Improving High-Latitude Rice Nitrogen Management with the CERES-Rice Crop Model

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.

[1]  Jianliang Huang,et al.  Improving nitrogen fertilization in rice by sitespecific N management. A review , 2010, Agronomy for Sustainable Development.

[2]  Deanna L. Osmond,et al.  In-Season Optimization and Site-Specific Nitrogen Management for Soft Red Winter Wheat , 2003 .

[3]  S. Ranamukhaarachchi,et al.  Assessment of the CERES-Rice model for rice production in the Central Plain of Thailand , 2001, The Journal of Agricultural Science.

[4]  Y. Miao,et al.  Within‐Field Variation in Corn Yield and Grain Quality Responses to Nitrogen Fertilization and Hybrid Selection , 2006 .

[5]  Bin Liu,et al.  Improving nitrogen use efficiency with minimal environmental risks using an active canopy sensor in a wheat-maize cropping system , 2017 .

[6]  P. C. Robert,et al.  Potential Impact of Precision Nitrogen Management on Corn Yield, Protein Content, and Test Weight , 2007 .

[7]  Johanna Link,et al.  Evaluating the economic and environmental impact of environmental compensation payment policy under uniform and variable-rate nitrogen management , 2006 .

[8]  Shanyu Huang,et al.  Active canopy sensor-based precision N management strategy for rice , 2012, Agronomy for Sustainable Development.

[9]  Thomas S. Colvin,et al.  Model-Based Technique to Determine Variable-Rate Nitrogen for Corn , 1999 .

[10]  Wei Shi,et al.  Evaluating different approaches to non-destructive nitrogen status diagnosis of rice using portable RapidSCAN active canopy sensor , 2017, Scientific Reports.

[11]  Gerrit Hoogenboom,et al.  Application of the CSM-CERES-Rice model for evaluation of plant density and nitrogen management of fine transplanted rice for an irrigated semiarid environment , 2011, Precision Agriculture.

[12]  J. Ritchie,et al.  A Comprehensive Review of the CERES-Wheat, -Maize and -Rice Models’ Performances , 2016 .

[13]  Penghuan Liu,et al.  A preliminary precision rice management system for increasing both grain yield and nitrogen use efficiency , 2013 .

[14]  R. Schindelbeck,et al.  Dynamic Model Improves Agronomic and Environmental Outcomes for Maize Nitrogen Management over Static Approach. , 2017, Journal of environmental quality.

[15]  Liangzhi Gao,et al.  Rice clock model―a computer model to simulate rice development , 1992 .

[16]  Xin-ping Chen,et al.  Development of Regional Nitrogen Rate Guidelines for Intensive Cropping Systems in China , 2013 .

[17]  M. Wopereis,et al.  Crops that feed the world 7: Rice , 2012, Food Security.

[18]  Deng Wei,et al.  Climate change in the Sanjiang Plain disturbed by large-scale reclamation , 2002 .

[19]  Anil Kumar Singh Precision Farming , 2019, International Journal of Trend in Scientific Research and Development.

[20]  J. Paz,et al.  Evaluating Management Zone Optimal Nitrogen Rates with a Crop Growth Model , 2006 .

[21]  James W. Jones,et al.  Estimating potential economic return for variable soybean variety management , 2003 .

[22]  Prasad S. Thenkabail,et al.  Land Resources Monitoring, Modeling, and Mapping with Remote Sensing , 2015 .

[23]  Shanyu Huang,et al.  Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor , 2013 .

[24]  Yao Yin-kun Current Rice Management Practices of Farmers in Heilongjiang Land Reclamation Area and Improvement Strategies , 2012 .

[25]  G. Pan,et al.  Storage and sequestration potential of topsoil organic carbon in China's paddy soils , 2004 .

[26]  Johanna Link,et al.  Evaluation of current and model-based site-specific nitrogen applications on wheat (Triticum aestivum L.) yield and environmental quality , 2008, Precision Agriculture.

[27]  K. Singh,et al.  Evaluation of CERES-rice model (V. 4.0) under temperate conditions of Kashmir valley, India , 2007 .

[28]  J. Paz,et al.  Examples of strategies to analyze spatial and temporal yield variability using crop models , 2002 .

[29]  U. A. Naher,et al.  Variety-Specific Nitrogen Fertilizer Recommendation for Lowland Rice , 2005 .

[30]  Brian L. Steward,et al.  Methodology to link production and environmental risks of precision nitrogen management strategies in corn , 2006 .

[31]  Weifeng Zhang,et al.  Improving nitrogen management via a regional management plan for Chinese rice production , 2015 .

[32]  L. Tong,et al.  The current status, threats and protection way of Sanjiang Plain wetland, Northeast China , 2005, Journal of Forestry Research.

[33]  E. Gérardeaux,et al.  Positive effects of climate change on rice in Madagascar , 2011, Agronomy for Sustainable Development.

[34]  Fei Yuan,et al.  Satellite Remote Sensing-Based In-Season Diagnosis of Rice Nitrogen Status in Northeast China , 2015, Remote. Sens..

[35]  Li Shao-kun,et al.  Comparitive Study on the Measure Methods of the Leaf Area , 2005 .

[36]  Hui Ju,et al.  Adaptation of agriculture to warming in Northeast China , 2007 .

[37]  Yan-sui Liu,et al.  Climate warming and land use change in Heilongjiang Province, Northeast China , 2011 .

[38]  K. Pannangpetch,et al.  Evaluation of CSM-CERES-rice in simulating the response of lowland rice cultivars to nitrogen application , 2012 .