Response of Greengram to Climate Change in Northern Transition Zone of Karnataka: DSSAT Model Based Assessment

Greengram [Vigna radiata (L.) Wilczek] is one of the important pulse crops cultivated in India since ancient times. Historically, India has been the largest global producer and consumer of greengram. In India it is cultivated on an area of about 34.55 lakh ha with production of 16.11 lakh tons and productivity of 466 kg ha^-1. It has wider adaptability due to short duration, low input requirements and the ability to fix the nitrogen in symbiotic association with rhizobia (58-109 kg N per hectare), which not only enables it to meet its own nitrogen requirement, but also benefits the succeeding crops hence well suited in sequence with rabi crops (Singh and Singh, 2011). Greengram is the main grain legume crop of Northern Transition Zone of Karnataka (NTZ) grown during kharif season both on black and red soils. The optimum average temperature for potential yield of greengram lies between 28-30°C. In Karnataka, it is cultivated on an area of 4.19 lakh hectares with a production of 1.15 lakh tonnes at an average productivity of 383 kg ha^-1 (Anonymous, 2017).
        
Climate change and agriculture are highly interrelated, both of which take place at global level with huge variation at regional and zonal level. The effect of climate on agriculture is related to both variability in local weather (i.e., changes in rainfall pattern) and global climate pattern (i.e., temperature). Rising temperature and erratic rainfall patterns projected under future climates are expected to affect the performance and productivity of most of the crops including greengram. Therefore, it is important to study the impact of projected rise in temperature and changes in rainfall on the performance of greengram at zonal level. For such studies dynamic crop simulation models come as handy and efficient tools. The DSSAT (Decision Support System for Agrotechnology Transfer) is one such model to assess the possible impacts of climate change on agriculture.
        
Heinemann et al., (2006) from America calibrated and validated CSM-CROPGRO-Drybean model to study the impact of climate change on rain-fed common bean production systems. They considered as historic period from 1980 to 2005 and four representative concentration pathways (RCP- 2.6 and 8.5) for the near future from 2020 to 2045 for assessing the common bean yields. The results showed that climate change impacts on average simulated yield ranged from -267 to 272 and -439 to 314.2 kg ha^-1 for RCP 2.6 and 8.5. The study showed that there was an interaction between temperature and CO₂ for common bean and it is changing the yield distribution for the wet common bean season at Goias State of America. Sexton and Farris (1998) also from USA used CROPGRO model to simulate drybean growth and yield in summer condition. The model simulated results showed that dry beans would give more consistent yields, but again varies with location and variety grown. Fu et al., (2016) studied the changes in yield of soyabean in relation to combined effects of CO₂, temperature and precipitation by CROPGRO- Soybean model and observed that yield was projected to decrease under the future climate. Singh et al., (2014) investigated the impacts of climate change by using CROPGRO-Groundnut model on the productivity of groundnut at three sites (Anantapur, Mahboobnagar and Junagadh) and found that at Anantapur changes in temperature and rainfall expected by 2030 and 2050 would decrease the pod yield by 13% and 20% respectively. At Mahboobnagar for same period pod yield decreased by 8 and 11% and at Junagadh by 2 and 7%, which shows that effect of climate on crops varies from region to region hence, such studies need to be carried out for each crop at zonal level.

Description of the study area
 
The field experiment on greengram, from which the data for modeling was used, was conducted during kharif seasons of 2016 and 2018 under AICRP on MULLaRP at Main Agricultural Research Station, Dharwad, located at 15° 26¢ North latitude, 75° 07¢ East longitude and at an altitude of 678 m above mean sea level (MSL). This station comes under the Northern Transitional Zone No-8 of agro-climatic zones of Karnataka. The average annual rainfall from 1985 to 2016 period was 722.80 mm. The soil of the experimental site was deep black clay with pH 7.61, EC 0.51 dS m^-1, organic carbon content 0.59%, available N 225 kg ha^-1, P₂O₅ 19 kg ha^-1 and K₂O 322 kg ha^-1 with a total profile depth of 180 cm.
 
Source and type of experimental data
 
The data on phenology i.e., physiological maturity, grain yield, total above ground biomass and seed weight were purchased from the AICRP on MULLaRP and used for model calibration and evaluation. The data on layer wise soil profile was used to build soil module, daily weather data (Tmax, Tmin, rainfall, solar radiation) for the experiment period (2016 and 2018) was used to build weather module, crop management and input data for both the years was used to build experiment file (X-file) and data on phenology, yield and yield attributes collected during both the years of experiment was used to build time-series (T-file) and end-of-season (A-file) files within DSSAT.
 
