Rodolfo Bongiovanni

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.

A Spatial Econometric Approach to the Economics of Site-Specific Nitrogen Management in Corn Production

The objective of this study is to determine the potential for using spatial econometric analysis of combine yield monitor data to estimate the site-specific crop response functions. The specific case study is for site-specific nitrogen (N) application to corn production in Argentina. Spatial structure of the yield data is modeled with landscape variables, spatially autoregressive error and groupwise heteroskedasticity. Results suggest that N response differs by landscape position, and that site-specific application may be modestly profitable. Profitability depends on the model specification used, with all spatial models consistently indicating profitability, whereas the nonspatial models do not.

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

Use of Lorenz curves and Gini coefficients to assess yield inequality within paddocks

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 REGRESSION MODELS FOR YIELD MONITOR DATA: A CASE STUDY FROM ARGENTINA

14.38 ha−1 (+3.54%).

Economics of site-specific nitrogen management for protein content in wheat

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.