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This fellowship was awarded to complete Ph.D. thesis research at the University of Florida, USA
Higher Education Commission of Pakistan
Rising temperature from climate change is the most threatening factor worldwide for crop production. Sustainable wheat production is a challenge due to climate change and variability, which is ultimately a serious threat to food security in Pakistan. A series of field experiments were conducted during seasons 2013–2014 and 2014–2015 in the semi-arid (Faisalabad) and arid (Layyah) regions of Punjab-Pakistan. Three spring wheat genotypes were evaluated under eleven sowing dates from 16 October to 16 March, with an interval of 14–16 days in the two regions. Data for the model calibration and evaluation were collected from field experiments following the standard procedures and protocols. The grain yield under future climate scenarios was simulated by using a well-calibrated CERES-wheat model included in DSSAT v4.7. Future (2051–2100) and baseline (1980–2015) climatic data were simulated using 29 global circulation models (GCMs) under representative concentration pathway (RCP) 8.5. These GCMs were distributed among five quadrants of climatic conditions (Hot/Wet, Hot/Dry, Cool/Dry, Cool/Wet, and Middle) by a stretched distribution approach based on temperature and rainfall change. A maximum of ten GCMs predicted the chances of Middle climatic conditions during the second half of the century (2051–2100). The average temperature during the wheat season in a semi-arid region and arid region would increase by 3.52 °C and 3.84 °C, respectively, under Middle climatic conditions using the RCP 8.5 scenario during the second half-century. The simulated grain yield was reduced by 23.5% in the semi-arid region and 35.45% in the arid region under Middle climatic conditions (scenario). Mean seasonal temperature (MST) of sowing dates ranged from 16 to 27.3 °C, while the mean temperature from the heading to maturity (MTHM) stage was varying between 12.9 to 30.4 °C. Coefficients of determination (R2) between wheat morphology parameters and temperature were highly significant, with a range of 0.84–0.96. Impacts of temperature on wheat sown on 15 March were found to be as severe as to exterminate the crop before heading. The spikes and spikelets were not formed under a mean seasonal temperature higher than 25.5 °C. In a nutshell, elevated temperature (3–4 °C) till the end-century can reduce grain yield by about 30% in semi-arid and arid regions of Pakistan. These findings are crucial for growers and especially for policymakers to decide on sustainable wheat production for food security in the region.
Jamshad Hussain; Tasneem Khaliq; Muhammad Rahman; Asmat Ullah; Ishfaq Ahmed; Amit Srivastava; Thomas Gaiser; Ashfaq Ahmad. Effect of Temperature on Sowing Dates of Wheat under Arid and Semi-Arid Climatic Regions and Impact Quantification of Climate Change through Mechanistic Modeling with Evidence from Field. Atmosphere 2021, 12, 927 .
AMA StyleJamshad Hussain, Tasneem Khaliq, Muhammad Rahman, Asmat Ullah, Ishfaq Ahmed, Amit Srivastava, Thomas Gaiser, Ashfaq Ahmad. Effect of Temperature on Sowing Dates of Wheat under Arid and Semi-Arid Climatic Regions and Impact Quantification of Climate Change through Mechanistic Modeling with Evidence from Field. Atmosphere. 2021; 12 (7):927.
Chicago/Turabian StyleJamshad Hussain; Tasneem Khaliq; Muhammad Rahman; Asmat Ullah; Ishfaq Ahmed; Amit Srivastava; Thomas Gaiser; Ashfaq Ahmad. 2021. "Effect of Temperature on Sowing Dates of Wheat under Arid and Semi-Arid Climatic Regions and Impact Quantification of Climate Change through Mechanistic Modeling with Evidence from Field." Atmosphere 12, no. 7: 927.
