A regional analysis model of maize kernel moisture. (21st February 2021)

• Record Type: Journal Article
• Authors: Li, Lulu; Ming, Bo; Gao, Shang; Wang, Keru; Hou, Peng; Jin, Xiuliang; Chu, Zhendong; Zhang, Wanxu; Huang, Zhaofu; Li, Hongyan; Zhou, Xianlin; Bai, Shijie; Zhang, Zhentao; Xie, Ruizhi; Li, Shaokun
• Is Part Of: Agronomy Journal. Volume 113:Number 2(2021)
• Page Start: 1467
• Page End: 1479
• Publication Date: 2021-02-21
• Subjects: Agronomy -- Periodicals; 630
• DOI: 10.1002/agj2.20532
• Languages: English
• ISSNs: 0002-1962

Abstract

Abstract: With the popularization of late‐maturing and high‐yielding maize ( Zea mays L.) hybrids, high kernel moisture concentration at the usual harvest time has resulted in increased kernel breakage and additional drying costs. To achieve low kernel moisture at harvest in China's maize‐growing areas, there is a need for the selection of fast dry‐down hybrids and the prediction of the ideal harvest time. During 2014–2017, the time‐series kernel moisture concentrations of three maize hybrids were measured in the field in three major maize‐producing regions in China. Our goal was to accurately predict maize kernel dry‐down in the field. We found that the Logistic Power model M = 90/[1 + ( T / a ) b ] could be used to accurately predict the entire dry‐down process of maize kernels across hybrids and regions (concordance correlation coefficient, 0.884–0.996; RMSE, 2.76–5.16%; R 2, .943–.986; and coefficient of residual mass, −0.09–0.14), where M is the kernel moisture concentration (wet basis), a and b are parameters that reflect the dry‐down characteristics of the hybrids, and T is the thermal time (°C d) from silking based on mean daily temperature over the periods of grain‐filling and grain‐drying. This work provides a new and convenient model for predicting kernel moisture concentration and evaluating the dry‐down characteristics of hybrids using parameters a and b . Core Ideas: We developed a new model to predict maize kernel dry‐down. The model derives at entire dry‐down process compared with phase‐based models. The model correctly predicts kernel dry‐down on a regional scale.