Assessing the impact of climate variability on maize using simulation modeling under semi-arid environment of Punjab, Pakistan

Abstract

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, 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 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.

Modelling Impacts of Climate Change on Maize (&lt;i&gt;Zea mays&lt;/i&gt; L.) Growth and Productivity: A Review of Models, Outputs and Limitations

“…Like other crops, the main challenge that affects maize growth and development is the changing weather pattern, leading to intra-seasonal changes in yield (Lin et al, 2017). Other factors are variable soil properties, crop agronomic management practices including planting, fertilizer application, tillage among others (Ahmed et al, 2018;Lin et al, 2017;Ramirez-Cabral, Kumar, & Shabani, 2017;Tesfaye et al, 2015;Tesfaye et al, 2018). We believe that analysing the performance of various maize simulation models is vital for addressing the challenges posed by climate change.…”

Climate Smart Interventions of Small-Holder Farming Systems

“…It is alarming that 60% of population is experiencing food insecurity. The average food supply of 2440 kcal/person/day in the country is yet insufficient to meet the demand [5]. In Pakistan 62% of total energy is meet through cereals (wheat > rice > maize) after milk and vegetables in terms of calories consumed.…”

“…These elements of adaptations give sound genetic concepts as an important intervention in designing cultivar [4]. Various crop models are being used in improving natural resources to evaluate the impact of future potential climate on crop production [5]. Crop simulation models are appropriate tools for the assessment of crop production options for an environment, including inorganic fertilization levels, plant spacing, planting times and others management options [13,14].…”

“…Climate resilience improvement is in common use now a day to inform crop management options. In view of current and future climate change and variability, interest among researchers to apply such technique is increasing to strengthen the climate resilience in crops of hot and dry areas [4,5]. Climate resilience is quite resemblance to vulnerability and commonly defined as "the ability to bounce back after an external shock or stress".…”

Implications of 1.5 and 2.0 ºC additional warming for wheat yield using a gridded modeling approach

“…Climate change is threatening agriculture and food security globally ( Lipper et al, 2014;Lobell and Gourdji, 2012;Moorhead, 2009). Variations in temperature and uneven distribution of precipitation, negatively impact the crop production (Ahmed et al, 2018;Asseng et al, 2015a;Rahman et al, 2018;Ullah et al, 2019a). Future climate projections indicate that the global surface temperature is expected to increase by 0.8°C to 1.2°C between 2030 to 2052, resulting in decrease in agriculture productivity (IPCC, 2013).…”