Comparing outputs of multiple climate and crop models is an option to assess the uncertainty in simulations in a changing climate. The use of multiple wheat models under five plausible future simulated climatic conditions is rarely found in literature. CERES-Wheat, DSSAT-Nwheat, CROPSIM-Wheat, and APSIM-Wheat models were calibrated with observed data form eleven sowing dates (15 October to 15 March) of irrigated wheat trails at Faisalabad, Pakistan, to explore close to real climate changing impacts and adaptations. Twenty-nine GCM of CMIP5 were used to generate future climate scenarios during 2040–2069 under RCP 8.5. These scenarios were categorized among five climatic conditions (Cool/Wet, Cool/Dry, Hot/Wet, Hot/Dry, Middle) on the basis of monthly changes in temperature and rainfall of wheat season using a stretched distribution approach (STA). The five GCM at Faisalabad and Layyah were selected and used in the wheat multimodels set to CO2 571 ppm. In the future, the temperature of both locations will elevate 2–3 °C under the five climatic conditions, although Faisalabad will be drier and Layyah will be wetter as compared with baseline conditions. Climate change impacts were quantified on wheat sown on different dates, including 1 November, 15 November, and 30 November which showed average reduction at semiarid and arid environment by 23.5%, 19.8%, and 31%, respectively. Agronomic and breeding options offset the climate change impacts and also increased simulated yield about 20% in all climatic conditions. The number of GCMs was considerably different in each quadrate of STA, showing the uncertainty in possible future climatic conditions of both locations. Uncertainty among wheat models was higher at Layyah as compared with Faisalabad. Under Hot/Dry and Hot/Wet climatic conditions, wheat models were the most uncertain to simulate impacts and adaptations. DSSAT-Nwheat and APSIM-Wheat were the most and least sensitive to changing temperature among years and climatic conditions, respectively.
Jamshad Hussain; Tasneem Khaliq; Senthold Asseng; Umer Saeed; Ashfaq Ahmad; Burhan Ahmad; Muhammad Fahad; Muhammad Awais; Asmat Ullah; Gerrit Hoogenboom. Climate change impacts and adaptations for wheat employing multiple climate and crop modelsin Pakistan. Climatic Change 2020, 163, 253 -266.
AMA StyleJamshad Hussain, Tasneem Khaliq, Senthold Asseng, Umer Saeed, Ashfaq Ahmad, Burhan Ahmad, Muhammad Fahad, Muhammad Awais, Asmat Ullah, Gerrit Hoogenboom. Climate change impacts and adaptations for wheat employing multiple climate and crop modelsin Pakistan. Climatic Change. 2020; 163 (1):253-266.
Chicago/Turabian StyleJamshad Hussain; Tasneem Khaliq; Senthold Asseng; Umer Saeed; Ashfaq Ahmad; Burhan Ahmad; Muhammad Fahad; Muhammad Awais; Asmat Ullah; Gerrit Hoogenboom. 2020. "Climate change impacts and adaptations for wheat employing multiple climate and crop modelsin Pakistan." Climatic Change 163, no. 1: 253-266.
Low planting density and deficient nitrogen application are factors that significantly decrease the yield of pearl millet in Pakistan. Optimizing their management is imperative in increasing millet production efficiency, especially with N fertilization, which can strongly affect hybrid millet response. Therefore, a field experiment was conducted at the Agronomic Research Area, University of Agriculture, Faisalabad (semi-arid) and the Agronomic Research Station, Karor Lal Eason, District Layyah (arid) over two summer seasons (2015 and 2016). The experiment consisted of three intra-row spacings (10, 15, and 20 cm) as main plots, while four nitrogen rates (0, 150, 200, and 250 kg ha−1) were randomized in subplots. The treatments were triplicated each year at both locations. The results depicted a significant change in millet crop development with a change in planting density and nitrogen rate in semi-arid and arid environments. The decrease in planting density resulted 1–2 day(s) delay in 50% flowering, milking, and maturity in semi-arid and arid region during both years of study. Higher dry matter accumulation was observed at medium planting density (15 cm intra-row spacing) and higher levels of nitrogen (250 kg ha−1) at both locations and growing seasons. The yield and attributed yield performed well with 15-cm plant spacing coupled with N application from 150–200 kg ha−1, and resulted in high nitrogen use efficiency (NUE). The results of the quadratic relationship and economic analysis linked with yield and nitrogen levels at 15-cm spacing showed 176 and 181 kg N ha−1 optimum levels (mean of years) against the economic N levels of 138 and 137 kg N ha−1 for Faisalabad and Layyah, respectively. The benefit–cost ratio (BCR) showed 31% and 45% mean excessive N at 200 and 250 kg N ha−1, in Faisalabad and Layyah, respectively. So, it is concluded that the optimum economic level of N should be sought out according to the soil and climate of an area for the production of hybrid pearl millet on a sustainable basis.
