利用 APSIM 模型模拟玉米产量对氮素的时空响应

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Precision Agriculture Pub Date : 2024-08-13 DOI:10.1007/s11119-024-10178-1
Laura J. Thompson, Sotirios V. Archontoulis, Laila A. Puntel
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引用次数: 0

摘要

背景基于过程的作物生长模型可以解释影响作物生产最佳氮肥用量的土壤和作物动态。目前,人们对基于过程的模型在田间特定地点特定区域的准确性以及在校准这些模型时需要考虑的关键因素还缺乏了解。方法我们在内布拉斯加州东南部进行了为期两年的四次氮肥率实验(2 块田 × 田内 2 个区)。实验数据用于校准和测试 APSIM 模型。通过在不同氮肥施用率下对校准模型进行多次迭代,获得了 APSIM 模拟的每个区域和地点的玉米产量对氮肥的响应。结果和结论APSIM 模型预测了 11 个历史年份的玉米产量,相对均方根误差 (RRMSE) 为 12%,而氮研究中 EONR 的产量相对均方根误差 (RRMSE) 为 8.8%。不同地点、年份和区域的模拟 EONR 均低于观测到的 EONR,误差大于产量。在细粒度土壤中,氮肥的模拟增产效果被低估,而在中等粒度土壤中则被高估。玉米对氮的长期产量响应表明,模拟 EONR 的时间变化大于空间变化。长期 EONR 和 EONR 时的产量随着降雨量的增加而增加,而零 N 时的产量在正常年份最大。时间变化主要由氮损失的年际变化(CV 为 67% ± 9.5)驱动。土壤质地、水文特性、地下水位和瓦片排水是准确校准特定地点模型的关键变量。通过纳入原位或遥感数据,更好地估算氮的动态变化,可以改进特定地点的 EONR 模拟。我们的结论是,APSIM 可以为该地区的系统动力学提供有价值的见解,但它无法提供精确的氮速率估算。我们的研究有助于利用模拟建模了解田间变化。
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Simulating within-field spatial and temporal corn yield response to nitrogen with APSIM model

Context

Process-based crop growth models can explain soil and crop dynamics that influence the optimal N rate for crop production. Currently, there is a lack of understanding regarding the accuracy of process-based models for site-specific zones within fields, as well as the key factors that need to be considered when calibrating these models for zone-specific economic optimum N rate (EONR).

Objective

We calibrated the Agricultural Production Systems sIMulator (APSIM) model in contrasting zones within fields, quantified the model performance, and used the calibrated model to develop long-term corn yield response to N to assess the temporal variability between zones and sites to assist decision making.

Methods

We conducted four N rate experiments (2 fields × 2 zones within a field) over two years in southeast Nebraska. Experimental data were used to calibrate and test the APSIM model. APSIM simulated corn yield response to N for each zone and site was obtained by running numerous iterations of the calibrated model at different N rates. Observed and simulated corn yield response to N rate were analyzed with statistical models to estimate the EONR.

Results and conclusions

The APSIM model predicted corn yield over 11 historical years with a relative root mean square error (RRMSE) of 12% and yield at EONR in the N studies with RRMSE of 8.8%. The simulated EONR was lower than the observed EONR across sites, years, and zones with greater error than yield. The simulated yield increase with N fertilization was under-estimated in fine textured soils and over-estimated in medium textured soils. Long-term corn yield response to N showed that temporal variation in simulated EONR was greater than spatial variation. Long-term EONR and yield at EONR increased with increasing rainfall, while yield at zero N was greatest in normal years. Temporal variation was driven primarily by year-to-year variation in N loss (CV of 67% ± 9.5). Soil texture, hydrological properties, water table, and tile drainage were key variables for accurate site-specific model calibration. Improvements in simulating site-specific EONR may be realized by including in-situ or remotely sensed data for better estimation of N dynamics. We concluded that APSIM can provide valuable insights into systems dynamics in this region, but it can’t provide precise N-rate estimates. Our study contributes to understanding of the within-field variability using simulation modeling.

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来源期刊
Precision Agriculture
Precision Agriculture 农林科学-农业综合
CiteScore
12.30
自引率
8.10%
发文量
103
审稿时长
>24 weeks
期刊介绍: Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
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