Assessment of DSSAT and AquaCrop models to simulate soybean and maize yield under water stress conditions

IF 0.8 4区农林科学 Q3 AGRICULTURE, MULTIDISCIPLINARY

Spanish Journal of Agricultural Research Pub Date : 2023-07-07 DOI:10.5424/sjar/2023213-19918

Ali DEHGHAN MOROOZEH, Bahman Farhadi Bansouleh, M. Ghobadi, Abdoreza Ahmadpour

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引用次数: 0

Abstract

Aim of study: To evaluate the performance of DSSAT and AquaCrop models in the estimation of soybean and grain maize yield under water stress conditions in a semi-arid region. Area of study: Kermanshah, Iran. Material and methods: AquaCrop and DSSAT were assessed to simulate soybean and maize. Both models were calibrated using field data. Field experiments were performed in a randomized complete block design with eight and four irrigation treatments for soybeans and maize, respectively with three replications. Measures of Normalized Root Mean Square Error (nRMSE) and Nash-Sutcliffe Model Efficiency were used to evaluate the accuracy of the models. For this purpose, simulated values of leaf area index / green crop canopy, grain yield, biomass, and soil moisture were compared with measured data. Main results: Results indicated that the CROPGRO-Soybean in DSSAT software simulated more accurate crop growth of soybean than AquaCrop. The average nRMSE of the DSSAT model for estimating soil moisture, leaf area index, grain yield, and biomass were 6%, 14%, 16% and 20%, respectively. For maize, AquaCrop simulated crop growth more reliably than CERES-maize. The average nRMSE of 3%, 10%, 13% and 27% of the Aquacrop model in simulating the parameters of soil moisture, green crop canopy, biomass, and grain yield. Research highlights: Considering the better performance of AquaCrop for maize and DSSAT for soybean in the study area, it is not possible to propose a specific model to simulate the growth of all crops in a region.

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水分胁迫条件下DSSAT和AquaCrop模型对大豆和玉米产量的模拟评估

研究目的:评价DSSAT和AquaCrop模型在半干旱区水分胁迫条件下大豆和玉米产量估算中的性能。研究领域:伊朗克尔曼沙阿。材料与方法:采用AquaCrop和DSSAT模拟大豆和玉米。两个模型都使用现场数据进行了校准。田间试验采用随机完全区组设计，大豆和玉米分别采用8个和4个灌溉处理，3个重复。采用归一化均方根误差(nRMSE)和Nash-Sutcliffe模型效率来评估模型的准确性。为此，将叶面积指数/绿色作物冠层、粮食产量、生物量和土壤水分的模拟值与实测值进行比较。主要结果:结果表明，DSSAT软件中的CROPGRO-Soybean比AquaCrop更准确地模拟了大豆的作物生长。DSSAT模型估算土壤水分、叶面积指数、粮食产量和生物量的平均nRMSE分别为6%、14%、16%和20%。对于玉米，AquaCrop比ceres玉米更可靠地模拟作物生长。在模拟土壤水分、绿色作物冠层、生物量和粮食产量等参数时，Aquacrop模型的平均nRMSE分别为3%、10%、13%和27%。研究重点:考虑到研究区域玉米AquaCrop和大豆DSSAT的性能较好，不可能提出一个特定的模型来模拟一个区域所有作物的生长。

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来源期刊

Spanish Journal of Agricultural Research 农林科学-农业综合

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

6 months

期刊介绍： The Spanish Journal of Agricultural Research (SJAR) is a quarterly international journal that accepts research articles, reviews and short communications of content related to agriculture. Research articles and short communications must report original work not previously published in any language and not under consideration for publication elsewhere. The main aim of SJAR is to publish papers that report research findings on the following topics: agricultural economics; agricultural engineering; agricultural environment and ecology; animal breeding, genetics and reproduction; animal health and welfare; animal production; plant breeding, genetics and genetic resources; plant physiology; plant production (field and horticultural crops); plant protection; soil science; and water management.