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

2020 ◽  
Author(s):  
S. Sagar Dhage ◽  
R. H. Patil
Keyword(s):  
Climate Change ◽  
Grain Yield ◽  
Transition Zone ◽  
Weather Data ◽  
Total Biomass ◽  
Rainfed Condition ◽  
Physiological Maturity ◽  
Model Based ◽  
Dssat Model ◽  
Seasonal Analysis

Background: Rise in temperature and expected changes in erratic rainfall patterns projected under future climates are going to affect the performance and productivity of most crops, especially under rainfed condition. But, extent of adverse effect would vary from location to location and crop to crop. Greengram is an important Kharif season crop of Northern Transition Zone (NTZ) of Karnataka mainly grown under rainfed conditions. Methods: Calibrated and validated DSSAT-CROPGRO model was used to study response of greengram to climate change in NTZ of Karnataka. A combination of three temperature (control, +1°C and +2°C) and three rainfall (control, -10% and -20%) scenarios resulting nine combinations were used to simulate phenology, yield and total biomass using weather data for the period of 32 years (1985-2016). Result: Model based seasonal analysis showed that the greengram is more sensitive to change in rainfall than temperature. Rise in temperature by 1-2°C, reduced days to physiological maturity by 2 to 3 days and yield by 1.7 to 3.5%. On the contrary, reduction in 20% rainfall alone reduced grain yield and total biomass by 9.5% and 10.48%, respectively. Combined effect of reduced rainfall (-20%) and elevated temperature (2°C) resulted in 16.36 and 21.16% reduction in grain yield and total biomass, respectively. This indicates that, rainfall plays greater role on kharif greengram yield in NTZ.

Short Communication: Climate change and biofuel wheat: A case study of southern Saskatchewan

2012 ◽  
Vol 92 (3) ◽  
pp. 421-425 ◽  
Author(s):  
Hong Wang ◽  
Yong He ◽  
Budong Qian ◽  
Brian McConkey ◽  
Herb Cutforth ◽  
...  
Keyword(s):  
Climate Change ◽  
Grain Yield ◽  
Short Communication ◽  
Weather Data ◽  
Yield Reduction ◽  
Climate Change Scenarios ◽  
Communication Climate ◽  
Stochastic Weather Generator ◽  
Ipcc Sres

Wang, H., He, Y., Qian, B., McConkey, B., Cutforth, H., McCaig, T., McLeod, G., Zentner, R., DePauw, R., Lemke, R., Brandt, K., Liu, T., Qin, X., White, J., Hunt, T. and Hoogenboom, G. 2012. Short Communication: Climate change and biofuel wheat: A case study of southern Saskatchewan. Can. J. Plant Sci. 92: 421–425. This study assessed potential impacts of climate change on wheat production as a biofuel crop in southern Saskatchewan, Canada. The Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) was used to simulate biomass and grain yield under three climate change scenarios (CGCM3 with the forcing scenarios of IPCC SRES A1B, A2 and B1) in the 2050s. Synthetic 300-yr weather data were generated by the AAFC stochastic weather generator for the baseline period (1961–1990) and each scenario. Compared with the baseline, precipitation is projected to increase in every month under all three scenarios except in July and August and in June for A2, when it is projected to decrease. Annual mean air temperature is projected to increase by 3.2, 3.6 and 2.7°C for A1B, A2 and B1, respectively. The model predicted increases in biomass by 28, 12 and 16% without the direct effect of CO2 and 74, 55 and 41% with combined effects (climate and CO2) for A1B, A2 and B1, respectively. Similar increases were found for grain yield. However, the occurrence of heat shock (>32°C) will increase during grain filling under the projected climate conditions and could cause severe yield reduction, which was not simulated by DSSAT-CSM. This implies that the future yield under climate scenarios might have been overestimated by DSSAT-CSM; therefore, model modification is required. Several measures, such as early seeding, must be taken to avoid heat damages and take the advantage of projected increases in temperature and precipitation in the early season.

Greengram Based Cropping Sequence for Sustainability under Changing Climate in Semi-arid Part of Karnataka, India

2021 ◽  
Author(s):  
R.H. Patil ◽  
Parashuram Kumbar ◽  
S. Sagar Dhage
Keyword(s):  
Sequential Analysis ◽  
Field Experiments ◽  
Analysis Tool ◽  
Crop Failure ◽  
Output Analysis ◽  
Rainfed Condition ◽  
Dssat Model ◽  
Different Seasons ◽  
Seasonal Analysis ◽  
Semi Arid