Asmat Ullah; Ishfaq Ahmad; Muhammad Ur Rahman; Muhammad Waseem; Muhammad Waqas; Muhammad Bhatti; Ashfaq Ahmad. Optimizing Management Options through Empirical Modeling to Improve Pearl Millet Production for Semi-Arid and Arid Regions of Punjab, Pakistan. Sustainability 2020, 12, 7715 .
AMA StyleAsmat Ullah, Ishfaq Ahmad, Muhammad Ur Rahman, Muhammad Waseem, Muhammad Waqas, Muhammad Bhatti, Ashfaq Ahmad. Optimizing Management Options through Empirical Modeling to Improve Pearl Millet Production for Semi-Arid and Arid Regions of Punjab, Pakistan. Sustainability. 2020; 12 (18):7715.
Chicago/Turabian StyleAsmat Ullah; Ishfaq Ahmad; Muhammad Ur Rahman; Muhammad Waseem; Muhammad Waqas; Muhammad Bhatti; Ashfaq Ahmad. 2020. "Optimizing Management Options through Empirical Modeling to Improve Pearl Millet Production for Semi-Arid and Arid Regions of Punjab, Pakistan." Sustainability 12, no. 18: 7715.
Agriculture is very vulnerable to temperature and drought in semi-arid and arid regions. Farming communities are especially vulnerable to the potential impact of climate change on crop and livestock. For Pakistan, a potential increase of 2.8°C for the maximum day temperature and 2.2°C decrease in night temperature by the mid-century has been reported. The goal of this chapter is to introduce climate-smart interventions as mitigation and adaptation strategies coupled with crop diversification through the introduction of climate resilient crops in existing cropping systems. Firstly, it describes the impacts of climate change in context to current food security situation in Pakistan and, secondly, potential climate smart interventions to combat changes in the country. Crop models, their application for developing adaptations, modeling technique and its integration with breeding, remote sensing and its application, policy interventions and resource smart interventions in context to changing climate are imperative means to favor the farming community in future farming. Introducing climate resilient crops can be rescued and recognized in dry and hot areas of Pakistan using climate smart interventions and resource use efficiency may be determined with the aid of computer and decision support IT tools in resource inefficient areas.
Asmat Ullah; Ishfaq Ahmad; Habib- Ur- Rehman; Umer Saeed; Abid Mahmood; Gerrit Hoogenboom. Climate Smart Interventions of Small-Holder Farming Systems. Climate Change and Agriculture 2019, 1 .
AMA StyleAsmat Ullah, Ishfaq Ahmad, Habib- Ur- Rehman, Umer Saeed, Abid Mahmood, Gerrit Hoogenboom. Climate Smart Interventions of Small-Holder Farming Systems. Climate Change and Agriculture. 2019; ():1.
Chicago/Turabian StyleAsmat Ullah; Ishfaq Ahmad; Habib- Ur- Rehman; Umer Saeed; Abid Mahmood; Gerrit Hoogenboom. 2019. "Climate Smart Interventions of Small-Holder Farming Systems." Climate Change and Agriculture , no. : 1.
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.
Asmat Ullah; Ashfaq Ahmad; Tasneem Khaliq; Umer Saeed; Muhammad Habib Ur Rahman; Jamshad Hussain; Shafqat Ullah; Gerrit Hoogenboom. Assessing climate change impacts on pearl millet under arid and semi-arid environments using CSM-CERES-Millet model. Environmental Science and Pollution Research 2019, 26, 6745 -6757.
AMA StyleAsmat Ullah, Ashfaq Ahmad, Tasneem Khaliq, Umer Saeed, Muhammad Habib Ur Rahman, Jamshad Hussain, Shafqat Ullah, Gerrit Hoogenboom. Assessing climate change impacts on pearl millet under arid and semi-arid environments using CSM-CERES-Millet model. Environmental Science and Pollution Research. 2019; 26 (7):6745-6757.
Chicago/Turabian StyleAsmat Ullah; Ashfaq Ahmad; Tasneem Khaliq; Umer Saeed; Muhammad Habib Ur Rahman; Jamshad Hussain; Shafqat Ullah; Gerrit Hoogenboom. 2019. "Assessing climate change impacts on pearl millet under arid and semi-arid environments using CSM-CERES-Millet model." Environmental Science and Pollution Research 26, no. 7: 6745-6757.
Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha−1, while remote sensing showed the RMSE of 397 kg ha−1. Overall strength of relationship between estimated and actual grain yields were good with R2 of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.
Ashfaq Ahmad; Umer Saeed; Muhammad Fahad; Asmat Ullah; Muhammad Habib Ur Rahman; Jasmeet Judge. Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan. Journal of the Indian Society of Remote Sensing 2018, 46, 1701 -1711.
AMA StyleAshfaq Ahmad, Umer Saeed, Muhammad Fahad, Asmat Ullah, Muhammad Habib Ur Rahman, Jasmeet Judge. Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan. Journal of the Indian Society of Remote Sensing. 2018; 46 (10):1701-1711.
Chicago/Turabian StyleAshfaq Ahmad; Umer Saeed; Muhammad Fahad; Asmat Ullah; Muhammad Habib Ur Rahman; Jasmeet Judge. 2018. "Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan." Journal of the Indian Society of Remote Sensing 46, no. 10: 1701-1711.
Climate change and variability are major threats to crop productivity. Crop models are being used worldwide for decision support system for crop management under changing climatic scenarios. Two-year field experiments were conducted at the Water Management Research Center (WMRC), University of Agriculture Faisalabad, Pakistan, to evaluate the application of CERES-Maize model for climate variability assessment under semi-arid environment. Experimental treatments included four sowing dates (27 January, 16 February, 8 March, and 28 March) with three maize hybrids (Pioneer-1543, Mosanto-DK6103, Syngenta-NK8711), adopted at farmer fields in the region. Model was calibrated with each hybrid independently using data of best sowing date (27 January) during the year 2015 and then evaluated with the data of 2016 and remaining sowing dates. Performance of model was evaluated by statistical indices. Model showed reliable information with phenological stages. Model predicted days to anthesis and maturity with lower RMSE (< 2 days) during both years. Model prediction for biological yield and grain yield were reasonably good with RMSE values of 963 and 451 kg ha−1, respectively. Model was further used to assess climate variability. Historical climate data (1980–2016) were used as input to simulate the yield for each year. Results showed that days to anthesis and maturity were negatively correlated with increase in temperature and coefficient of regression ranged from 0.63 to 0.85, while its values were 0.76 to 0.89 kg ha−1 for grain yield and biological yield, respectively. Sowing of maize hybrids (Pioneer-1543 and Mosanto-DK6103) can be recommended for the sowing on 17 January to 6 February at the farmer field for general cultivation in the region. Early sowing before 17 January should be avoided due to severe reduction in grain yield of all hybrids. A good calibrated CERES-Maize model can be used in decision-making for different management practices and assessment of climate variability in the region.
Ishfaq Ahmed; Muhammad Habib Ur Rahman; Shakeel Ahmed; Jamshad Hussain; Asmat Ullah; Jasmeet Judge. Assessing the impact of climate variability on maize using simulation modeling under semi-arid environment of Punjab, Pakistan. Environmental Science and Pollution Research 2018, 25, 28413 -28430.
AMA StyleIshfaq Ahmed, Muhammad Habib Ur Rahman, Shakeel Ahmed, Jamshad Hussain, Asmat Ullah, Jasmeet Judge. Assessing the impact of climate variability on maize using simulation modeling under semi-arid environment of Punjab, Pakistan. Environmental Science and Pollution Research. 2018; 25 (28):28413-28430.
Chicago/Turabian StyleIshfaq Ahmed; Muhammad Habib Ur Rahman; Shakeel Ahmed; Jamshad Hussain; Asmat Ullah; Jasmeet Judge. 2018. "Assessing the impact of climate variability on maize using simulation modeling under semi-arid environment of Punjab, Pakistan." Environmental Science and Pollution Research 25, no. 28: 28413-28430.
