Simulating the Impact of Climate Change on Growth and Yield of Maize Using CERES-Maize Model under Temperate Kashmir

Climate variability has been and continues to be, the principal source of fluctuations in global food production in countries of the developing world and is of serious concern. Process-based models use simplified functions to express the interactions between crop growth and the major environmental factors that affect crops (i.e., climate, soils and management), and many have been used in climate impact assessments. Average of 10 years weather data from 1985 to 2010, maximum temperature shows an increasing trend ranges from 18.5 to 20.5°C.This means there is an increase of 2°C within a span of 25 years. Decreasing trend was observed with respect to precipitation was observed with the same data. The magnitude of decrease was from 925 mm to 650 mm of rainfall which is almost decrease of 275 mm of rainfall in 25 years. Future climate for 2011-2090 from A1B scenario extracted from PRECIS run shows that overall maximum and minimum temperature increase by 5.39°C (±1.76) and 5.08°C (±1.37) also precipitation will decrease by 3094.72 mm to 2578.53 (±422.12) The objective of this study was to investigate the effects of climate variability and change on maize growth and yield of Srinagar Kashmir. Two enhanced levels of temperature (maximum and minimum by 2 and 4°C) and CO2 enhanced by 100 ppm & 200 ppm were used in this study with total combinations of 9 with one normal condition. Elevation of maximum and minimum temperature by 4°C anthesis and maturity of maize was earlier 14 days with a deviation of 18% and 26 days with a deviation of 20% respectively. Increase in temperature by 2 to 4°C alone or in combination with enhanced levels of CO2 by 100 and 200 ppm the growth and yield of maize was drastically declined with an reduction of about 40% in grain yield. Alone enhancement of CO2 at both the levels fails show any significant impact on maize yield.

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Studying Yield and Water Productivity of Maize at Enhanced Level of Temperature Using DSSAT 4.7.5

Current Journal of Applied Science and Technology ◽

10.9734/cjast/2020/v39i2630909 ◽

2020 ◽

pp. 93-104

Author(s):

B. A. Lone ◽

A. Fayaz ◽

S. Qayoom ◽

N. A. Dar ◽

Z. A. Dar ◽

...

Keyword(s):

Irrigation Water ◽

Minimum Temperature ◽

Temperature Increase ◽

Water Productivity ◽

Weather Conditions ◽

Climate Impact ◽

Maximum Temperature ◽

Economic Losses ◽

Irrigation Water Productivity ◽

Process Based Models

Climate variability has been and continues to be, the principal source of fluctuations in global food production in countries of the developing world and is of serious concern. Agriculture, with its allied sectors, is unquestionably are highly dependent on weather conditions, any weather aberrations cause atmospheric and other forms of stress and in turn, will increase the vulnerability of these farmers to economic losses. Process-based models use simplified functions to express the interactions between crop growth and the major environmental factors that affect crops (i.e., climate, soils, and management), and many have been used in climate impact assessments. The climatic scenario from A1B scenario 2011-2090 extracted from PRECIS run shows that overall maximum and minimum temperature increase by 5.39°C (±1.76) and 5.08°C (±1.37). A decrease of about 20 quintals was recorded when maximum temperature was enhanced by +4°C and about 10 quintals decreased at +2°C. Enhancement of minimum temperature by +3°C shows a decrease of about 16 quintals in tops weight. Combination of both minimum and maximum temperature remarkably decreased grain yield at (maximum & minimum +2°C) up to 25.41%. Max. temperature lead to staggering in the irrigation water productivity, however, a consistant increase in the irrigation water productivity was realised with an increase in minimum temperature. Dry matter productivity of 50 kg DM /ha/mm [ET] was observed with the increase of 1°C in both Max. and Min. temperatures and the lowest value of (16.7 kg DM /ha/mm[ET]) was recorded when the crop is supposed to grow at enhanced level maximum temperature by +4°C both maximum and minimum temperature. Increase in the both max and minimum temperature by +1°C lead to maximum irrigation water productivity of 22.4 (kg[yield]/ha/mm[irrig]) and the lowest irrigation water productivity of 16.7 (kg[yield]/ha/mm[irrig]) was registerd when both max. as well as min. temp. was raised by +4°C minimum temperature.

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Evaluation of Future Climate and Potential Impact on Streamflow in the Upper Nan River Basin of Northern Thailand

Advances in Meteorology ◽

10.1155/2020/8881118 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Miyuru B. Gunathilake ◽

Yasasna V. Amaratunga ◽

Anushka Perera ◽

Imiya M. Chathuranika ◽

Anura S. Gunathilake ◽

...

