The Origins, Implications, and Consequences of Yield‐Based Nitrogen Fertilizer Management

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.

Estimating Economically Optimal Levels of Nitrogen Fertilizer in No-Tillage Continuous Corn

“…This excessive use of fertilizer can be attributed in part to the common use of yield-based methods for nitrogen recommendations. These are part of the principal algorithms of most decision tool software being sold to farmers for fertilizer management (Rodriguez et al, 2019). The yield-based algorithms are based on achieving a specific corn yield goal such that if a farmer wants more corn bushels per acre, he needs to apply more nitrogen (Camberato, 2012).…”

Soil‐test biological activity with the flush of CO2: VI. Economics of optimized nitrogen inputs for corn

“…A standard N fertilizer recommendation system was assumed based on widespread acceptance of yield goal (Mg grain ha −1 ) × 21.4 (kg N Mg −1 grain) (1.2 lb N/bu grain) across numerous states in the United States (Rodriguez, Bullock, & Boerngen, 2019). This was referred to as the standard (STD) system.…”

An Assessment of the Site-Specific Nutrient Management (SSNM) Strategy for Irrigated Rice in Asia

“…Yield-goal-based recommendations are N fertilizer recommendations that are based on the farmer's "target yield" or the anticipated yield gain, i.e., "yield potential" or the yield that the farmer "hopes to achieve" on their field in a specific production unit, with additional adjustments in some areas for soil organic matter (SOM) or other soil characteristics [6,7]. The yield-goal-based approach makes economic sense if the crop response function takes on the von Liebig functional form, i.e., there is a kink in the function, so that input and output prices do not affect the (interior) solution to the profit maximization problem [8]. This reflects the "law of the minimum" [9], whereas crop growth is constrained by the level of the scarcest nutrient, exhibiting zero elasticity of factor substitution.…”