Climate change involves long term changes in climate including increase in temperature, elevated CO2 and uneven distribution of rainfall quantity and periodicity. Though daily mean temperature (Tmean) is considered a widely useful index of assessment of climate change, averaging can obscure some of the variations expected in diurnal temperature range (DTR). The aim of this study was to evaluate the effectiveness of DTR relative to Tmean as a metric for predicting millet yield using a combination of a historical dataset (1980–2010) and two climate model (MIROC5 and GFDL) projections for 2017–2046 under RCP 4.5 and 8.5 across two different environments (arid, Layyah and semi-arid, Faisalabad) of Punjab, Pakistan. Provincial datasets of pearl millet yields were collected and checked for an empirical relationship between Tmean, DTR and crop yield. The mean of projections showed increasing DTR relative to baseline in both environments. Projected Tmax and Tmin were highly correlated (0.90–0.99) for both environments and climate models. MIROC5 predicted Tmax and Tmin well and GFDL performed efficiently in predicting precipitation by 2046. The data also showed more hot days in future decades and erractic rainfall pattern by 2046 in both environments. The Genetic Algorithm (GA) appeared to be a good approach to assess climate change impact on pearl millet yield in Punjab, Pakistan, predicting negative yield impacts (11–12%) due to future warming. We suggest broadening tests of this method to other cases around the world, with similar climate regimes.
Asmat Ullah; Nasrin Salehnia; Sohrab Kolsoumi; Ashfaq Ahmad; Tasneem Khaliq. Prediction of effective climate change indicators using statistical downscaling approach and impact assessment on pearl millet (Pennisetum glaucum L.) yield through Genetic Algorithm in Punjab, Pakistan. Ecological Indicators 2018, 90, 569 -576.
AMA StyleAsmat Ullah, Nasrin Salehnia, Sohrab Kolsoumi, Ashfaq Ahmad, Tasneem Khaliq. Prediction of effective climate change indicators using statistical downscaling approach and impact assessment on pearl millet (Pennisetum glaucum L.) yield through Genetic Algorithm in Punjab, Pakistan. Ecological Indicators. 2018; 90 ():569-576.
Chicago/Turabian StyleAsmat Ullah; Nasrin Salehnia; Sohrab Kolsoumi; Ashfaq Ahmad; Tasneem Khaliq. 2018. "Prediction of effective climate change indicators using statistical downscaling approach and impact assessment on pearl millet (Pennisetum glaucum L.) yield through Genetic Algorithm in Punjab, Pakistan." Ecological Indicators 90, no. : 569-576.
Asmat Ullah; Ashfaq Ahmad; Tasneem Khaliq; Javaid Akhtar. Recognizing production options for pearl millet in Pakistan under changing climate scenarios. Journal of Integrative Agriculture 2017, 16, 762 -773.
AMA StyleAsmat Ullah, Ashfaq Ahmad, Tasneem Khaliq, Javaid Akhtar. Recognizing production options for pearl millet in Pakistan under changing climate scenarios. Journal of Integrative Agriculture. 2017; 16 (4):762-773.
Chicago/Turabian StyleAsmat Ullah; Ashfaq Ahmad; Tasneem Khaliq; Javaid Akhtar. 2017. "Recognizing production options for pearl millet in Pakistan under changing climate scenarios." Journal of Integrative Agriculture 16, no. 4: 762-773.
Asmat Ullah; Abdul Latif Malghani; Fiaz Hussain. Growth and yield response of wheat (Triticum aestivum L.) to phosphobacterial inoculation. Russian Agricultural Sciences 2012, 38, 11 -13.
AMA StyleAsmat Ullah, Abdul Latif Malghani, Fiaz Hussain. Growth and yield response of wheat (Triticum aestivum L.) to phosphobacterial inoculation. Russian Agricultural Sciences. 2012; 38 (1):11-13.
Chicago/Turabian StyleAsmat Ullah; Abdul Latif Malghani; Fiaz Hussain. 2012. "Growth and yield response of wheat (Triticum aestivum L.) to phosphobacterial inoculation." Russian Agricultural Sciences 38, no. 1: 11-13.
Shabana Nazeer; Asmat Ullah. Effect of tillage systems and farm manure on various properties of soil and nutrient’s concentration. Russian Agricultural Sciences 2011, 37, 232 -238.
AMA StyleShabana Nazeer, Asmat Ullah. Effect of tillage systems and farm manure on various properties of soil and nutrient’s concentration. Russian Agricultural Sciences. 2011; 37 (3):232-238.
Chicago/Turabian StyleShabana Nazeer; Asmat Ullah. 2011. "Effect of tillage systems and farm manure on various properties of soil and nutrient’s concentration." Russian Agricultural Sciences 37, no. 3: 232-238.