Generic optimization approach of soil hydraulic parameters for site-specific model applications

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Jonas Trenz, Emir Memic, William D. Batchelor, Simone Graeff-Hönninger
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Abstract

Site-specific crop management is based on the postulate of varying soil and crop requirements in a field. Therefore, a field is separated into homogenous management zones, using available data to adapt management practices environment to maximize productivity and profitability while reducing environmental impacts. Due to advancing sensor technologies, crop growth and yield data on more minor scales are common, but soil data often needs to be more appropriate. Crop growth models have shown promise as a decision support tool for site-specific farming. The Decision Support System for Agrotechnology Transfer (DSSAT) is a widely used point-based model. To overcome the problem of inappropriate soil input data problem, this study introduces an external plug-in program called Soil Profile Optimizer (SPO), which uses the current DSSAT v4.8 to calibrate soil profile parameters on a site-specific level. Developed as an inverse modelling approach, the SPO can calibrate selected soil profile parameters by targeting available in-season plant data. Root Mean Square Error (RMSE) and normalized RMSE as error minimization criteria are used. The SPO was tested and evaluated by comparing different simulation scenarios in a case study of a 3-yr field trial with maize. The scenario with optimized soil profiles, conducted with the SPO, resulted in an R2 of 0.76 between simulated and observed yield and led to significant improvements compared to the scenario conducted with field scale soil profile information (R2 0.03). The SPO showed promise in using spatial plant measurements to estimate management zone scale soil parameters required for the DSSAT model.

Abstract Image

场地特定模型应用中土壤水力参数的通用优化方法
特定地点作物管理是基于对不同土壤和作物需求的假设。因此,油田被划分为同质的管理区域,利用可用的数据来适应管理实践环境,以最大限度地提高生产率和盈利能力,同时减少对环境的影响。由于传感器技术的进步,更小尺度的作物生长和产量数据很常见,但土壤数据往往需要更合适。作物生长模型已显示出作为特定地点农业决策支持工具的前景。农业技术转移决策支持系统(DSSAT)是一种应用广泛的基于点的模型。为了克服土壤输入数据不合适的问题,本研究引入了一个名为土壤剖面优化器(SPO)的外部插件程序,该程序使用当前DSSAT v4.8来校准特定站点的土壤剖面参数。作为一种反向建模方法,SPO可以通过瞄准可用的季节性植物数据来校准选定的土壤剖面参数。使用均方根误差(RMSE)和归一化RMSE作为误差最小化标准。在玉米3年田间试验的案例研究中,通过比较不同的模拟情景,对SPO进行了测试和评价。采用SPO优化土壤剖面的情景,模拟产量与观测产量之间的R2为0.76,与采用现场尺度土壤剖面信息的情景相比有显著提高(R2为0.03)。SPO显示了利用空间植物测量来估计DSSAT模型所需的管理区尺度土壤参数的前景。
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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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