Simulated CSM-CROPGRO-cotton yield under projected future climate by SimCLIM for southern Punjab, Pakistan

Amin, Asad; Nasim, Wajid; Mubeen, Muhammad; Ahmad, Ashfaq; Nadeem, Muhammad; Urich, Peter; Fahad, Shah; Ahmad, Shakeel; Wajid, Aftab; Tabassum, Fareeha; Hammad, Hafiz Mohkum; Sultana, Syeda Refat; Anwar, Syamsul; Khan, Shahbaz; Wahid, Abdul; Wilkerson, Carol Jo; Hoogenboom, Gerrit

doi:10.1016/j.agsy.2017.05.010

Cited by 67 publications

(12 citation statements)

References 31 publications

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“…At the end of this century, in Punjab, Pakistan, the rise in average temperature simulated by RCP scenarios will be above 2 to 3 • C (Figure 2). Fruits initiated 60 to 70 schedule days before final harvesting do not supply to yield [28]. Hence, suitable timing of topping to avoid fruits' inadequate formation is vital for high-quality and maximum yield.…”

Section: Adaptation To Climate-smart Managementmentioning

confidence: 99%

Impact of Climate Warming on Cotton Growth and Yields in China and Pakistan: A Regional Perspective

et al. 2021

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Year to year change in weather poses serious threats to agriculture globally, especially in developing countries. Global climate models simulate an increase in global temperature between 2.9 to 5.5 °C till 2060, and crop production is highly vulnerable to climate warming trends. Extreme temperature causes a significant reduction in crop yields by negatively regulating the crop phenology. Therefore, to evaluate warming impact on cotton (Gossypium hirsutum L.) production and management practices, we quantified agrometeorological data of 30 years by applying multiple crop modelling tools to compute the expected rise in temperature, impact of crop phenology, yield loss, provision of agrometeorology-services, agronomic technologies, and adaptation to climate-smart agriculture. Model projections of 15 agrometeorology stations showed that the growing duration of the sowing-boll opening and sowing-harvesting stages was reduced by 2.30 to 5.66 days decade−1 and 4.23 days decade−1, respectively, in Pakistan. Temperature rise in China also advanced the planting dates, sowing emergence, 3–5 leaves, budding anthesis, full-bloom, cleft-boll, boll-opening, and boll-opening filling by 24.4, 26.2, 24.8, 23.3, 22.6, 15.8, 14.6, 5.4, 2.9, and 8.0 days. Furthermore, present findings exhibited that the warming effect of sowing-harvest time was observed 2.16 days premature, and delayed for 8.2, 2.4, and 5.3 days in the 1970s, 1980s, and 1990s in China. APSIM-cotton quantification revealed that the sowing, emergence, flowering, and maturity stages were negatively correlated with temperature −2.03, −1.93, −1.09, and −0.42 days °C−1 on average, respectively. This study also provided insight into the adaptation of smart and better cotton by improving agrotechnological services.

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Section: Adaptation To Climate-smart Managementmentioning

confidence: 99%

Impact of Climate Warming on Cotton Growth and Yields in China and Pakistan: A Regional Perspective

et al. 2021

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“…The DSSAT CSM can simulate crop growth, development, and yield in response to variability in weather conditions, soil properties, and management practices (Jones et al, 2003;Hoogenboom et al, 2012Hoogenboom et al, , 2015Thorp et al, 2014). The DSSAT CSM CROPGRO-Cotton model has been extensively used by researchers worldwide for various applications (Buttar et al, 2007;Pathak et al, 2007;Ortiz et al, 2009;Pereira et al, 2009;Thorp et al, 2010Thorp et al, , 2014Modala et al, 2015;Adhikari et al, 2016Adhikari et al, , 2017Mauget et al, 2017;Loison et al, 2017;Amin et al, 2018;Spivey et al, 2018).…”

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confidence: 99%

Determining Optimum Irrigation Termination Periods for Cotton Production in the Texas High Plains

