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Many mechanized crop producers and agribusinesses are fascinated with precision agriculture technology, but adoption has lagged behind the expectations. Among the reasons for slow adoption of precision agriculture technology is that initial users focused excessively on in‐field benefits from variable‐rate fertilizer application using regional average fertilizer recommendations. This article illustrates how greater use of site‐specific crop response information can improve variable rate input application recommendations. Precision agriculture is spatial information technology applied to agriculture. The technologies include global position systems (GPS), geographic information systems (GIS), yield monitoring sensors, and computer controlled within‐field variable rate application (VRA) equipment. Experimentation with these technologies is occurring everywhere there is large scale mechanized agriculture. Commercial use has been greatest in the US, where 43% of farm retailers offered VRA services in 2001. Except for certain high‐value crops like sugar beet, farmer adoption of VRA has been modest. The farm level profitability of VRA continues to be questionable for bulk commodity crops. The theoretical model and illustration presented here suggest that VRA fertilization has not yet reached its profitability potential. Most VKA field trials to date have relied upon existing state‐wide or regional input rate recommendations. Unobserved soil characteristics can potentially interact with an input to make its effect on yield vary site‐specifically within fields. Failure to use site‐specific response functions for VRA applications may lead to a misallocation of inputs just as great as that which results from using uniform applications instead of VRA. Agricultural economists have a long history of estimating output response to input applications. Several have started to develop tools to estimate site‐specific responses from yield monitor and other precision agriculture data. Likewise, agricultural economists have developed an important body of research results on information value based on managing variability—typically in temporal settings. With these tools, a major potential exists to develop further benefits from precision agriculture technologies that permit truly spatially tailored input applications.

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.

Consumer preference information is essential to targeting research. This paper reports an effort of a multi‐disciplinary team to measure the market value of cowpea characteristics. Five samples were purchased once per month in seven markets in Ghana and Cameroon starting in September 1996. In the market, price and vendor characteristics were noted. In the laboratory, size of grains, testa color, testa texture, eye color and damage levels were recorded. A hedonic pricing regression model was used. Results indicate that grain size is the most important characteristic. Consumers seem more sensitive to bruchid (Callosobruchus maculates) damage than hypothesized. Cowpeas with white testa command a clear premium only in one of the Ghanaian markets. In Ghana, black eyes sell at a premium, but in Cameroon black eyes are discounted. In general, this study indicates that quality characteristics are very important in West African food markets. Even low income consumers are willing to pay a premium for products that match their preferences, and they are vigilant in identifying products that do not meet their standards. Purchasing samples on a regular basis and hedonic pricing offers a practical way for biological scientists and economists to work together to measure these consumer preferences.

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In the United States average adoption rates have increased for precision agriculture (PA) technologies used to produce many field crops. PA makes use of information collected on the farm to target site-specific, intensive management of farm production. The United States Department of Agriculture (USDA) Agricultural Resource Management Survey (ARMS) allows close examination of regional patterns of adoption, and how crop types and region interact with differences in farm sizes and soil productivity variability to influence adoption rates. The most common PA technologies are guidance systems that use global positioning systems (GPS) to steer tractors and other farm equipment. Remote sensing, soil mapping, and yield mapping all use GPS to geolocate data and create maps used to guide farm management decision. Variable rate input-application technologies (VRT) make use of remote images, soil tests, yields maps and other sources of information to apply different, more precise levels of inputs in farmer's fields. GPS guided VRT fertilization was introduced in the early 1990s and increased slowly over the last three decades. The ARMS data for winter wheat (2017), corn (2016) and soybeans (2012) showed use of VRT seeding and pesticide applications growing rapidly. The data indicated that PA technology was being used on farms across all sizes and all regions, with adoption occurring more rapidly on larger farms. VRT use on soybean farms was highest in areas of higher soil variability.

Economic losses to stored grain can potentially come from both quantity losses and quality losses in the form of price discounts for damage from insects and mold. This article uses choice experiments conducted with physical samples of maize to estimate discounts for damaged grain among maize traders in Malawi. Using the Equality Constrained Latent Class method to correct for non-attendance to the price attribute, we find that traders place a statistically and economically significant discount on insect-damaged maize. We estimate that a 1% increase in maize damage reduces the price of maize by 2.8% to 3.6%, depending on damage level. We discuss the implications of these results for farmers' incentives to adopt improved storage technologies that can reduce post-harvest losses.

Jomini, P.A., Deuson, RR., Lowenberg-DeBoer, J. and Bationo, A., 1991. Modelling stochastic crop response to fertilization when carry-over matters. Agric. Econ.,