优化赤水灌溉和施肥，促进贝宁北部反季节玉米生产

IF 5.6 1区农林科学 Q1 AGRONOMY

Field Crops Research Pub Date : 2024-10-19 DOI:10.1016/j.fcr.2024.109613

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引用次数: 0

摘要

背景土壤水分和肥力管理一直是西非作物生产的主要挑战，而气候多变性又加剧了其影响。本研究利用 DSSAT CERES-Maize 作物模型，评估了贝宁反季节玉米生产中灌溉和施肥水平的最佳组合。方法进行了两年（2018 年和 2019 年）4 级赤字养分（DN）试验和两年（2019 年和 2020 年）4 级赤字灌溉（DI）试验，并收集了玉米生长和产量数据。利用 2018 年（DN2018 年）缺养试验和 2019 年（DI2019 年）缺灌试验的作物数据对 DSSAT 模型进行了校准，并利用 DN2019 年和 DI2020 年的试验数据进行了验证。结果该模型预测了谷物产量（GY）和总地上生物量（TB），在验证过程中，GY 的相对均方根误差和效率系数分别为 18.3 % 和 0.38，TB 的相对均方根误差和效率系数分别为 11.7 % 和 0.50。然而，该模型没有考虑 DI 或 DN 对物候期的影响，这导致校准和验证期间 DI 和 DN 处理的开花期和成熟期预测值相似。此外，该模型对 DI 期间记录的高温度值（45°C）很敏感，导致 DI 处理的谷粒灌浆率降低。DI 处理比 DN 处理对 DUL、SLL、SAT、RGFIL 和 RUE 的变化更敏感；而 DN 处理对 CTCNP2 更敏感。将玉米无性期的需水量减少 40%，预测的谷物产量与全灌溉处理相似；将需水量减少 60%，预测的水分利用效率（WUE）与全灌溉处理相似。此外，在最佳 DI 和不施肥的情况下，谷物产量的年际变异性较低，但在高 DI 和高施肥量的情况下，谷物产量的年际变异性较高。结论由于模型结构在处理水分和养分胁迫对物候期的影响方面存在缺陷，因此在最佳 DI 和 DN 水平下的预计谷物产量和 WUE 很可能被低估。为了可靠地评估水分和养分胁迫对谷物产量和WUE的影响，需要更新DSSAT中CERES作物模型的参数化和代码，以充分考虑水分和养分胁迫对物候期的影响，以及物候期对LAI和产量预测的贡献。

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Optimizing deficit irrigation and fertilizer application for off-season maize production in Northern Benin

Context

Soil water and fertility management have been the main challenges of crop production in West Africa, and their impacts are exacerbated by climate variability. While research has been conducted to optimize fertility and water applications for rainfed crops production in this region, little is known about the management of these resources for off-season cereal crops production.

Objective

This study assessed the optimal combination of irrigation and fertilizer levels for off-season maize production in Benin, using the DSSAT CERES-Maize crop model.

Methods

Two years’ experiments (2018 and 2019) of 4 levels of deficit nutrient (DN) and two years’ experiments (2019 and 2020) of 4 levels of deficit irrigation (DI) were conducted and data were collected on maize growth and yield. DSSAT model was calibrated using crop data from DN experiment in 2018 (DN2018) and DI experiment in 2019 (DI2019), and validated using the DN2019 and the DI2020 experimental data. Then, a long-term scenarios analysis (40-years, 1980–2019) was performed to optimize (i) DI levels, (ii) DN rates; and (iii) combined DI levels and DN rates.

Results

The model predicted the grain yield (GY) and total aboveground biomass (TB), with a relative root mean square error and a coefficient of efficiency of 18.3 % and 0.38 for the GY and 11.7 % and 0.50 for the TB during the validation, respectively. However, the model did not account for the effects of DI or DN on the phenological dates, which led to similar predicted values for the anthesis and maturity dates among DI and DN treatments during calibration and validation. Moreover, the model was sensitive to periods with high values of temperature (>45°C) recorded during the DI period, inducing a reduction of the grain filling rate in DI treatments. DI treatments were more sensitive to a change in DUL, SLL, SAT, RGFIL and RUE than the DN treatments; while the DN treatments were more sensitive to the CTCNP2. Reducing maize water requirements by 40 % at the vegetative stage resulted in similar predicted grain yield as in the full irrigation treatment; while reducing the water requirements by 60 % resulted in similar predicted water use efficiency (WUE) as in the full irrigation treatment. Furthermore, the inter-annual variability of grain yield was lower under the optimal DI combined with no fertilizer but higher under high DI combined with higher fertilizer rates. Finally, a combination of 40–60 % of deficit irrigation at the vegetative stage and one-third to half of the recommended fertilizer rates depending on resources availability was the optimum combination of DI and DN rates for off-season maize production.

Conclusions

The projected grain yield and WUE under optimal DI and DN levels were likely underestimated due to shortcomings in the model structure to deal with effects of water and nutrient stresses on phenological dates. For reliable assessments of the effects of water and nutrient stresses on grain yield and WUE, there is need to update parameterization and code of the CERES crop models in DSSAT to have a sufficiently strong effect of water and nutrient stress on phenological dates, and the contribution of phenology to LAI and yields predictions.

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来源期刊

Field Crops Research 农林科学-农艺学

CiteScore

9.60

自引率

12.10%

发文量

307

审稿时长

46 days

期刊介绍： Field Crops Research is an international journal publishing scientific articles on: √ experimental and modelling research at field, farm and landscape levels on temperate and tropical crops and cropping systems, with a focus on crop ecology and physiology, agronomy, and plant genetics and breeding.