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<p>Agriculture—a sector heavily reliant on natural resources—is intrinsically tied to climate change (CC) in countries like Pakistan. Rising temperatures, changing rainfall patterns, and reducing available irrigation water deeply impact on this sector, especially in developing countries. In this scenario, crop water requirement (CWR) and irrigation scheduling (IS) are two of the most critical components in agriculture. This study assesses spatiotemporal trends in CWR and IS of major crops (wheat, rice, sugarcane, maize, and cotton) grown in Punjab, Pakistan. Reference evapotranspiration (ET<sub>o</sub>) was estimated using Penman–Monteith model across selected 36 districts for 1950–2021. Decadal spatiotemporal trends, hotspots, and homogeneity of CWR were analyzed. Decadal gross irrigation requirement (GIR) was also estimated. Agro climatic zone-specific assessment revealed complex dynamics of CWR and IS across different crop growth stages. CWR of the major crops was 594, 718, 1,844, 558 and 1,300 mm/dec, respectively. Decadal analysis showed an increase in CWR in the late-twentieth century. Concentrated hotspots were observed in southern regions for cotton, wheat, and sugarcane, and for rice and maize in central and northern regions. The years 1997 and 1998 were observed to be the infraction points in CWR of all the crops. Spatiotemporal analysis of CWR revealed increase in CWR in the southern districts due to sporadic precipitation patterns. Decadal GIR and IS revealed an increase in CWR of all crops towards the end of study period. The findings highlight a crucial need of zone- and crop-specific strategies for sustainable production and conservation of natural resources.</p> Graphical Abstract <p>The graphical abstract illustrates a data-driven assessment of the spatiotemporal variability of crop water requirement (CWR) and irrigation scheduling (IS) for major crops in Punjab, Pakistan over the period of 1950–2021. Climate data from 36 districts was collected, followed by spatial processing in ArcGIS to convert raster layers into point data. FAO Penman–Monteith method using the CROPWAT was used to compute reference evapotranspiration (ET<sub>o</sub>), CWR, and irrigation scheduling parameters. These parameters were spatially interpolated using the inverse distance weighted technique (IDW) and hotspot analysis to detect geographical patterns. Temporal trend and homogeneity tests were conducted in XLSTAT to identify significant changes over time. Python, OriginPro, and Excel were used for data handling and visualization. The line graphs represent the homogeneity in CWR trends over the study period for all the crops. The spatial map and the heatmap represent production and decade-wise mean irrigation requirement of wheat crop, respectively. The spatial maps represent the CWR of wheat, crop evapotranspiration, and effective rainfall of the 36 districts. Decade-wise hotspots of CWR for wheat crop are also highlighted. The heat maps illustrate the decade-wise gross irrigation requirement of cotton crop in the “cotton belt” of the study area. Finally, the conclusion highlights higher CWR in Southern Punjab, an infraction point in the year 1998, and a rise in gross irrigation requirements (GIR) in recent decades. These changes, attributed to increasing ET₀ and climate variability, indicate growing pressure on Punjab’s water resources and call for an adaptive long-term irrigation planning.</p> <p></p>

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Data-Driven Irrigation Scheduling: A Spatiotemporal Assessment of Crop Water Requirements for Major Crops in Punjab, Pakistan

  • 期刊论文

  • 发表日期：

    2025年7月26日

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  • Saliha Qamar,
  • Hadeed Ashraf,
  • Muhammad Sultan,
  • Sobhy M. Ibrahim,
  • Muhammad Usman Khan

摘要

Agriculture—a sector heavily reliant on natural resources—is intrinsically tied to climate change (CC) in countries like Pakistan. Rising temperatures, changing rainfall patterns, and reducing available irrigation water deeply impact on this sector, especially in developing countries. In this scenario, crop water requirement (CWR) and irrigation scheduling (IS) are two of the most critical components in agriculture. This study assesses spatiotemporal trends in CWR and IS of major crops (wheat, rice, sugarcane, maize, and cotton) grown in Punjab, Pakistan. Reference evapotranspiration (ETo) was estimated using Penman–Monteith model across selected 36 districts for 1950–2021. Decadal spatiotemporal trends, hotspots, and homogeneity of CWR were analyzed. Decadal gross irrigation requirement (GIR) was also estimated. Agro climatic zone-specific assessment revealed complex dynamics of CWR and IS across different crop growth stages. CWR of the major crops was 594, 718, 1,844, 558 and 1,300 mm/dec, respectively. Decadal analysis showed an increase in CWR in the late-twentieth century. Concentrated hotspots were observed in southern regions for cotton, wheat, and sugarcane, and for rice and maize in central and northern regions. The years 1997 and 1998 were observed to be the infraction points in CWR of all the crops. Spatiotemporal analysis of CWR revealed increase in CWR in the southern districts due to sporadic precipitation patterns. Decadal GIR and IS revealed an increase in CWR of all crops towards the end of study period. The findings highlight a crucial need of zone- and crop-specific strategies for sustainable production and conservation of natural resources.

Graphical Abstract

The graphical abstract illustrates a data-driven assessment of the spatiotemporal variability of crop water requirement (CWR) and irrigation scheduling (IS) for major crops in Punjab, Pakistan over the period of 1950–2021. Climate data from 36 districts was collected, followed by spatial processing in ArcGIS to convert raster layers into point data. FAO Penman–Monteith method using the CROPWAT was used to compute reference evapotranspiration (ETo), CWR, and irrigation scheduling parameters. These parameters were spatially interpolated using the inverse distance weighted technique (IDW) and hotspot analysis to detect geographical patterns. Temporal trend and homogeneity tests were conducted in XLSTAT to identify significant changes over time. Python, OriginPro, and Excel were used for data handling and visualization. The line graphs represent the homogeneity in CWR trends over the study period for all the crops. The spatial map and the heatmap represent production and decade-wise mean irrigation requirement of wheat crop, respectively. The spatial maps represent the CWR of wheat, crop evapotranspiration, and effective rainfall of the 36 districts. Decade-wise hotspots of CWR for wheat crop are also highlighted. The heat maps illustrate the decade-wise gross irrigation requirement of cotton crop in the “cotton belt” of the study area. Finally, the conclusion highlights higher CWR in Southern Punjab, an infraction point in the year 1998, and a rise in gross irrigation requirements (GIR) in recent decades. These changes, attributed to increasing ET₀ and climate variability, indicate growing pressure on Punjab’s water resources and call for an adaptive long-term irrigation planning.