Understanding Corn Yield Potential in Different Environments

Authors

Achim R. Dobermann, University of Nebraska-LincolnFollow
Timothy J. Arkebauer, University of Nebraska-LincolnFollow
Kenneth G. Cassman, University of Nebraska-LincolnFollow
Rhae A. Drijber, University of Nebraska-LincolnFollow
John L. Lindquist, University of Nebraska-LincolnFollow
James E. Specht, University of Nebraska-LincolnFollow
Daniel T. Walters, University of Nebraska-LincolnFollow
Haishun Yang, University of Nebraska-LincolnFollow
Daniel N. Miller, University of Nebraska-LincolnFollow
Darren L. Binder, University of Nebraska-LincolnFollow
Gregory J. Teichmeier, University of Nebraska-LincolnFollow
Richard B. Ferguson, University of Nebraska-LincolnFollow
Charles S. Wortmann, University of Nebraska-LincolnFollow

ORCID IDs

Charles S. Wortmann

Date of this Version

2003

Comments

2003 Annual Report to the Fluid Fertilizer Foundation on grant “Yield potential and optimal soil productivity in irrigated corn systems of the north-central USA”

Abstract

The UNL research program on Ecological intensification of irrigated maize-based cropping systems aims to (i) improve understanding of the yield potential of corn and soybean and how it is affected by management, (ii) develop a scientific basis for evaluating yield potential at different locations, (iii) develop practical technologies for managing intensive cropping systems at 70-80% of the yield potential, and (iv) conduct integrated assessment of productivity, profitability, input use efficiency, soil carbon sequestration, energy and carbon budgets, and trace gas emissions. Results of this work have been reported earlier (Arkebauer et al., 2001; Dobermann et al., 2002). In this paper we discuss examples of progress made in 2002, focusing on obtaining additional data sets from high-yield environments and on using crop simulation modeling for understanding yield potential.

Yield potential (or potential crop production, Ymax) can be defined as the maximum yield that can be achieved in a given environment for a certain plant species. Thus, yield potential refers to a situation of unlimited water and nutrient supply, where potential plant production is solely determined by growth-defining factors such as genetic characteristics, solar radiation, temperature, and CO₂ concentration (van Ittersum et al., 2003). Management of Ymax is only possible through breeding and tactical decisions such as selecting the right cultivar, sowing date, and plant density in relation to variation in Ymax that is due to the seasonal pattern of radiation and temperature.

Approaches used to quantify Ymax include (i) theoretical calculations from components of yield and radiation use efficiency, (ii) measurements in well-controlled, small-scale experiments in which elimination of biotic and abiotic stresses (water, nutrients, pests) is attempted, and (iii) estimation by crop simulation models. Much debate is ongoing about what the yield potential of corn is. Highest corn yields have been reported in yield contests, and the winning yields have been used as a proxy for estimating corn yield potential and yield potential trends. For example, Waggoner (1994) and Evans (1993) speculate that there is no limit to corn yield potential in the foreseeable future based on the linear increase in winning yields in yield contests and the increasing size of the yield gap between average farm yields and contest-winning yields. In contrast, Tollenaar and Lee (2002) argue there has been little improvement in corn yield potential under optimal growth conditions and that most of the improvement in yield has resulted from increased stress resistance. The paucity of data from well-designed field experiments in which yields approach those reported in the yield contests makes it difficult to test these conflicting hypotheses (Duvick and Cassman, 1999). It also makes it difficult for crop scientists to validate the ability of corn growth models to simulate potential yield.