concentration of K in the conventionally treated soil might be the result of a slightly more acidic pH which might lead to the higher loss rate . Plants absorb K ions from the soil solution and usually contain more of this element than other minerals such as Ca, Mg or P. The concentration of K in plant cells exceeds significantly the content of this element in the surrounding environment. This indicates that K is actively transported by plants, despite the concentration gradient. Our results clearly confirm this process.Due to many factors, the content of K in the plant can vary from 1245 to 33,190 mg∙kg−1. It was observed that the roots contained less K than the above ground parts of the plant , which can be clearly observed in the obtained results for the root and leaves of parsley.Analysis of K concentration revealed that organically grown vegetables in our study had generally higher content of this element compared to conventional ones. The difference was significant in the case of celery, carrot, potato and onion.

It is in agreement with the results obtained by Worthington which showed that organic crops contain on average 10% more of K than conventional ones. On the other hand, Warman and Havard demonstrated that the superior level of K in organic crops is not conclusive. Some vegetables grown in a conventional way, such as carrot, had a higher content of K over organic ones,but in the case of cabbage, it was opposite.The total content of P in the soil varies from 200 to 1500 mg∙kg−1 and a significant amount of this element comes from soil organic matter . The soil monitoring program revealed that the average concentration of this element in Poland is 274 mg∙kg−1 and that the average for the south west region is 352mg∙kg−1. Furthermore, Lemanowicz and Koper  reported that arable soils usually contain more of this element and in Poland; it is on average 478 mg∙kg−1.In our study, the P content of the soil was generally higher than in Poland and in this region. It was observed that the organically treated soil was twice richer in P compared to the conventional one.

Lemanowicz and Koper , in their studies,showed that the use of organic fertilizers in the form of manure significantly increases the amount of available P, while mineral fertilizers in the form of ammoniumnitrate reduce its amount in the soil. hey showed that among the different types of fertilizations,organic ones such as compost contributed to the highest increase of soil P content. This may explain the results obtained in our study, where an elevated amount of this element was found in the organic soil probably due to the regular application of compost and cow manure.P is taken up by plants from the soil solution as soluble or thophosphates  at a soil pH between 6 and 7.

This included their outputs, the market prices for their products,their wage bill and the cost of each input. In the sample, some farms depend exclusively on the family for labour input while others use hired workers.Given that western agriculture is still predominantly characterised by family farms , there is a long established tradition of including family labour in institutional reports and research studies that seek to provide comparable farm incomes . Therefore, we calculate and add the opportunity cost of family work by applying the average hourly cost of external wages in our sample to the number of hours of family work on each farm so as to calculate income before and after wages. Our environmental performance indicators are paddy field GHG emissions and energy consumption, distinguishing between direct and indirect consumption.

The GHG protocol distinguishes three scopes which help identify the information that needs to be collected about the discharged and induced greenhouses gases : Scope 1 deals with emissions released directly by the company. This includes production and service processes owned or controlled by the company as well as the corporate fleet of cars and trucks. The GHG protocol covers only the six GHGs listed in the Kyoto protocol. CFCs and NOx are excluded, it is argued,on political grounds.Scope 2 coverse missions indirectly caused by the generation of purchased electricity. And Scope 3 includes emissions from suppliers of inputs and downstream emissions from distribution, use and end of product. Scope 3 extends this accounting scope to emissions indirectly attributable to the purchase of all kinds of goods and services such as semi-manufactured goods, transportation services, waste disposal services, outsourced activities, etc.

This study considers all three Scopes. Unfortunately,we did not have access to measurements of other environmental externalities of rice production, such as, impact on human health, loss of biodiversity,wildlife and landscape degradation, water filtering or the substitution of natural wet lands . These environmental impacts regrettably lie outside the scope of this paper. The collection and conversion of data were made possible thanks to a joint enterprise involving the authors and the researchers of an EU-funded project for assessing the potential of agriculture to combat climate change . This project seeks to apply a common evaluation system in the four largest agricultural economies of the EU so as to identify suitable farming practices.This has resulted in the development of diagnostic software capable of converting the data collected via surveys into direct  and indirect energy consumption, both expressed in gigajoules  per year. To this end, a questionnaire was first designed to facilitate information collection and to enable aconsistent level of comparison.

