AgroBioSciences, Plant Stress Physiology Laboratory, Mohammed VI Polytechnic University, 43150, Benguerir, Morocco.
Gembloux Agro-Bio Tech Faculty, Water, Soil & Plant Exchanges, University of Liege, B-5030, Gembloux, Belgium.
Environ Sci Pollut Res Int. 2023 Oct;30(48):106083-106098. doi: 10.1007/s11356-023-29658-4. Epub 2023 Sep 19.
The impact of climate change on water resource availability and soil quality is more and more emphasized under the Mediterranean basin, mostly characterized by drought and extreme weather conditions. The present study aims to investigate how electromagnetic induction technique and soil mapping combined with crop yield data can be used to optimize phosphorus (P) use efficiency by chickpea crop under drip fertigation system. The study was carried out on a 2.5-ha agricultural plot and the agronomic experiments in two growing cycles of chickpea crop. Soil spatial variability was first assessed by the measurement of soil apparent electrical conductivity (ECa) using the CMD Mini-Explorer sensor, and then, soil physicochemical properties were evaluated based on an oriented soil sampling scheme to explore other soil spatial variabilities influencing chickpea yield and quality. Data from the first agronomic experiment were used in geostatistical, multiple linear regression (MLR), and fuzzy c-means unsupervised classification algorithms to properly identify P drip fertigation management zones (MZs). Results from the Person's correlation analysis revealed that chickpea grain yield was more influenced by soil ECa (r = - 0.56), pH (r = - 0.84), ECe (r = - 0.6), P content (r = 0.72), and calcium (Ca) content (r = - 0.83). The proposed MLR-based model to predict chickpea grain yield showed good performances with a normalized root mean square error (NRMSE) of 0.11% and a coefficient of determination (R) equal to 0.69. The identified MZs were verified by the one-way variance analysis for the studied soil and plant attributes, revealing that the first MZ1 presents a high grain yield, high soil P content, and low ECa. The low fertility MZ2 located in the south part of the studied site presented a low chickpea grain yield due to the low P content and the high ECa. Moreover, the application of P-variable rate fertigation regimes in the second field experiment significantly improved P use efficiency, chickpea grain yield, seed quality, and farmer income by 18%, 12%, 9%, and 136 $/ha, respectively, as compared to the conventional drip fertigation practices. The approach proposed in this study can greatly contribute to optimizing agro-input use efficiency under drip fertigation system, thereby improving farmers' incomes, preserving the ecosystem, and ensuring sustainable cropping systems in the Mediterranean climate.
在受地中海气候影响的地区，水资源的可利用性和土壤质量越来越受到重视，该地区的气候特点主要是干旱和极端天气条件。本研究旨在探讨如何将电磁感应技术与土壤制图相结合，并结合作物产量数据，优化滴灌系统下鹰嘴豆的磷（P）利用效率。该研究在一个 2.5 公顷的农业地块上进行，在鹰嘴豆作物的两个生长周期中进行了农业试验。首先，通过使用 CMD Mini-Explorer 传感器测量土壤表观电导率（ECa）来评估土壤空间变异性，然后，根据定向土壤采样方案评估土壤物理化学性质，以探索影响鹰嘴豆产量和质量的其他土壤空间变异性。第一个农业试验的数据用于地统计学、多元线性回归（MLR）和模糊 c-均值无监督分类算法，以正确识别 P 滴灌施肥管理区（MZ）。Person 相关分析的结果表明，鹰嘴豆籽粒产量受土壤 ECa（r = -0.56）、pH（r = -0.84）、ECe（r = -0.6）、P 含量（r = 0.72）和钙（Ca）含量（r = -0.83）的影响更大。基于 MLR 的预测鹰嘴豆籽粒产量模型表现出良好的性能，归一化均方根误差（NRMSE）为 0.11%，决定系数（R）等于 0.69。通过单向方差分析对研究的土壤和植物属性进行了 MZ 的验证，结果表明，第一个 MZ1 表现出高籽粒产量、高土壤 P 含量和低 ECa。位于研究地点南部的低肥力 MZ2 由于 P 含量低和 ECa 高，导致鹰嘴豆籽粒产量低。此外，与传统的滴灌施肥相比，在第二个田间试验中应用 P 变量施肥制度显著提高了 P 利用效率、鹰嘴豆籽粒产量、种子质量和农民收入，分别提高了 18%、12%、9%和 136 美元/公顷。本研究提出的方法可以极大地促进滴灌系统下农业投入品利用效率的优化，从而提高农民收入、保护生态系统，确保地中海气候下可持续的种植系统。