Ale¹,

Omani²,

Himanshu³

et al. 2020

Transactions of the ASABE

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HighlightsIrrigation water use efficiency was consistently higher under deficit irrigation as compared to full irrigation.Irrigation water use was always less than the annual allowable pumping limit under deficit irrigation.The first/second week of September was ideal for terminating irrigation under full/deficit irrigation in normal years.Ideal irrigation termination periods in wet/dry years were a week earlier/later than those in normal years.Abstract. Cotton ( L.) production in the Texas High Plains (THP) region relies heavily on irrigation with groundwater from the underlying Ogallala Aquifer. However, rapidly declining groundwater levels in the aquifer and increasing pumping costs pose challenges for sustainability of irrigated cotton production in this region. Adoption of efficient irrigation strategies, such as terminating irrigation at an appropriate time in the growing season, could enable producers to increase irrigation water use efficiency (IWUE) while maintaining desired yield goals. The objective of this study was to determine optimum irrigation termination periods for cotton production in the THP under full and deficit irrigation conditions using the Decision Support System for Agrotechnology Transfer (DSSAT) CROPGRO-Cotton model, which was evaluated in a prior study in the THP using measured data from an IWUE field experiment at Halfway, Texas. The treatment factors in the field experiment included irrigation capacities of 0 mm d-1 (low, L), 3.2 mm d-1 (medium, M), and 6.4 mm d-1 (high, H), applied during the vegetative, reproductive, and maturation growth stages. This study focused on a full irrigation (HHH) treatment and three deficit irrigation (LMH, LHM, and LMM) treatments. Eight irrigation termination dates with a one-week interval between 15 August and 30 September were simulated, and the impact of irrigation termination date on cotton IWUE and seed cotton yield were studied by dividing the 39-year (1978 to 2016) simulation period into dry, normal, and wet years based on the precipitation received from 1 April to the simulated irrigation termination date. Results indicated that the simulated IWUE was consistently higher under the LHM, LMH, and LMM treatments when compared to the HHH treatment. Based on the simulated average seed cotton yield and IWUE, optimum irrigation termination periods for cotton were found to be the first week of September (about 118 days after planting, DAP) for the HHH and LMH treatments and the second week of September (125 DAP) for the LHM and LMM treatments in normal years. In wet years, optimum irrigation termination periods were a week earlier than those in normal years and a week later in dry years for the HHH, LHM, and LMM treatments. For the LMH treatment, the optimum irrigation termination period in wet years was the same as that in normal years and two weeks later in dry years. The results from this study along with field-specific, late-season information will assist THP cotton producers in making appropriate irrigation termination decisions for improving economic productivity of the Ogallala Aquifer and thereby ensuring water security for agriculture. However, the recommendations from this study should be used with caution, as the optimum irrigation termination periods could potentially change with changes in cultivar characteristics, soil type, climate, and, crop management practices. Keywords: CROPGRO-Cotton, Deficit irrigation, DSSAT, Full irrigation, Irrigation water use efficiency, Seed cotton yield.

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“…It might be due to an extension in the growing and grain filling period when planted earlier than the current planting date as mentioned by Lv et al (2019). Amin et al (2018) recommend the moderate use of fertilizer doses to…”

Section: Discussionmentioning

confidence: 99%

Climate change impact uncertainty assessment and adaptations for sustainable maize production using multi-crop and climate models

Yasin

Ahmad²,

Khaliq

et al. 2021

Environ Sci Pollut Res

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Future climate scenarios are predicting considerable threats to sustainable maize production in arid and semi-arid regions. These adverse impacts can be minimized by adopting modern agricultural tools to assess and develop successful adaptation practices. A multi-model approach (climate and crop) was used to assess the impacts and uncertainties of climate change on maize crop. An extensive field study was conducted to explore the temporal thermal variations on maize hybrids grown at farmer’s fields for ten sowing dates during two consecutive growing years. Data about phenology, morphology, biomass development, and yield were recorded by adopting standard procedures and protocols. The CSM-CERES, APSIM, and CSM-IXIM-Maize models were calibrated and evaluated. Five GCMs among 29 were selected based on classification into different groups and uncertainty to predict climatic changes in the future. The results predicted that there would be a rise in temperature (1.57–3.29 °C) during the maize growing season in five General Circulation Models (GCMs) by using RCP 8.5 scenarios for the mid-century (2040–2069) as compared with the baseline (1980–2015). The CERES-Maize and APSIM-Maize model showed lower root mean square error values (2.78 and 5.41), higher d-index (0.85 and 0.87) along reliable R2 (0.89 and 0.89), respectively for days to anthesis and maturity, while the CSM-IXIM-Maize model performed well for growth parameters (leaf area index, total dry matter) and yield with reasonably good statistical indices. The CSM-IXIM-Maize model performed well for all hybrids during both years whereas climate models, NorESM1-M and IPSL-CM5A-MR, showed less uncertain results for climate change impacts. Maize models along GCMs predicted a reduction in yield (8–55%) than baseline. Maize crop may face a high yield decline that could be overcome by modifying the sowing dates and fertilizer (fertigation) and heat and drought-tolerant hybrids.

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Simulated CSM-CROPGRO-cotton yield under projected future climate by SimCLIM for southern Punjab, Pakistan

Cited by 67 publications

References 31 publications

Impact of Climate Warming on Cotton Growth and Yields in China and Pakistan: A Regional Perspective

Impact of Climate Warming on Cotton Growth and Yields in China and Pakistan: A Regional Perspective

Determining Optimum Irrigation Termination Periods for Cotton Production in the Texas High Plains

Climate change impact uncertainty assessment and adaptations for sustainable maize production using multi-crop and climate models

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