With the data, and on the basis of a series of consultations with experts in the field of rice production in the region, the team were able to build the environmental indicators that are used in this study. These data refer to both physical and monetary measurements of farm size, location, annual yields, brand and age of machinery used, litres of fuel consumed, kilograms of seeds planted, amounts of fertilisers, herbicides and pesticides used in the field, characteristics and amounts of water required, and flooding practices during the season. These raw data were then converted into GHG emissions and energy consumption statistics.ISO 14064-1 and the GHG protocol  guidelines were followed to convert the data collected into GHG emissions. Emissions of different GHGs were converted and are expressed in equivalent tons of carbon dioxide per year. The main audience for the results of the study are the farmers included in the sample given that the project studies current practices in order to identify best practices and innovative methods for improving environmental performance. On the conclusion of the study, two meetings were held with the farmers in order to share our results and to suggest practices that help combat climate change.The study sample comprises nine farms. Of the nine farms, eight practise the various techniques of conventional farming and one operates as an organic farm.In total, nine farmers attended a personal interview with the authors of the study.

The selection of farmers was made based on the personal availability to participate and the comparability among farms. In accordance with the ethical agreement governing interviews, the specific identity of the participants cannot be disclosed. Five farms specialise in a variety of rice known by the name of gleva,and four specialise in a variety known as bomba. The varieties of rice produced, the size of the farms and the yield productivity per hectare of the farms included in the sample can be considered representative of rice farms in Spain.All the data collected adhere to the same definitions and were measured applying the same rules. All figures and data correspond to the same year,that of 2011.The inclusion of an organic farm allowed comparisons to be drawn, given that previous research suggests that organic farming tends to have a lower environmental impact .

Automated actuation devices  like sprinklers, foggers, valve controlled irrigation system, harvesters and others, can be used to control irrigation, fertilization and pests in order to offset the adverse conditions .These included grid soil sampling,yield monitoring, and crop scouting. The strategically positioned sensors collect data in the form of electronic computer databases which gives birth to the Geographic Information System for statistical analyses of data, to determine variability of agricultural land with respect to its properties . An effective method used to easily interpret remote sensing data is calculating vegetation indices-VI .

VIs has been widely used to assess vegetation condition, cover,and growth, as well as evaluate canopy attributes such as leaf area index  and plant height. VI values indicate differences in vegetation condition or amount. It is necessary to determine the total number of sensory devices required to be deployed per ha of cultivatable land while considering planting density. Closed loop control ability as well as adaptability  for different scenarios and sensor nodes is needed for the field operations . Each of these scattered sensor nodes has the capability to collect and route data either to other sensors or back to an external base station. The base station is made capable of connecting the sensor network to an existing communications infrastructure or to the Internet where a user can have access to the reported data . However, operational techniques are required to be transmitted to the farmers for incorporation of RST into farming by the extension agent whose responsibility is to teach farmers the techniques in utilizing new technology in farming.

These extension agents were trained by lecturers of Agriculture in the universities to make them competent in identification of farmers’ problems for solution. These extension agents and lecturers are knowledgeable in innovation that can be transmitted to the farmers for enhanced production especially in the use of sensory technology for precision farming.In Nigeria, many farmers detect presence of draught, pests and diseases and plant-water requirement through scouting method. This traditional method provides single point coverage which does not permit farmers to know the exert soil and plant conditions in addition to preventing them by monitoring trends in the production for effective crop management decisions. It was also observed that despite farmers’ effort to use different means for increased crop production in the country, occurrence of irregular and non-predictive rainfall pattern and sunshine hours continued to lower harvests of cassava, maize, melon and yam with at least 2.5% decline of harvests per annum . Apart from loss of produce during cultivation, about half of the food produced is never consumed due to inefficiencies in the harvesting of crops.

Studies showed – that the world’s population is expected to nearly double by 2050 while food supply is unlikely to follow the same pattern even by doubling the area under cultivation , due to the impact of climate change; there is need for continued crop production to meet the needs of the teaming population through the use of sensory technology. This means that in order to ensure high productivity, farmers must utilize different technologies in production process to provide arable crops like cassava, cowpea, maize yam and others. However, the knowledge of the Graphical User Interface of the sensor technology application is needed on the side of the farmers to monitor and control scenarios, and instruct commands for  decision on the farm. It was therefore based on this scenario that this study was initiated to identify the techniques in utilizing sensor technology so that the extension agents could use it as a training package for teaching farmers for precision crop production in the country;hence the study. The major purpose of this study was to identify techniques needed in utilizing remote sensor technology for precision crop production by farmers.

