James Lowenberg-DeBoer

Precision Agriculture and Sustainability

Precision Agriculture (PA) can help in managing crop production inputs in an environmentally friendly way. By using site-specific knowledge, PA can target rates of fertilizer, seed and chemicals for soil and other conditions. PA substitutes information and knowledge for physical inputs. A literature review indicates PA can contribute in many ways to long-term sustainability of production agriculture, confirming the intuitive idea that PA should reduce environmental loading by applying fertilizers and pesticides only where they are needed, and when they are needed. Precision agriculture benefits to the environment come from more targeted use of inputs that reduce losses from excess applications and from reduction of losses due to nutrient imbalances, weed escapes, insect damage, etc. Other benefits include a reduction in pesticide resistance development. One limitation of the papers reviewed is that only a few actually measured directly environmental indices, such as leaching with the use of soil sensors. Most of them estimated indirectly the environmental benefits by measuring the reduced chemical loading. Results from an on-farm trial in Argentina provide an example of how site-specific information and variable rate application could be used in maintaining profitability while reducing N applications. Results of the sensitivity analysis show that PA is a modestly more profitable alternative than whole field management, for a wide range of restrictions on N application levels. These restrictions might be government regulations or the landowners understanding of environmental stewardship. In the example, variable rate of N maintains farm profitability even when nitrogen is restricted to less than half of the recommended uniform rate.

Farmer Experience with Precision Agriculture in Denmark and the US Eastern Corn Belt

Abstract.Two mail surveys were carried out in Denmark and the Eastern Corn Belt, USA in 2002. Questionnaires were sent to 580 farmers who had used precision agriculture (PA) and 198 responses were received. The surveys focused on the current status of use of PA in both countries, including: PA practices, equipment and software, Internet and e-mail use, information sources for PA, satisfaction level from service providers, data handling, interpretation, storage and ownership, value of data for decision making, changes in management practices, desired information and services, and the next planned step in the practice of PA. The survey results showed more similarities in practicing PA between the two countries than differences. Time requirement and high cost of data handling were cited as the main problems. Survey respondents found soil maps to be more valuable than yield maps in management decisions. About 80% of the respondents would like to store the PA data themselves. The majority of the respondents indicated that they have changed their management practices due to PA, but not substantially. Some 90 of the respondents used the Internet and e-mail for agricultural purposes, but only a small number for PA websites.

Economics of Variable Rate Lime in Indiana

In Indiana, variable rate application (VRA) of lime is often considered a good place to start site-specific management (SSM). This is because soil pH is one of the most variable of manageable soil characteristics in the state, the availability of essential nutrients is closely related to soil pH, and because spreaders can be retrofitted relatively inexpensively to do VRA. The objective of this study is to evaluate the profitability of VRA for lime as a stand-alone activity. The methodology involves a spreadsheet model using corn and soybean pH response functions estimated with small plot data. The overall results indicate increased annual returns to corn and soybean production with site-specific pH management strategies. On average, SSM with agronomic recommendations provides an increased annual return of

A Comparison of Four Spatial Regression Models for Yield Monitor Data: A Case Study from Argentina

7.24 per hectare (ha) (+1.78%). SSM with the economic decision rule provides an average increase in annual return of

Risk Sharing versus Low-Cost Credit Systems for International Development

19.55 ha−1 (+4.82%). Information strategy, which uses site-specific information to determine the economically optimal uniform rate of lime, provides an average increase in annual return of

Spatial Analysis of Yield Monitor Data: Case Studies of On-farm Trials and Farm Management Decision-making

14.38 ha−1 (+3.54%).

