No abstract

Core Ideas We evaluate an old and widely accepted yield‐based N fertilizer management algorithm. The algorithm, in itself, was a “ballpark” recommendation that served an important public function. The overconfidence in this algorithm may have harmed agriculture in a number of ways. The algorithm’s empirical derivation was seriously flawed. We examine the origins, implications, and consequences of yield‐based N fertilizer management. Yield‐based algorithms have dominated N fertilizer management of corn (Zea mays) in the United States for almost 50 yr, and similar algorithms have been used all over the world to make fertilizer recommendations for other crops. Beginning in the mid‐1990s, empirical research started to show that yield‐based rules‐of‐thumb in general are not a useful guide to fertilizer management. Yet yield‐based methods continue to be widely used, and are part of the principal algorithms of nearly all current “decision tool” software being sold to farmers for N management. We present details of the theoretical and empirical origins of yield‐based management algorithms, which were introduced by George Stanford (1966, 1973) as a way to make N fertilizer management less reliant on data. We show that Stanford’s derivation of his “1.2 Rule” was based on very little data, questionable data omissions, and negligible and faulty statistical analysis. We argue that, nonetheless, researchers, outreach personnel, and private‐sector crop management consultants were obliged to give some kind of N management guidance to farmers. Since data generation is costly, it is understandable that a broad, “ball park” rule‐of‐thumb was developed, loosely based on agronomic principles. We conclude by suggesting that technology changes now allow for exciting new possibilities in data‐intensive fertilizer management research, which may lead to more efficient N management possibilities in the near future.

We determine the production risk effects and welfare implications of single-trait Bt corn adoption in the Philippines. We use a stochastic production function estimation approach that allows for examining the skewness effects of Bt within a damage abatement specification. Our results indicate that Bt corn has a statistically significant yield increasing, risk-increasing (i.e., variance-increasing) and downside risk-reducing (i.e., skewness-increasing) effects. Based on risk premium, certainty equivalent, and loss probability welfare measures, Bt corn farmers in the Philippines are better-off (in absolute terms) relative to non-Bt farmers given Bt corn's dominant yield increasing effect and downside risk-reducing effect.

The site-specific nutrient management (SSNM) strategy provides guidelines for effective nitrogen, phosphorus and potassium management to help farmers make better decisions on fertilizer input and output levels in rice (Oryza sativa) production. The SSNM fertilizer recommendations are based on the yield goal approach, which has been frequently cited in empirical studies. This study evaluates the assumptions underlying the SSNM strategy for rice in the top rice-producing countries around the world, including India, Indonesia, the Philippines, Thailand, and Vietnam. Using a generalized quadratic production function, I explore whether major nutrients are substitutes as inputs and if there are complementarities between inorganic fertilizer and soil organic matter (SOM). The results suggest the relationships among major nutrients vary across sites—some inputs are complements, some are substitutes, and some are independent. The SOM also significantly affects the nitrogen fertilizer uptake. I conclude by suggesting that the SSNM strategy can be made to be more adaptive to farmer’s fields if these relationships are accounted for in the fertilizer recommendation algorithm.

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