The result of the study in Table 1 revealed that in planning for utilization of sensor technology the farmers needed to acquire computer operation technique to collect route data to other sensors and back to external base station, determine the number of sensor devices required per hectare, determine the plant density, set up based information, configure and distribute sensor devises in the farm, connect sensor to any existing communication infrastructure , identify crop farm to be monitored and agro-based allied information, relevant personnel,determine records to keep and provide necessary fund among others. The findings of this study were in line with the findings of that the base station needs to be connected to an existing communications infrastructure or to the Internet where a user can have access to the reported data. The findings of the study were also in conformity with the findings of  who found out that configuration of different scenarios is necessary while found out that sensor nodes when appropriately configured collect and route data either to other sensors or back to an external base station.

Transgenic crops can be generated with the use of recombinant DNA techniques which alter the crop’s genetic makeup by manipulating the genome—either by introduction, deletion, substitution, or silencing of an individual gene or group of genes of interest. The functionality of transgenes has expanded with time. In the beginning,only traits that exhibited complete dominance, free of the interaction from the native plant genome or the environment, were targeted. For such traits, only one copy of the trait introduced into one of the inbred parents was required. Fortunately, some of these dominant traits such as insect resistance and herbicide tolerance provided solution to major production hurdles encountered by farmers in producing major crops of food and fiber.

Cultural practice modification offered by early transgenic varieties not only enhanced economic return to the farmer because of higher crop yields but also had multiplier effects of soil erosion prevention from reduced tillage,and reduction in environmental pollution from the residual herbicides and insecticides. Now, the scope of transgenic technology has expanded to include quantitative traits such as stress tolerance and yield improvement that necessitate the interaction of the introgressed genes with native genes engaged in the metabolic pathway for the phenotypic trait expression where environment may also influence considerably the final phenotypic expression.Stacking of introgressed genes in hybrids to create value combination of traits is also receiving considerable attention.

Genetic engineering is usually resorted when improvement through conventional breeding and mutagenesis have been exhausted. Such situations arise if a desired trait is not present in the crop germplasm,the trait has proven difficult or very time consuming to improve through conventional breeding, or there is a need to remove or switch off particular genes. Commercialization of first genetically engineered crop started back in 1996 and since then it has reached new heights in its application and wide adaptability to various sectors of modern agriculture. Since 1996 to 2013 there has been tremendous increase in the acreage of genetically engineered crops. Between 1996 and 2013 there has been more than 100 fold increase in the acreage of genetically engineered crops . The ability of genetic engineering to incorporate foreign traits into plants was first exhibited in 1970s.

Although approved earlier for limited sale, it was not until 1996 that the Monsanto Company got the approval to commercially market European corn borer  resistant corn , Colorado potato beetle  resistant potato , cotton bollworm complex resistant cotton , and a non-selective, broad spectrum herbicide glyphosate tolerant soybean  Merr. The genes for all insecticidal proteins, modifiedcry1Ab in corn , modified cry1Ac in cotton , and modified cry3Ab in potato  derived from commonly found soil bacteria—Bacillus thuringiensis.Glyphosate tolerant soybean was developed by incorporating abacterium glyphosate resistant EPSP  synthase gene . Each generation represents unique thrust areas for developing transgenic crops and has contributed to the present pool of commercially adopted transgenic crops .Modern agriculture has been quick to adopt the commercial first generation transgenic crops expressing herbicide tolerance and insect resistance since these effects were clearly visible in the crop production systems. The second generation transgenic crops were designed for product quality characteristics but they did not lived up to expectations, and no commercial crop with these specific characteristics is presently in the market. However, the first approved transgenic food, Calgene’s Flavr Savr tomato which reached the market in 1994, succeeded in delaying softening of the ripe fruit after harvesting but was a complete commercial failure and was withdrawn from the market in 1997 out of safety concerns.

The third generation of transgenic crops has been engineered for use as biofactories or living reactors in the production of pharmaceuticals and industrial chemicals and is often referred as “molecular farming”. The early and most cost-reward producing use of GE has been in the development of insecticide and pesticide resistance in field crops. A great deal of interest has currently been shown in incorporating tolerance to environmentals tresses in crop cultivars in order to stabilize the yield under fluctuating environmental conditions. In addition, as enhanced nutritive value of crop has gathered much interest to combat malnutrition in developing countries and to meet the food preference of naturalists, several transgenic cultivars with fortified nutritive values have been released. Some degree of success has also been accomplished in developing crops with chemical constituent of industrial value and the use of plants as hosts for pharmaceutical products. The transgenes currently employed in weed management confer resistance to crops allowing use of effective broad-spectrum herbicides. The gene characterizing bacterial enzyme conferring tolerance to glyphosate has been most widely used, however, transgenes conferring tolerance to bromoxynil,glufosinate,and sulfonylurea  are also registered .