Stochastic dominance analysis of on-farm-trial data: The riskiness of alternative phosphate sources in Burkina Faso

The gap between data analysis and site-specific recommendations has been identified as one of the key constraints on widespread adoption of precision agriculture technology. This disparity is in part due to the fact that analytical techniques available to understand crop GIS layers have lagged behind development of data gathering and storage technologies. Yield monitor, sensor and other spatially dense agronomic data is often autocorrelated, and this dependence among neighboring observations violates the assumptions of classical statistical analysis. Consequently, reliability of estimates may be compromised. Spatial regression analysis is one way to more fully exploit the information contained in spatially dense data. Spatial regression techniques can also adjust for bias and inefficiency caused by spatial autocorrelation. The objective of this paper is to compare four spatial regression methods that explicitly incorporate spatial correlation in the economic analysis of variable rate technology: (1) a regression approach adopted from the spatial econometric literature; (2) a polynomial trend regression approach; (3) a classical nearest neighbor analysis; and (4) a geostatistical approach. The data used in the analysis is from a variable rate nitrogen trial in the Córdoba Province, Argentina, 1999. The spatial regression approaches offered stronger statistical evidence of spatial heterogeneity of corn yield response to nitrogen than ordinary least squares. The spatial econometric analysis can be implemented on relatively small data sets that do not have enough observations for estimation of the semivariogram required by geostatistics. The nearest neighbor and polynomial trend analyses can be implemented with ordinary least squares routines that are available in GIS software. The main result of this study is that conclusions drawn from marginal analyses of this variable rate nitrogen trial were similar for each of the spatial regression models, although the assumptions about spatial process in each model are quite different.

A Model for Agro-Economic Analysis of Soil pH Mapping

Low-cost credit programs in developing countries have failed to achieve agricultural technology adoption goals. This research attributes the failure to the inability of poor farmers to bear the combined business and financial risks posed by adopting new technologies and develops proposals for the design of credit programs that reduce these risks. Agronomic and socioeconomic data are combined through simulation and mathematical programming to analyze problems of decision making under risk for developing countries. The results will assist in the design of new rural financial institutions conducive to the adoption of new production technologies by subsistence farmers.

MANAGING PHOSPHOROUS SOIL DYNAMICS OVER SPACE AND TIME

A 3-year case study was undertaken of how North American farmers use yield monitors for on-farm trials in farm management decision making. Case study methods were used because relatively few farmers quantitatively analyze yield monitor data. At this early research stage, insufficient farm management information about the data was available to ask the right questions in a large-scale survey. In addition to the formal case study of farmers experienced at using yield monitors to collect on-farm trial data, the study evaluated the effect of yield monitor data quality on farm decisions. Two levels of yield data quality included standard output where the default settings of farm-level mapping software were accepted and where filtering of the data was undertaken. Results indicated that yield data quality affects farm management decisions. In addition, farmers receiving a spatial analysis of their on-farm trial data tended to use split-field designs instead of replicated split-planter designs. They were also more confident in their decisions than before participation in the spatial analysis project, and made decisions more quickly.

The influence of cowpea characteristics on cowpea prices in Senegal

Stochastic dominance was used to determine the risk characteristics of phosphate fertilization of millet, sorghum and maize with commercial NPK fertilizer, rock phosphate and partially acidulated rock phosphate in Burkina Faso. On-farm-trial data from 1989, 1990 and 1991 in three rainfall zones was used. The analysis shows that among the four treatments tested, commercial NPK fertilizer has the most desirable risk characteristics. It is acceptable to risk averse decision makers for all three crops in all rainfall zones. The no-fertilizer control is dominated by the fertilizer treatments. The rock phosphate treatments have higher yields and in certain cases higher returns than the no-fertilizer control, but those benefits are less sure than for the soluble commercial fertilizer. The distributions of cash returns to rock phosphate treatments are rarely significantly different from those of the control. Rock phosphate treatments never dominate the commercial fertilizer treatment. If farmers have a choice between commercial fertilizer, rock phosphate and partially acidulated rock phosphate, at current prices most of those who use fertilizer would choose the soluble commercial product. If the availability of commercial fertilizer were limited (e.g. by lack of hard currency), some farmers would use rock phosphate-especially the partially acidulated product. Stochastic dominance permitted a timely and detailed analysis of risk inherent in phosphate fertilizer alternatives. Because on-farm-trails involve a modest number of alternatives, pairwise stochastic dominance comparisons are feasible. The stochastic dominance analysis permits researchers to communicate to extension staff and policymakers not only the degree of risk, but also something about the characteristics of the crop response that contribute to risk. The key to effective use of stochastic dominance is careful study of the distributions and understanding why a technology is dominated or is potentially acceptable to risk averse decisionmakers.