INFORMATION AND THE ADOPTION OF PRECISION FARMING TECHNOLOGIES

Precision farming technologies have been commercially available since the early 1990s, but the pace of adoption among U.S. farmers has been modest. This study examines the relationship between the adoption of diagnostic and application techniques of precision farming and sources of information available to farmers about precision farming. The model used in the analysis accounts for sources of self-selection in the adoption process that could bias the results. Results indicate interpersonal information sources have increased adoption relative to information from the mass media, and the private sector has been the driving force behind the diffusion of precision farming. Information from crop consultants and input suppliers has had the greatest impact on the adoption of precision farming technologies. These sources likely provide the greatest technical expertise about precision farming, and thus are better equipped to ease the significant human capital requirement of precision farming technologies.information sources, logit analysis, precision farming, self-selection bias, technology adoption, Farm Management, Research and Development/Tech Change/Emerging Technologies,

Precision farming technology, adoption decisions and productivity of vegetables in resource-poor environments

‘Precision Farming’ or ‘Precision Agriculture’ aims at increasing productivity, decreasing production costs and minimizing the environmental impact of farming. In this context, the present study has been undertaken to understand the impact of precision farming on resource-poor regions and underprivileged farmers. Specifically, the study has looked into productivity, income, employment, and adoption behaviour of technology in agriculture. The study, conducted in the Dharmapuri district, has collected data on precision and non-precision farmings through the interview schedule during the year 2007. Sources of the productivity difference between the precision and conventional farmings have been identified by decomposing the productivity change. Financial impact of adoption has been studied through a two-stage econometric model. The first stage of the model consists of an adoption decision model that describes the factors which influence the likelihood of adopting precision farming. Results of first stage have provided input for the second stage of the model, which has been used to estimate the impact of precision farming on farm financial performance. The study has revealed that adoption of precision farming has led to 80 per cent increase in yield in tomato and 34 per cent in brinjal production. Increase in gross margin has been found as 165 and 67 per cent, respectively in tomato and brinjal farming. The contribution of technology for higher yield in precision farming has been 33.71 per cent and 20.48 per cent, respectively in tomato and brinjal production. The elasticity of 0.39 for the adoption in tomato and 0.28 in brinjal has indicated that as the probability of adoption increases by 10 per cent, net return increases by 39 per cent and 28 per cent in tomato and brinjal cultivation. Lack of finance and credit facilities have been identified as the major constrains in nonadoption of precision farming. The study has suggested that providing of subsidies for water-soluble fertilizers and pump-sets will increase adoption of precision farming.Crop Production/Industries,

Rights in a Cloud of Dust: The Value and Qualities of Farm Data and How Its Property Rights Should Be Viewed Moving Forward

Historically, technology growth has been slower in agriculture than other industries. However, a rising demand for food and an increase in efficient farm practices has changed this, leading to a rise in precision farming technologies. Now, entities that provide services or information to farmers need precision farming technologies to compete, and more farmers are adopting precision farming technologies. These technologies help farmers, but questions still remain about ownership rights in the data that farmers create

Knowledge of Precision Farming Beneficiaries

Precision Farming is one of the many advanced farming practices that make production more efficient by better resource management and reducing wastage. TN-IAMWARM is a world bank funded project aims to improve the farm productivity and income through better water management. The present study was carried out in Kambainallur sub basin of Dharmapuri district with 120 TN-IAMWARM beneficiaries as respondents. The result indicated that more than three fourth (76.67 %) of the respondents had high level of knowledge on precision farming technologies which was made possible by the implementation of TN-IAMWARM project. The study further revealed that educational status, occupational status and exposure to agricultural messages had a positive and significant contribution to the knowledge level of the respondents at 0.01 level of probability whereas experience in precision farming and social participation had a positive and significant contribution at 0.05 level of probability

