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

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.

Conformance of sowing dates for maximizing heat use efficiency and seed cotton yield in arid to semi-arid cotton zone of Pakistan.

Pakistan is placed among the most vulnerable countries with relation to climate change and its impacts on agricultural productivity. Cotton is staged as the cash crop of the country and the main source of raw material for textile, oil, and feed industry. Varying environmental attributes have significant effects on the duration of vegetative and reproductive stages of cotton crop. To evaluate the potential impacts of varied temperatures regimes in different sowing times, field experiments were carried out throughout the cotton growing areas of Pakistan from Faisalabad in Central Punjab to RYK in Southern Punjab and Sakrand in Sindh to Dera Ismail Khan in Khyber Pakhtunkhwa (KPK) Province. Crop was sown on six different sowing dates starting from 1st March towards 15th May with 2-week intervals for two crop seasons (2016 and 2017). The timing of phenological events like emergence, squaring, flowering, and boll opening was recorded on calendar days and cumulative heat units (GDDs) were calculated for flowering and boll opening stages. Heat use efficiency for these sowing times was estimated. Data regarding yield-related parameters like opened bolls per plant, average boll weight, and seed cotton yield were also recorded during the study. Results revealed that duration of the growth stages was significantly affected by variation in mean thermal kinetics in varied sowing times in all four different environments. Seed cotton yield and heat use efficiency were also varied among the locations and sowing dates. The maximum seed cotton yield was recorded in Sakrand location at 15th April sowing date. The dependence of the phenological advancement on temperature and negative impacts of higher thermal stress on cotton productivity were also confirmed throughout the cotton growing zone of Pakistan.

Assessing the climate change impacts and adaptation strategies for rice production in Punjab, Pakistan.

Climate change is a serious threat to agriculture and food security. The development of adaptation measures is one of the key strategies to offset the climate change impacts on rice crop. A study was planned to develop adaptation strategies for future rice crop through simulation model. This study was consisted of six transplanting dates and three seedling densities which were tested to develop adaptations at three locations, i.e., Faisalabad, Hafizabad, and Gujranwala during the years 2015 and 2016. Climate change scenarios were developed for selected locations through statistical downscaling by selecting five general circulation models under representative concentration pathways (RCPs 8.5) for mid-century (2039-2069). The APSIM-ORYZA model was calibrated well with experimental data which showed an error of 2.35% between observed and simulated grain yield. The impact of climate change was studied by calculating the difference of baseline (1980-2010) and future yield. Model simulated results indicated that the rise in temperature will reduce the rice yield by 7.3%. To overcome this decrease in rice yield, suitable adaptation strategies were tested for mid-century. The developed adaptations, i.e., increased in plant population, nitrogen amount, and early transplanting, improved the rice yield by 8.7% under RCPs 8.5 for mid-century scenarios. Overall, this study provide better understanding of the adaptation processes for sustainable rice yield under anticipated future climate change.

Assessing climate change impacts on pearl millet under arid and semi-arid environments using CSM-CERES-Millet model.

Climate change adversely affects food security all over the world, especially in developing countries where the increasing population is confronting food insecurity and malnutrition. Crop models can assist stakeholders for assessment of climate change in current and future agricultural production systems. The aim of this study was to use of system analysis approach through CSM-CERES-Millet model to quantify climate change and its impact on pearl millet under arid and semi-arid climatic conditions of Punjab, Pakistan. Calibration and evaluation of CERES-Millet were performed with the field observations for pearl millet hybrid 86M86. Mid-century (2040-2069) climate change scenarios for representative concentration pathway (RCP) 4.5 and RCP 8.5 were generated based on an ensemble of selected five general circulation models (GCMs). The model was calibrated with optimum treatment (15-cm plant spacing and 200 kg N ha-1) using field observations on phenology, growth and grain yield. Thereafter, pearl millet cultivar was evaluated with remaining treatments of plant spacing and nitrogen during 2015 and 2016 in Faisalabad and Layyah. The CERES-Millet model was calibrated very well and predicted the grain yield with 1.14% error. Model valuation results showed that there was a close agreement between the observed and simulated values of grain yield with RMSE ranging from 172 to 193 kg ha-1. The results of future climate scenarios revealed that there would be an increase in Tmin (2.8 °C and 2.9 °C, respectively, for the semi-arid and arid environment) and Tmax (2.5 °C and 2.7 °C, respectively, for the semi-arid and arid environment) under RCP4.5. For RCP8.5, there would be an increase of 4 °C in Tmin for the semi-arid and arid environment and an increase of 3.7 °C and 3.9 °C in Tmax, respectively, for the semi-arid and arid environment. The impacts of climate changes showed that pearl millet yield would be reduced by 7 to 10% under RCPs 4.5 and 8.5 in Faisalabad and 10 to 13% in Layyah under RCP 4.5 and 8.5 for mid-century. So, CSM-CERES-Millet is a useful tool in assessing the climate change impacts.