Background: Greengram based cropping sequences are followed in semi-arid parts of Karnataka, India. But, due to increasingly erratic and changing monsoon patterns under current climates the sustainability and profitability of these sequences are becoming more uncertain. Hence, a modeling study using seasonal analysis tool of DSSAT model was taken up to identify the most reliable sequence.Methods: Field experiments were carried out from 2015-2018 to calibrate and validate DSSAT model for four crop cultivars (greengram, chickpea, wheat and sorghum) under rainfed condition on deep black soils and then Sequential Analysis Tool of DSSAT model was run for 32 years (1985-2016) for three cropping sequences i.e., greengram-sorghum, greengram-wheat and greengram-chickpea. The simulated output analysis was done using yield, number of years crop failed during different seasons and the B:C ratio of each sequence. Result: Out of 32 years greengram crop, grown during kharif, failed only once whereas, during rabi season wheat, sorghum and chickpea failed seven, six and five years, respectively. Greengram-chickpea sequence recorded the highest B:C ratio (2.38) followed by greengram-sorghum (2.25) and greengram-wheat (1.76). Considering chances of crop failure and B:C ratio greengram-chickpea sequence was found to be the most reliable and remunerative system under rainfed condition of Karnataka during current climate.

scholarly journals Impacts of rainfall and temperature on photoperiod insensitive sorghum cultivar : model evaluation and sensitivity analysis

2021 ◽  
Vol 21 (3) ◽  
pp. 262-269
Author(s):  
F. M. AKINSEYE ◽  
A. H. FOLORUNSHO ◽  
AJEIGBE ◽  
A. HAKEEM ◽  
S. O. AGELE
Keyword(s):  
Grain Yield ◽  
Production Systems ◽  
Significant Risk ◽  
Model Performance ◽  
Farming Systems ◽  
Application Rate ◽  
Weather Data ◽  
Mean Bias Error ◽  
Bias Error ◽  
Total Biomass

A combination of local-scale climate and crop simulation model were used to investigate the impacts of change in temperature and rainfall on photoperiod insensitive sorghum in the Sudanian zone of Mali. In this study, the response of temperature and rainfall to yield patterns of photoperiod insensitive sorghum (Sorghum bicolor L. Moench) using the Agricultural Production Systems Simulator (APSIM) model was evaluated. Following model calibration of the cultivar at varying sowing dates over two growing seasons (2013 and 2014), a long-term simulation was run using historical weather data (1981-2010) to determine the impacts of temperature and rainfall on grain yield, total biomass and water use efficiency at varying nitrogen fertilizer applications. The results showed that model performance was excellent with the lowest mean bias error (MBE) of -2.2 days for flowering and 1.4 days for physiological maturity. Total biomass and grain yield were satisfactorily reproduced, indicating fairly low RMSE values of 21.3% for total biomass and very low RMSE of 11.2 % for grain yield of the observed mean. Simulations at varying Nfertilizer application rate with increased temperature of 2 °C, 4 °C and 6 °C and decreased rainfall by 25 and 50 % (W-25% and W-50%) posed a highly significant risk to low yield compared to increase in rainfall. However, the magnitude of temperature changes showed a decline in grain yield by 10%, while a decrease in rainfall by W-25% and W-50% resulted in yield decline between 5% and 37%, respectively. Thus, climate-smart site-specific utilization of the photoperiod insensitive sorghum cultivar suggests more resilient and productive farming systems for sorghum in semi-arid regions of Mali. 

scholarly journals Modeling the effect of planting date on Maize (Zea mays L.) crop in Sabour region of Bihar using DSSATv3.5 crop simulation model

MAUSAM ◽  
2021 ◽  
Vol 61 (1) ◽  
pp. 75-80
Author(s):  
P. K. SINGH ◽  
L. S. RATHORE ◽  
K. K. SINGH ◽  
A. K. BAXLA ◽  
R. K. MALL
Keyword(s):  
Grain Yield ◽  
Weather Forecast ◽  
Sowing Date ◽  
Weather Data ◽  
Maximum Temperature ◽  
Local Conditions ◽  
Medium Range Weather Forecast ◽  
Physiological Maturity ◽  
Grain Yields ◽  
Medium Range

CERES-Maize model calibrated for local conditions of Sabour has been used to evaluate the relevance medium range weather forecast relative to the maize crop growth period. The procedure is to place the reference year's daily weather into the model up to the time the yield prediction is to be made and sequences of historical data (one sequence per year) after that time until the end of growing season to give yield estimates. A procedure that makes use of historical weather data, medium range weather forecast (mrwf) and current weather data in conjunction with the CERES-Maize model was developed to arrive at a probable distribution of predicted yields. The lower temperature and more solar radiation in tassel emergence to dough stage silk emergence to physiological maturity phase and lower maximum temperature are found favorable to contribute more in increasing the grain yields. The CERES- Maize model correlated for the genetic coefficient predicts the silking dates and physiological maturity very well. Kharif maize gave the highest grain yield of 3490 kg/ha in 1999 and the lowest of 2474 kg/ha in 1979. Among eight different sowing dates the lowest average grain yield was 3190 kg/ha for the last sowing date and the highest average grain yield was 3313 kg/ha in 2nd sowing date. The 25 percentiles were less than the mean grain yields and also 75 percentiles.  