Keyword(s):

River Basin ◽

Minimum Temperature ◽

Climate Models ◽

Hydrological Modeling ◽

Winter Season ◽

Future Climate ◽

Maximum Temperature ◽

Monthly Precipitation ◽

Northern Thailand ◽

The Impact

Water resources in Northern Thailand have been less explored with regard to the impact on hydrology that the future climate would have. For this study, three regional climate models (RCMs) from the Coordinated Regional Downscaling Experiment (CORDEX) of Coupled Model Intercomparison Project 5 (CMIP5) were used to project future climate of the upper Nan River basin. Future climate data of ACCESS_CCAM, MPI_ESM_CCAM, and CNRM_CCAM under Representation Concentration Pathways RCP4.5 and RCP8.5 were bias-corrected by the linear scaling method and subsequently drove the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) to simulate future streamflow. This study compared baseline (1988–2005) climate and streamflow values with future time scales during 2020–2039 (2030s), 2040–2069 (2050s), and 2070–2099 (2080s). The upper Nan River basin will become warmer in future with highest increases in the maximum temperature of 3.8°C/year for MPI_ESM and minimum temperature of 3.6°C/year for ACCESS_CCAM under RCP8.5 during 2080s. The magnitude of changes and directions in mean monthly precipitation varies, with the highest increase of 109 mm for ACESSS_CCAM under RCP 4.5 in September and highest decrease of 77 mm in July for CNRM, during 2080s. Average of RCM combinations shows that decreases will be in ranges of −5.5 to −48.9% for annual flows, −31 to −47% for rainy season flows, and −47 to −67% for winter season flows. Increases in summer seasonal flows will be between 14 and 58%. Projection of future temperature levels indicates that higher increases will be during the latter part of the 20th century, and in general, the increases in the minimum temperature will be higher than those in the maximum temperature. The results of this study will be useful for river basin planners and government agencies to develop sustainable water management strategies and adaptation options to offset negative impacts of future changes in climate. In addition, the results will also be valuable for agriculturists and hydropower planners.

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Effects of inter-annual climate variability on grape harvest timing in rainfed hilly vineyards of Piedmont (NW Italy)

10.5194/egusphere-egu2020-10563 ◽

2020 ◽

Author(s):

Danilo Rabino ◽

Marcella Biddoccu ◽

Giorgia Bagagiolo ◽

Guido Nigrelli ◽

Luca Mercalli ◽

...

Keyword(s):

Climate Variability ◽

Weather Data ◽

Maximum Temperature ◽

Data Series ◽

Positive Trend ◽

Climate Variables ◽

Climate Data ◽

Historical Series ◽

Nw Italy ◽

Level Of Significance

Historical weather data represent an extremely precious resource for agro-meteorology for studying evolutionary dynamics and for predictive purposes, to address agronomical and management choices, that have economic, social and environmental effect. The study of climatic variability and its consequences starts from the observation of variations over time and the identification of the causes, on the basis of historical series of meteorological observations. The availability of long-lasting, complete and accurate datasets is a fundamental requirement to predict and react to climate variability. Inter-annual climate changes deeply affect grapevine productive cycle determining direct impact on the onset and duration of phenological stages and, ultimately, on the grape harvest and yield. Indeed, climate variables, such as air temperature and precipitation, affect evapotranspiration rates, plant water requirements, and also the vine physiology. In this respect, the observed increase in the number of warm days poses a threat to grape quality as it creates a situation of imbalance at maturity, with respect to sugar content, acidity and phenolic and aromatic ripeness.A study was conducted to investigate the relationships between climate variables and harvest onset dates to assess the responses of grapevine under a global warming scenario. The study was carried out in the &#8220;Monferrato&#8221; area, a rainfed hillslope vine-growing area of NW Italy. In particular, the onset dates of harvest of different local wine grape varieties grown in the Vezzolano Experimental Farm (CNR-IMAMOTER) and in surrounding vineyards (affiliated to the Terre dei Santi Cellars) were recorded from 1962 to 2019 and then related to historical series of climate data by means of regression analysis. The linear regression was performed based on the averages of maximum and minimum daily temperatures and sum of precipitation (1962&#8211;2019) calculated for growing and ripening season, together with a bioclimatic heat index for vineyards, the Huglin index. The climate data were obtained from two data series collected in the Experimental farm by a mechanical weather station (1962-2002) and a second series recorded (2002-2019) by an electro-mechanical station included in Piedmont Regional Agro-meteorological Network. Finally, a third long-term continuous series covering the period from 1962 to 2019, provided by Italian Meteorological Society was considered in the analysis.The results of the study highlighted that inter-annual climate variability, with a general positive trend of temperature, significantly affects the ripening of grapes with a progressive anticipation of the harvest onset dates. In particular, all the considered variables excepted precipitation, resulted negatively correlated with the harvest onset date reaching a high level of significance (up to P< 0.001). Best results have been obtained for maximum temperature and Huglin index, especially by using the most complete dataset. The change ratios obtained using datasets including last 15 years were greater (in absolute terms) than results limited to the period 1962-2002, and also correlations have greater level of significance. The results indicated clearly the relationships between the temperature trend and the gradual anticipation of harvest and the importance of having long and continuous historical weather data series available.

