Veysi Shadman, Galehban Eslam, Nouri Milad, Mallah Sina, Nouri Hamideh
Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran.
Heliyon. 2024 Aug 15;10(16):e36350. doi: 10.1016/j.heliyon.2024.e36350. eCollection 2024 Aug 30.
This study presents a comprehensive framework for analyzing water productivity products provided by the FAO Water Productivity Open-access portal (WaPOR), focusing on various crops cultivated in both rainfed and irrigated areas within a semi-arid basin in Iran. Two indices, namely Gross Water Productivity (GWP) and Net Water Productivity (NWP), were introduced to quantify water productivity across crop fields. However, these indices may mislead decision-makers, because they aggregate water productivity for all crops and exacerbate the challenges posed by water scarcity. Therefore, mapping crop types seems necessary to enhance the interpretation of these indices and develop a dimensionless index for comparing different crops. The results demonstrated a fundamental change when comparing dimensionless water productivity with GWP and NWP products. Surprisingly, some pixels initially exhibiting high water productivity ranked as low water-productive pixels based on the derived dimensionless index, and vice versa. Based on dimensionless indicators, rainfed crops, particularly rainfed cereals, ranked as the most water-productive crops. The areas with dimensionless values below 0.5 warrant heightened attention to curtail non-beneficial water consumption and elevate water productivity. This research emphasizes the significance of mapping cultivation types as supplementary layers to facilitate precise, data-driven decision-making and enable comparisons of crops based on dimensionless water productivity indices.
本研究提出了一个全面的框架,用于分析联合国粮食及农业组织水生产率开放获取平台(WaPOR)提供的水生产率产品,重点关注伊朗一个半干旱流域内雨养和灌溉地区种植的各种作物。引入了两个指标,即总水生产率(GWP)和净水生产率(NWP),以量化作物田的水生产率。然而,这些指标可能会误导决策者,因为它们汇总了所有作物的水生产率,加剧了水资源短缺带来的挑战。因此,绘制作物类型图似乎有必要,以加强对这些指标的解读,并开发一个无量纲指标来比较不同作物。结果表明,将无量纲水生产率与GWP和NWP产品进行比较时出现了根本性变化。令人惊讶的是,一些最初显示高水生产率的像素根据导出的无量纲指标被列为低水生产率像素,反之亦然。根据无量纲指标,雨养作物,特别是雨养谷物,被列为水生产率最高的作物。无量纲值低于0.5的区域需要格外关注,以减少无益的用水并提高水生产率。本研究强调了绘制种植类型图作为补充图层的重要性,以促进精确的、数据驱动的决策,并能够基于无量纲水生产率指标对作物进行比较。