Based on quadratic response models, the University of Illinois recommends 22 kg fertilizer N Mg·' corn (Zea mays L.) grain yield, with adjustments for cropping history, manure, and planting date. Recent work suggests recommendations based on the quadratic model are excessive. This field study evaluated the quadratic-plus-plateau and quadratic models and the current recommendation for a 10-to 12-yr, two-location, two-crop-sequence data set with 5 N fertilizer rates in Illinois. For all location-sequences, we detected a systematic bias with the quadratic model as compared with tbe quadratic-plus-plateau model. The quadratic response model predicted a maximum grain yield of 3 to 6% larger and an optimal N fertilizer rate 5 to 60% larger than that predicted by the quadratic-plus plateau model and decreased profit from $0.61 to $17.12 ba-1 yr·'. Current University of Illinois recommendations differed by from -6 to + 104 kg N ha·• from the amount predicted by tbe quadratic-plus-plateau and resulted in a profit reduction of $0.01 to $31.42 ha·' yr·'. These analyses indicate that the quadratic-plus-plateau is preferable to the quadratic model for predicting N fertilizer requirements of corn. Economic analysis indicates that the consequences of using the quadratic model rather than quadratic-plus-plateau model can be large, but not in all cases. Furthermore, the general recommendation of 22 kg N Mg -• of expected grain yield minus rate adjustments is not appropriate in all cases. Site-specificity plays a large role in determining optimal N rates. Crop sequenceCom-soybean Continuous com Com-soybean-wheat Continuous com Com-soybean-wheat Continuous com Nitrogen fertilizer --kg N ha·'-

Not all fields, nor even portions of fields, have the same economically optimal corn plant density. However, until the recent introduction of precision farming, producers could not benefit from these accepted intrafield differences. This field study was conducted on 170 cooperating farmer fields throughout the Midwestern U.S. Corn Belt between 1987 and 1996 and consisted of over 42 000 individual experimental units. At each location, corn (Zea mays L.) was overplanted and thinned to 44 000 to 104 000 plants ha−1. The objective of our field research was to estimate the economic value, to the farmer, of variable rate seeding (VRS) as compared with uniform rate seeding (URS). We first estimated the correlation between field quality and economically optimal plant density. The economically optimal uniform plant density for the Midwest Corn Belt was 67 900 plants ha−1. For every tonne per hectare increase in site quality, as measured by yield potential, the predicted value of the site‐specific economically optimal plant density increased by approximately 1200 plants ha−1. We compared differences in revenues minus seed costs on four simulated fields. The value of VRS, ignoring the costs of VRS equipment and services, ranged from $12.83 ha−1 for farmers with VRS technology and full information to $0.15 ha−1 for farmers with VRS technology but only partial information. Profitable implementation of VRS will require detailed and expensive information regarding site characteristics, production inputs, and stochastic factors. Therefore, VRS will remain economically infeasible for most commercial corn growers until the cost of obtaining such information decreases considerably.

Specific recommendations for variable rate nitrogen (VRN) fertilization in corn (Zea mays L.) are required to realize the potential environmental and economic benefits of this technology. However, recommendations based on algorithms that consider the processes controlling crop response to nitrogen fertilizer (NF) within fields have not yet been developed. The objectives of this study were to develop site-specific corn yield production functions for VRN fertilization and to determine the site-specific variables controlling corn response to NF. The experiments were conducted on eight commercial production fields. Fields were divided into 13-20 sections composed of five plots. Each plot received one NF rate. Sitespecific variables included primary and secondary terrain attributes, and the Illinois Soil Nitrogen Test (ISNT). Nitrogen fertilizer significantly increased corn yield and it interacted with at least one site-specific variable. The ISNT was the site-specific variable that interacted with NF in most fields where the CV of ISNT was larger than 10%. The parameter estimates indicate that ISNT had a positive effect on corn yield and that it reduced the response to NF. Terrain attributes also affected corn yield and its response to NF. In general, parameter estimates indicated that well drained areas (i.e. small specific catchment area, moderate slopes) had higher yields and responded less to NF than areas where water is expected to accumulate. These results indicate that terrain attributes as surrogates for soil water content and the ISNT as a measure of soil mineralizable nitrogen are site-specific characteristics that affect corn yield and its response to NF.

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Political preference function (PPF) studies assume that policy‐making can be described by a mathematical problem in which a rational government maximizes a function of interest groups' welfares. The purpose of my paper is to explain in‐depth ramifications of PPF study assumptions and methodology. PPF studies that attempt to measure interest group political power do so by measuring marginal rates of transformation along a Pareto frontier. One can estimate political power with a PPF only if observed policies are Pareto‐efficient, which may depend on the assumed number of interest groups and policy instruments. Marginal rates of transformation need not reveal anything meaningful about interest group political power, and may incorrectly measure the direction of transfers.

The Data-Intensive Farm Management (DIFM) project works with participating farmers, using precision technology to inexpensively design and run randomized agronomic field trials on whole commercial farm fields, to provide data-based, site-specific farm input management guidance, thus providing economic and environmental benefits. This article lays out a conceptual framework used by the multidisciplinary DIFM research team to facilitate collaboration and then presents details of DIFM's procedures for what it calls on-farm precision experimentation (OFPE), which includes field trial design and implementation, data generation, processing, and management, and analysis. It is argued that DIFM's data and the agricultural "Big Data" currently being collected with remote and proximal sensors are complementary; that is, more of either increases the value of the other. In 2019, DIFM and affiliates conducted over 120 trials, ranging from 10 to 100 ha in size, on maize, wheat, soybeans, cotton, and barley in eight US states, Argentina, Brazil, and South Africa. The DIFM project is developing cyberinfrastructure to "scale up" its activities, to permit researchers and crop consultants worldwide to work with farmers to conduct trials, then process and manage the data. In Addition, DIFM is in the early stages of developing a software system for semiautomatic data analytics, and a cloud-based farm management aid, the purpose of which is to facilitate conversations between agronomists and farmers about implementing data-driven input management decisions. The proposed framework allows researchers, agronomists, and farmers to carry out on-farm precision experimentation using novel digital tools.

At this early stage, it seems evident that because soybeans do not cross-pollinate, non-GM soybean seed is already being delivered at levels of purity high enough to not preclude, a priori, the final delivery of soybeans that meet the 1% contamination tolerance level of the EU. Cross-pollination makes keeping non-GM corn seed sufficiently pure more difficult. Still, at this early stage it is not clear that any additional steps need to be taken to increase its purity level, either. If purity levels do need to be increased, it is an open question whether it would be less costly to organize and enforce agreements among neighboring farmers to increase the spatial and temporal isolation of seed-producing corn, or STS soybean foundation, parent, or commercial seed, the maturing plants may be sprayed with chlorimuron and thifensulfuron, thus stunting the growth of any glyphosate-resistant plants that happen to be growing in the STS field, and making it less likely that any seeds coming out of that field will be genetically modified (Outtrim, 2000).

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