On‐Farm Estimation of Nutrient Requirements for Spring Corn in North China

Published in Agron. J. 104:1436–1442 (2012) Posted online 1 Aug. 2012 doi:10.2134/agronj2012.0125 Copyright © 2012 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. I corn production is required to meet the demand for food, biofuel, and fiber due to the rapidly expanding world population, especially in developing countries such as China and India (Wang et al., 2009; Chen et al., 2011). Pursuing high grain yields in China has been the top priority in policy and practices through the use of high-yield crop varieties, chemical fertilizers, irrigation, and mechanization. For example, in the past 10 yr (2000–2009), China’s consumption has accounted for 54% of the global increase in chemical fertilizer (27 Tg) (FAO, 2010). The recent increase in nutrient inputs have not increased grain yields in China; in contrast, it has exacerbated many environmental problems such as the release of greenhouse gases, eutrophication, and soil quality degradation (Cassman et al., 2003; Edgerton, 2009; Vitousek et al., 2009; Guo et al., 2010; Le et al., 2010; Chen et al., 2011). The imbalances in nutrient management practices were mainly caused by a wide variation in plant nutrient requirements and a lack of tools to estimate the nutrient requirements and obtain a targeted yield (Witt et al., 1999; Setiyono et al., 2010). Determination of quantitative crop nutrient requirements would optimize crop nutrient management and better serve policymakers. Previous studies have quantified crop nutrient requirements for various environmental and management practices using nutrient recommendation algorithms to identify the nutrient uptake-yield relationships (Reid et al., 2002; Heckman et al., 2003; Cui et al., 2008). Some studies have found high variability in plant nutrient requirements with regard to nutrient supplies, grain yield, genotype, and environment (Scharf and Alley, 1994; Fiez et al., 1995; Doberman and Cassman, 2002; Cui et al., 2008; Pettigrew, 2008, Hou et al., 2012). For example, excessive nutrients may decrease internal plant nutrient efficiency due to nutrient accumulation (Cassman et al., 2003). Nutrients such as N, P, and K significantly affect plant nutrient requirements. For example, increased K fertilizer significantly improves agronomic efficiency in corn by inducing high N uptake (Zhang et al., 2010). However, the wide variation in plant nutrient requirements has limited the optimization of current nutrient management strategies for crops. Most traditional nutrient recommendation algorithms have estimated plant nutrient-yield relationships based on fertilizer response trials conducted at a research station. However, these trials generally did not examine how interactions among N, P, and K affect plant nutrient internal efficiencies (IEs, kg grain per kg of nutrient in plant dry matter) and did not allow for differentiation according to different yield targets. These problems could be resolved with a generic approach developed by Janssen et al. (1990) and Smaling and Janssen (1993) using the ils QUEFTS model. The QUEFTS model was originally developed to quantify the yield as a function of N, P, and K from soil and fertilizer (Janssen et al., 1990). Then, the model was further modified by Smaling and Janssen (1993) and Witt et al. (1999) to develop relationships between grain yield and nutrient uptake requirements and to estimate plant nutrient uptake requirements for a given yield level. The relationship ABSTRACT Estimating the nutrient requirements of corn (Zea mays L.) is crucial to facilitate fertilizer management practices and agricultural policies. Our understanding of crop nutrient uptake requirements is limited by traditional site-specific nutrient recommendation algorithms. A database composed of 1065 on-farm observations collected during 2006 to 2009 in North China was used to assess the reciprocal internal efficiencies (RIEs, kg of nutrients in plant dry matter per Mg of grain) calculated by the QUantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model under different nutrient supply conditions. We found that nutrient supply conditions have a pronounced effect on RIEs. In the absence of nutrients, the RIE values were higher than with other nutrient-supply treatments, which reflect severe nutrient deficiencies in plants. In the presence of excessive nutrients, the RIEs increased without a corresponding increase in grain yield. Hence, using the data derived from optimal nutrient supply plots with current corn hybrids, the RIEs simulated by QUEFTS, were 15.3 kg N, 2.9 kg P, and 8.3 kg K per Mg at 60 to 70% of the yield potential. These values maximized the nutrient yield-producing uptake efficiency for spring corn in North China.