Performance of four crop model for simulations of wheat phenology, leaf growth, biomass and yield across planting dates.

Robustness of four wheat simulation model were tested with 2-year field experiments of three cultivars across a wide range of sowing dates in two different climatic regions: Faisalabad (semi-arid) and Layyah (arid), in Punjab-Pakistan. Wheat growing season temperature ranged from -0.1°C to 43°C. The wide series of sowing dates was a unique opportunity to grow the wheat in an environment which temperatures varies from -0.1°C to 43°C. The CERES-Wheat, Nwheat, CROPSIM-Wheat and APSIM-Wheat model were calibrated against the least-stressed treatment for each wheat cultivar. Overall, the four models described performance of early, optimum and late sown wheat well, but poorly described yields of very late planting dates with associated high temperatures during grain filling. The poor accuracy of simulations of yield for extreme planting dates point to the need to improve the accuracy of model simulations at the high end of the growing temperature range, especially given the expected future increases in growing season temperature. Improvement in simulation of maximum leaf area index of wheat for all models is needed. APSIM-Wheat only poorly simulated days to maturity of very and extremely late sown wheat compared to other models. Overall, there is a need of improvement in function of models to response high temperature.

Optimizing irrigation and nitrogen for wheat through empirical modeling under semi-arid environment.

Nitrogen fertilizer availability to plants is strongly linked with water availability. Excessive or insufficient use of nitrogen can cause reduction in grain yield of wheat and environmental issues. The per capita per annum water availability in Pakistan has reduced to less than 1000 m3 and is expected to reach 800 m3 during 2025. Irrigating crops with 3 or more than 3 in. of depth without measuring volume of water is not a feasible option anymore. Water productivity and economic return of grain yield can be improved by efficient management of water and nitrogen fertilizer. A study was conducted at post-graduate agricultural research station, University of Agriculture Faisalabad, during 2012-2013 and 2013-2014 to optimize volume of water per irrigation and nitrogen application. Split plot design with three replications was used to conduct experiment; four irrigation levels (I300 = 300 mm, I240 = 240 mm, I180 = 180 mm, I120 = 120 mm for whole growing season at critical growth stages) and four nitrogen levels (N60 = 60 kg ha-1, N120 = 120 kg ha-1, N180 = 180 kg ha-1, and N240 = 240 kg ha-1) were randomized as main and sub-plot factors, respectively. The recorded data on grain yield was used to develop empirical regression models. The results based on quadratic equations and economic analysis showed 164, 162, 158, and 107 kg ha-1 nitrogen as economic optimum with I300, I240, I180, and I120 mm water, respectively, during 2012-2013. During 2013-2014, quadratic equations and economic analysis showed 165, 162, 161, and 117 kg ha-1 nitrogen as economic optimum with I300, I240, I180, and I120 mm water, respectively. The optimum irrigation level was obtained by fitting economic optimum nitrogen as function of total water. Equations predicted 253 mm as optimum irrigation water for whole growing season during 2012-2013 and 256 mm water as optimum for 2013-2014. The results also revealed that reducing irrigation from I300 to I240 mm during 2012-2013 and 2013-2014 did not reduce crop yield significantly (P < 0.01). The excessive nitrogen application ranged from 31.2 to 55.4% at N180 and N240 kg ha-1 for different levels of irrigation. It is concluded from study that irrigation and nitrogen relationship can be used for efficient management of irrigation and nitrogen and to reduce nitrogen losses. The empirical equations developed in this study can help farmers of semi-arid environment to calculate optimum level of irrigation and nitrogen for maximum economic return from wheat.