scholarly journals Simulating the Impact of Climate Change on Rice Phenology and Grain Yield in Irrigated Drylands of Central Asia

2013 ◽  
Vol 52 (9) ◽  
pp. 2033-2050 ◽  
Author(s):  
K. P. Devkota ◽  
A. M. Manschadi ◽  
M. Devkota ◽  
J. P. A. Lamers ◽  
E. Ruzibaev ◽  
...  
Keyword(s):  
Climate Change ◽  
Grain Yield ◽  
Central Asia ◽  
Growth And Yield ◽  
Weather Data ◽  
Rice Grain ◽  
Climate Change Scenarios ◽  
Rice Varieties ◽  
Daily Weather Data ◽  
The Impact

AbstractRice is the second major food crop in central Asia. Climate change may greatly affect the rice production in the region. This study quantifies the effects of projected increases in temperature and atmospheric CO2 concentration on the phenological development and grain yield of rice using the “ORYZA2000” simulation model. The model was parameterized and validated on the basis of datasets from three field experiments with three widely cultivated rice varieties under various seeding dates in the 2008–09 growing seasons in the Khorezm region of Uzbekistan. The selected rice varieties represent short-duration (SD), medium-duration (MD), and long-duration (LD) maturity types. The model was linked with historical climate data (1970–99) and temperatures and CO2 concentrations projected by the Intergovernmental Panel on Climate Change for the B1 and A1F1 scenarios for the period 2040–69 to explore rice growth and yield formation at eight emergence dates from early May to mid-July. Simulation results with historical daily weather data reveal a close relationship between seeding date and rice grain yield. Optimal emergence dates were 25 June for SD, 5 June for MD, and 26 May for LD varieties. Under both climate change scenarios, the seeding dates could be delayed by 10 days. Increased temperature and CO2 concentration resulted in higher rice grain yields. However, seeding rice before and after the optimal seeding dates reduced crop yield and yield stability significantly because of spikelet sterility induced by both high and low temperatures. As the grain yield of SD varieties could be adversely affected by climate change, rice breeding programs for central Asia should focus on developing appropriate heat-tolerant MD and LD varieties.

Application of the CERES–Wheat model to yield predictions in the irrigated plains of the Indian Punjab

1997 ◽  
Vol 129 (1) ◽  
pp. 13-18 ◽  
Author(s):  
S. S. HUNDAL ◽  
PRABHJYOT-KAUR
Keyword(s):  
Grain Yield ◽  
Kernel Weight ◽  
Growth And Yield ◽  
Total Biomass ◽  
Wheat Growth ◽  
Physiological Maturity ◽  
Grain Yields ◽  
Kernel Weights ◽  
Biomass Yields ◽  
Crop Environment

The crop–environment resource synthesis model for wheat, CERES–Wheat, was used to simulate yields from 1985 to 1993 at Ludhiana, India. The simulated anthesis and physiological maturity dates, grain and total biomass yields of wheat were compared with actual observations for the commonly grown cultivar, HD–2329. The simulated and actual dates of phenological events showed deviations from only −9 to +6 days for anthesis and −6 to +3 days for physiological maturity of the crop. The model estimated the kernel weight within 88–113% (mean 100%) of the actual kernel weights. The model predicted the grain yields from 80 to 115% (mean 97·5%) of the observed grain yield. Biomass yields were predicted from 93 to 128% (mean 110·5%) of the observed yields. The results obtained with the model for the eight crop seasons demonstrated satisfactory predictions of phenology, growth and yield of wheat. However, the biomass simulations indicated the need for further examination of the factors controlling the partitioning of photosynthates during crop growth. The results of this study reveal that the calibrated CERES–Wheat model can be used for the prediction of wheat growth and yield in the central irrigated plains of the Indian Punjab.