Model calibration and validation
 
In this project greengram cultivar DGGV-2 was used. The genetic coefficients for this cultivar within DSSAT -CROPGRO model was calibrated with data collected from kharif 2016 experiment using GenCalc (Hunt et al., 1993), a semi-automated program embedded within DSSAT to optimize genetic coefficients, followed by manual method. Whereas, the data collected from kharif 2018 experiment was used for evaluation of the model. The detail on optimized coefficients for cultivar and the description of each coefficient are presented in Table 1.
 

 
Seasonal analysis to study the effect of changes in temperature and rainfall on greengram
For seasonal analysis study 32 years’ historical weather data (1985-2016) recorded from MARS, Dharwad weather observatory was collected and this period was considered as ‘current’ scenario. Three temperature scenarios and three rainfall scenarios were considered for this study. Temperature scenarios included current (actual observed during 1985-2016 with no change), +1.0°C and +2.0°C increase in both daily maximum and minimum temperature over current. Three rainfall scenarios included no change in rainfall (actual observed during 1985-2016) and -10% and -20% reduction in daily rainfall over control (i.e., no change in rainfall). Each of these scenarios was created for 32 years’ period from 1985 to 2016. A total combination of nine scenarios was created using three temperature and three rainfall scenarios (Table 2) and the calibrated and validated DSSAT- CROPGRO model was ran for greengram crop for each climate scenario for 32 years following standard production technology developed by the UAS, Dharwad for greengram in NTZ. The mean of 32 years, range and standard error were calculated for simulated outputs on days to maturity, grain yield and total biomass were presented here.
 

Climate is the most important dominating factor influencing the suitability and yield potential of a crop for a given location. That is why studies shown that more than 50 per cent of variation in crop yield are determined by climatic factors (Eghball et al., 1995). The most important climatic factors that influence growth, development and yield of crops are temperature and rainfall. Crop phenology is mainly driven by temperature; hence crop duration is affected with changes in temperature during crop growing season, whereas reduced rainfall creates moisture stress and affects physiological processes ultimately affecting yield.
 
Effect on crop maturity
 
On average, under current scenario (Sce-1) greengram takes 84 (±0.43) days for physiological maturity. With increase in temperature by 1-2°C, the days taken for maturity was reduced, on average by just 2 to 3 days (Table 3). This shows that increase in temperature reduces days to physiological maturity slightly, as the temperature range that exists currently in NTZ during kharif is quite optimum. Whereas, reduction in rainfall by 10 and 20% did not show any impact on days to maturity, which suggests that moisture stress created in this study by the reduction of daily rainfall during crop growing cycle didn’t reduce crop duration again because of optimum temperature under current climate. A hypothesized and as expected, temperature alone showed its effect on phenology and total crop duration during kharif season in NTZ (Table 3). Even in combination of +1 to 2°C temperature and -10 to 20% rainfall did not have much effect compared to rise in temperature alone.
 

 
Effect on grain yield
 
Effect of reduced rainfall amount on grain yield
 
Under Sce-1 (i.e., no change in rainfall and temperature as well) greengram recorded the highest grain yield (592.13 kg ha^-1) but when rainfall was reduced by 10 and 20% i.e., Sce-2 and Sce-3, the simulated yield was reduced to 578.13 and 535.89 kg ha^-1. This showed that 10% reduction in rainfall lowered the grain yield by 2.36% and 20% reduction in rainfall lowered the yield much more i.e., 9.50% (Table 4). This shows that effect of moisture stress on yield with increased reduction in rainfall is not linear i.e., not at constant rate and increased moisture stress has much more adverse effect on yield compared to mild stress.
 

 
Effect of rise in temperature on grain yield
 
Among different scenarios, Sce-1 (i.e., no change in temperature) as mentioned above, greengram recorded the highest grain yield of 592.13 kg ha-1, but when temperature was increased to +1°C and +2°C i.e., Sce-4 and Sce-5, the yield levels were reduced to 582 and 571.06 kg ha^-1. The simulated per cent reduction in yield was 1.71 and 3.56%, respectively (Table 4). The results are in agreement with the findings of Sinha and Swaminathan, (1991) who reported that increase in temperature resulted in an average yield loss of about 280 kg ha^-1 in rice.
 
