Abstract

Efficient use of nitrogen (N) fertilizer is critically important for China’s food security and sustainable development. Crop models have been widely used to analyze yield variability, assist in N prescriptions, and determine optimum N rates. The objectives of this study were to use the CERES-Rice model to simulate the N response of different high-latitude, adapted flooded rice varieties to different types of weather seasons, and to explore different optimum rice N management strategies with the combinations of rice varieties and types of weather seasons. Field experiments conducted for five N rates and three varieties in Northeast China during 2011–2016 were used to calibrate and evaluate the CERES-Rice model. Historical weather data (1960–2014) were classified into three weather types (cool/normal/warm) based on cumulative growing degree days during the normal growing season for rice. After calibrating the CERES-Rice model for three varieties and five N rates, the model gave good simulations for evaluation seasons for top weight (R2 ≥ 0.96), leaf area index (R2 ≥ 0.64), yield (R2 ≥ 0.71), and plant N uptake (R2 ≥ 0.83). The simulated optimum N rates for the combinations of varieties and weather types ranged from 91 to 119 kg N ha−1 over 55 seasons of weather data and were in agreement with the reported values of the region. Five different N management strategies were evaluated based on farmer practice, regional optimum N rates, and optimum N rates simulated for different combinations of varieties and weather season types over 20 seasons of weather data. The simulated optimum N rate, marginal net return, and N partial factor productivity were sensitive to both variety and type of weather year. Based on the simulations, climate warming would favor the selection of the 12-leaf variety, Longjing 21, which would produce higher yield and marginal returns than the 11-leaf varieties under all the management strategies evaluated. The 12-leaf variety with a longer growing season and higher yield potential would require higher N rates than the 11-leaf varieties. In summary, under warm weather conditions, all the rice varieties would produce higher yield, and thus require higher rates of N fertilizers. Based on simulation results using the past 20 years of weather data, variety-specific N management was a practical strategy to improve N management and N partial factor productivity compared with farmer practice and regional optimum N management in the study region. The CERES-Rice crop growth model can be a useful tool to help farmers select suitable precision N management strategies to improve N-use efficiency and economic returns.

Highlights

  • Rice (Oryza sativa L.) is an important staple cereal crop that is sustenance to over half of the world’s population [1]

  • Most farmers apply N fertilizers based on their experience, but this has led to common problems, such as over-application, improper timing, low N-use efficiency (NUE), and environmental N losses [4,5,6]

  • It is likely that rice Leaf area index (LAI) in newer varieties is higher than what is programmed in CERES-Rice; the model tended to underestimate the maximum LAI

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Summary

Introduction

Rice (Oryza sativa L.) is an important staple cereal crop that is sustenance to over half of the world’s population [1]. China produces 28% of global rice production, but consumes 36% of the global nitrogen (N) used for rice production [2]. While rice farm size in China is typically less than one ha, rice farm size in the Sanjiang Plain of Northeast China is around 20 ha, representing a large-scale farming system in China. Northeast China has a cool climate, but rice production in this region is vital for the nation’s food security [3,4]. Most farmers apply N fertilizers based on their experience, but this has led to common problems, such as over-application, improper timing, low N-use efficiency (NUE), and environmental N losses [4,5,6]. New approaches to develop N management strategies are urgently needed to improve NUE and reduce N loss to the environment

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