Winter wheat yield response to climate variability in Denmark

2010 ◽  
Vol 149 (1) ◽  
pp. 33-47 ◽  
Author(s):  
K. KRISTENSEN ◽  
K. SCHELDE ◽  
J. E. OLESEN
Keyword(s):  
Climate Change ◽  
Winter Wheat ◽  
Grain Yield ◽  
Inflection Point ◽  
Soil Types ◽  
Weather Data ◽  
Climate Projections ◽  
Climate Change Projections ◽  
Final Model ◽  
Winter Temperatures

SUMMARYData on grain yield from field trials on winter wheat under conventional farming, harvested between 1992 and 2008, were combined with daily weather data available for 44 grids covering Denmark. Nine agroclimatic indices were calculated and used for describing the relation between weather data and grain yield. These indices were calculated as average temperature, radiation and precipitation during winter (1 October–31 March), spring (1 April–15 June) and summer (16 June–31 July), and they were included as linear and quadratic covariates in a mixed regression model. The model also included an effect of year to describe the change in yield caused by unrecorded variables such as management changes. The final model included all effects that were significant for at least one of the two soil types (sandy and loamy soils). Seven of the nine agroclimatic indices were included in the final model that was used to predict the wheat grain yield under five climate scenarios (a baseline for 1985 and two climate change projections for 2020 and 2040) for two soil types and two locations in Denmark.The agroclimatic index for summer temperature showed the strongest effect causing lower yields with increasing temperature, whereas yield increased with increasing radiation during summer and spring. Winter precipitation and spring temperature did not affect grain yield significantly. Grain yield responded non-linearly to mean winter temperature with the highest yield at 4·4°C and lower yields both below and above this inflection point.The application of the model predicted that the average yield would decrease under projected climate change. The average decrease varied between 0·1 and 0·8 t/ha (comparable to a relative reduction of 1·6–12.3%) depending on the climate projection, location and soil type. On average, the grain yield decreased by about 0·25 t/ha (c. 3.6%) from 1985 to 2020 and by about 0·55 t/ha (c. 8·0%) from 1985 to 2040. The predicted yield decrease depended on climate projection and was larger for wheat grown in West Zealand than in Central Jutland and in most cases also larger for loamy soils than for sandy soils.The inter-annual variation in grain yield varied greatly between climate projections. The coefficient of variation (CV) varied between 0·16 and 0·46 and was smallest for wheat grown on loamy soils in Central Jutland in the baseline climate and largest for winter wheat grown under one of the 2040 climate projections. The increase in CV is not so much an effect of increased climatic variability under the climate change projections, but more an effect of increased winter temperature, where more extreme winter temperatures (lower or higher than the inflection point at 4·4°C) increased the effect of winter temperatures.

scholarly journals Evaluation of DSSAT model to predict sorghum yields under Conservation Agriculture in the Pandamatenga vertisols of Northern Botswana

2020 ◽  
Vol 1 (1) ◽  
pp. 027-036
Author(s):  
Kapweke Kandondi ◽  
Mpuisang Thembeka ◽  
Benedict Kayombo ◽  
Davis Samzala Marumo
Keyword(s):  
Grain Yield ◽  
Agricultural Research ◽  
Block Design ◽  
Conservation Agriculture ◽  
Weather Data ◽  
Research Station ◽  
No Tillage ◽  
Dssat Model ◽  
The Future ◽  
Sorghum Grain

This study was conducted at Pandamatenga of Chobe District in northern Botswana. The main aim of the study was to evaluate the Decision Support System for Agrotechnology Transfer (DSSAT) model in the prediction of sorghum yields under Conservation Agriculture (CA) technologies. A field experiment was conducted at the Pandamatenga Agricultural Research station during the 2015-2017 growing seasons. A randomized complete block design was used for the on-station field experimentation. The design had trial plots with four treatments, namely No Tillage (NT), No Tillage + Mulch (NT+M), Minimum Tillage (MT), and Broad Bed and Furrow (BBF), with four replicates rotated between sorghum cowpea. Sorghum grain yield results were analyzed using the Statistical Analysis Software (SAS version 9.2). Analysis of variance and means were separated using Duncan’s multiple range test at 5% confidence level. The DSSAT model was evaluated using the experimental data and weather data for the growing periods. The model was further used to test these CA technologies in terms of sorghum grain yield in the future. The DSSAT crop model provided reasonable predictions of sorghum grain yield under NT, MT, NT+M, and BBF on vertisols of Pandamatenga. The model furthermore predicted that sustained NT+M practice by smallholder rainfed farmers in Pandamatenga would increase sorghum grain yield production in the future.

scholarly journals 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 ◽  
Vol 12 (7) ◽  
pp. 927
Author(s):  
Jamshad Hussain ◽  
Tasneem Khaliq ◽  
Muhammad Habib ur Rahman ◽  
Asmat Ullah ◽  
Ishfaq Ahmed ◽  
...  
Keyword(s):  
Climate Change ◽  
Grain Yield ◽  
Arid Region ◽  
Field Experiments ◽  
Climatic Conditions ◽  
Seasonal Temperature ◽  
Sowing Dates ◽  
Rcp 8.5 ◽  
Semi Arid Region ◽  
Semi Arid

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.

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