PROFITABILITY OF VARIABLE RATE PHOSPHORUS IN A TWO CROP ROTATION

In the Midwest, the adoption of precision farming technologies began in the early 1990s. Research has produced profiles of early adopters, evaluated adoption trends and has identified factors that influence the adoption and profitability of precision farming. Importantly, this information is available to producers, who are interested in precision farming issues. In addition, the Midwest regional agricultural industry, strong promoters of precision farming technologies, has gained the confidence of farmers who now rely on them heavily for information on farming technologies. Precision farming in Arkansas, however, is still in its infancy. Adoption levels lag far behind those in the Midwest. Two reasons for this lag have been offered. First, some suggest that much of what is believed about the technologies in the state is based on hearsay or the results of small single farm case study analyses. Because these beliefs have not been rigorously substantiated with extensive empirical evidence it has not been possible to truly assess the status of adoption, to predict potential adoption trends, or to adequately advise farmers in a decision to include precision farming in their farm management plan. Second, others suggest that agricultural industry has not taken an active role in the promotion and sale of precision farming equipment and services. Without local availability, all the research in the world will not lead to adoption of technology in the state. The objective of this paper is to provide critical information to Arkansas agricultural producers, industry and extension with answers regarding 1) the current status of precision farming 2) the amount, source and effectiveness of precision farming promotion and 3) the potential future of precision farming in Arkansas. In the Spring of 1999, three groups, early adopters of precision farming technologies (EA), Cooperative Extension Service personnel (CES) and agricultural industry personnel (AI), were surveyed to ascertain the realities and perceptions of precision farming in Arkansas. The surveys included questions related to characteristics of early adopters, factors encouraging and hindering adoption, and the roles of CES and AI in the promotion of precision farming within Arkansas. The survey response rate was over 60 percent. To build profiles of Arkansas EA to compare responses regarding sources of precision farming information across all three groups three statistical tools were used to test hypotheses regarding factors which influence adoption. The surveys revealed that Arkansas EA are young, educated, computer using, experienced farmers controlling relatively large farms predominantly devoted to rice and soybean. These farmers currently employ yield and soil mapping, as well as VRT and GIS technologies in their operations. While many reasons (such as decreased costs, improved yields, and improved management capabilities) have been cited as factors that can encourage adoption, there are still any number of reasons why many Arkansas farmers have not yet adopted these technologies, including, technical difficulties, expense and unproven profitability. In addition, AI representatives see themselves as promoters of precision farming technologies in Arkansas while EA have cited instances of a lack of available equipment and also stated that they turn to CES rather than AI for farming information because they believe this is an unbiased source of information. The authors conclude that both reasons offered for the lag in adoption are likely and hope that these insights provide both the CES and AI representatives with information to help them focus their research and outreach activities so that more Arkansas producers can make informed decisions about precision farming.Crop Production/Industries,

Factors Influencing the Selection of Precision Farming Information Sources by Cotton Producers

Precision farming information demanded by cotton producers is provided by various suppliers, including consultants, farm input dealerships, University Extension systems, and media sources. Factors associated with the decisions to select among information sources to search for precision farming information are analyzed using a multivariate probit regression accounting for correlation among the different selection decisions. Factors influencing these decisions are age, education, and income. These findings should be valuable to precision farming information providers who may be able to better meet their target clientele needs.Extension, information-source-use decisions, media, multivariate probit, precision agriculture technologies, private sources, Farm Management, Teaching/Communication/Extension/Profession,

Factors Influencing Cotton Farmers’ Perceptions about the Importance of Information Sources in Precision Farming Decisions

Information generated by precision farming technologies is of particular importance to producers. Precision farming technologies implies the ability to improve the management of production factors using site-specific information. This study examines factors influencing cotton farmers’ perceptions about the importance of crop consultants, farm input dealerships, Extension, other farmers, trade shows, the Internet and printed news/media for making precision farming decisions using a rank ordered logit model (ROLM). Results suggest that age, land tenure, income, percentage of income from farming, and location may affect farmers’ perceptions about the importance of different information sources when making decisions about precision farming technologies. Results suggest that regardless of farmer/farm business characteristics other farmers (OF) is one of the most important information sources when making precision farming decisions. Findings suggest that high income producers are more likely to prefer crop consultants, University/Extension, trade shows, and the Internet over OF as a source of information when making decisions about precision farming technologies. Findings also suggest that researchers need to be very careful when designing questions that ask respondents to rank alternatives so that they guarantee that individuals with different skills are able to precisely understand what is being asked. Decreasing the number of alternatives respondents must consider may be one strategy to reduce the complexity of ranking questions to minimize the probability of the respondents leaving alternatives unranked or ranking them randomly.Information-source preferences, Rank Ordered Logit Model, Precision Farming, Production Economics, Research Methods/ Statistical Methods, Q16, C25,

QUANTIFYING THE DIFFERENCES IN MANAGEMENT GOALS AND TECHNOLOGY CHOICE IN PEANUT PRODUCTION

Precision farming and whole-field farming are compared with respect to yields, net present value of returns above nitrogen and water costs (NPVR), and nitrogen application rates to determine the differences in management practices. Precision farming yields, NPVR, and nitrogen application levels are then compared under yield maximizing verses profit maximizing strategies. The results quantify the gains from technology and management goals of peanut producers and suggest the technology effect is greater than the management effect.Crop Production/Industries,

Precision Farming by Cotton Producers in Eleven Southern States: Results from the 2005 Southern Precision Farming Survey

Precision Farming by Cotton Producers in Eleven Southern States: Results from the 2005 Southern Precision Farming Surveycotton, precision farming, survey, Agribusiness, Farm Management, Production Economics, Research and Development/Tech Change/Emerging Technologies,

RISK MANAGEMENT POTENTIAL OF PRECISION FARMING TECHNOLOGIES

Initial ideas on risk management uses of precision agricultural technology focused on site-specific treatment of problem areas to reduce the probability of low yields and returns. Recent discussions deal with sensor and remote-sensing information to improve marketing and "as applied maps" as trace-back mechanisms to manage liability. A theoretical model is presented that suggests that there are plausible circumstances under which precision farming can reduce temporal yield variability. Empirical evidence from an on-farm trial of site-specific P&K management in the Eastern Cornbelt supports the hypothesis that precision farming can have risk-reducing benefits.food safety, GIS, GPS, crop insurance, marketing, precision farming, site specific management, risk, Research and Development/Tech Change/Emerging Technologies, Risk and Uncertainty,