Combined effect of reduction in rainfall and rise in temperature on grain yield
 
Under Sce-6 and Sce-8, i.e., 10% and 20% reduction in rainfall and each coupled with +1°C rise in temperature, compared with Sce-1 (current) the reduction in yield was much more than the effect of either reduced rainfall or rise in temperature alone. The simulated yield of greengram in Sce-6 and Sce-8, as compared to Sce-1 (592.13 kg ha^-1), was reduced to 567.82 and 519.12 kg ha^-1. The reduction was to an extent of 4.72 and 13.19% for Sce-6 and Sce-8 (Table 4). Similarly, under Sce-7 and Sce-9 i.e., 10% and 20% reduction in rainfall and each coupled with +2°C rise in temperature, the yield of greengram as compared to Sce-1, reduced further down to 546.2 and 495.24 kg ha^-1 respectively. The simulated per cent reduction in grain yield was 7.76 and 16.36% for Sce-7 and Sce-9. Exposure to higher temperatures leads to faster accumulation of thermal units that means fulfillment of thermal requirement without producing sufficient biomass or economic yield (Aggarwal et al., 2004; Kumar et al., 2007 and Adak et al., 2010).
 
Effect on total biomass

Effect of reduced rainfall amount on total biomass
 
Among different scenarios, Sce-1 (i.e., no change in rainfall and temperature as well) recorded the highest total biomass of 3166 kg ha^-1, but when rainfall was reduced by 10 and 20% i.e., Sce-2 and Sce-3, the simulated total biomass was reduced to 3013.12 and 2834.09 kg ha^-1. This showed that 10% reduction in rainfall lowered the total biomass by 4.83% and 20% reduction in rainfall lowered the total biomass much more i.e., 10.48% (Table 5).
 

 
Effect of rise in temperature on total biomass
 
Among different scenarios, Sce-1 (i.e., no change in temperature) as mentioned above, recorded the highest total biomass of 3166 kg ha^-1, but when temperature was increased to +1°C and +2°C i.e., Sce-4 and Sce-5, the total biomass was reduced to 3014 and 2869.51 kg ha^-1, respectively. The simulated per cent reduction in biomass was 4.80 and 9.36% (Table 5). The results are in agreement with the findings of Karande et al., (2018) who reported reduction in the above ground biomass of greengram by 11.7 to 22.2% at Anand, Gujarat.
        
Under Sce-6 and Sce-8, i.e., 10% and 20% reduction in rainfall and each coupled with +1°C rise in temperature, compared with Sce-1 (current), the reduction in total biomass was much more than the effect of either reduced rainfall or rise in temperature alone. The simulated total biomass of greengram in Sce-6 and Sce-8 as compared to Sce-1 (3166 kg ha^-1) reduced to 2860.12 and 2655.83 kg ha^-1. The reduction was to an extent of 9.66 and 16.11% for Sce-6 and Sce-8 (Table 5). Similarly, under Sce-7 and Sce-9 i.e., 10% and 20% reduction in rainfall, each coupled with +2°C rise in temperature, the total biomass of greengram, as compared to Sce-1, reduced further down to 2714.41 and 2496.22 kg ha^-1 respectively. The simulated per cent reduction in grain yield was 14.26 and 21.16% for Sce-7 and Sce-9, respectively.

The DSSAT 4.6 (CROPGRO) model based seasonal analysis study showed that greengram crop was found to be more sensitive to changes in rainfall amount than temperature alone in NTZ of Karnataka. In NTZ of Karnataka during kharif season no much diurnal variation in maximum and minimum temperature is noticed and both maximum and minimum temperature values fall well within optimum range of temperature requirement of greengram. So, increase in temperature by 1 or 2°C during kharif has shown to have minimal adverse effect on grain yield, as it slightly advances days to physiological maturity by just 2 to 3 days. With respect to rainfall, greengram is more sensitive because it is cultivated as rainfed crop in NTZ of Karnataka. So, the reduction in rainfall by 10 and 20% led to considerable decrease in grain yield by 2.36 and 9.50%, respectively and total biomass by 4.83 and 10.48%, respectively. This clearly indicates that, rainfall has pivotal role in deciding the kharif greengram yield under NTZ. Hence, in coming decade’s adaptive measures such as soil and moisture conservation practices and/or application of supplemental irrigation during long dry spells need to be followed to cope up with erratic rainfall under future climates to maintain high yield of greengram.