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Global contribution of climate variability and trends to maize yield change in observations and crop models during 1980-2010

10.5194/egusphere-egu2020-21042 ◽

2020 ◽

Author(s):

Xiaomeng Yin ◽

Guoyong Leng

Keyword(s):

Climate Change ◽

Climate Variability ◽

Statistical Model ◽

Yield Loss ◽

Climate Change Impacts ◽

Maize Yield ◽

Climate Impacts ◽

Future Climate Change ◽

Crop Models ◽

Process Based Models

Understanding historical crop yield response to climate change is critical for projecting future climate change impacts on yields. Previous assessments rely on statistical or process-based crop models, but each has its own strength and weakness. A comprehensive comparison of climate impacts on yield between the two approaches allows for evaluation of the uncertainties in future yield projections. Here we assess the impacts of historical climate change on global maize yield for the period 1980-2010 using both statistical and process-based models, with a focus on comparing the performances between the two approaches. To allow for reasonable comparability, we develop an emulator which shares the same structure with the statistical model to mimic the behaviors of process-based models. Results show that the simulated maize yields in most of the top 10 producing countries are overestimated, when compared against FAO observations. Overall, GEPIC, EPIC-IIASA and EPIC-Boku show better performance than other models in reproducing the observed yield variations at the global scale. Climate variability explains 42.00% of yield variations in observation-based statistical model, while large discrepancy is found in crop models. Regionally, climate variability is associated with 55.0% and 52.20% of yield variations in Argentina and USA, respectively. Further analysis based on process-based model emulator shows that climate change has led to a yield loss by 1.51%-3.80% during the period 1980-1990, consistent with the estimations using the observation-based statistical model. As for the period 1991-2000, however, the observed yield loss induced by climate change is only captured by GEPIC and pDSSAT. In contrast to the observed positive climate impact for the period 2001-2010, CLM-Crop, EPIC-IIASA, GEPIC, pAPSIM, pDSSAT and PEGASUS simulated negative climate effects. The results point to the discrepancy between process-based and statistical crop models in simulating climate change impacts on maize yield, which depends on not only the regions, but also the specific time period. We suggest that more targeted efforts are required for constraining the uncertainties of both statistical and process-based crop models for future yield predictions.&#160;

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Relationship of weather and plant factors to alfalfa bloat in autumn

Canadian Journal of Animal Science ◽

10.4141/cjas91-101 ◽

1991 ◽

Vol 71 (3) ◽

pp. 861-866 ◽

Cited By ~ 3

Author(s):

J. W. Hall ◽

W. Majak

Keyword(s):

Key Words ◽

Total Nitrogen ◽

Minimum Temperature ◽

Dry Matter ◽

Extreme Case ◽

Weather Data ◽

Maximum Temperature ◽

Soluble Nitrogen ◽

Acid Detergent Fiber ◽

Relationship Of

The bloat status of cattle was recorded in the autumns of 6 yr when bloat occurred during the decade 1979–1988. Weather data were available for all 6 yr, plant dry matter, acid detergent fiber and plant chlorophyll for 3 yr and plant total nitrogen and soluble nitrogen for 4 yr. The percentages of dry matter and acid detergent fiber were lower and the concentrations of chlorophyll, total nitrogen and soluble nitrogen were higher on days when bloat occurred than when it did not. There was no difference in minimum temperature classified by bloat status on the same day, or in maximum temperature, hours of sunshine or precipitation classified by bloat status on the next day. Hours of sunshine and the temperature range were greater on days when bloat occurred. Bloat was observed after "killing frosts" of −2.2 °C in all years and in an extreme case after a daily minimum of −9.6 °C. Key words: Legume, bloat, alfalfa, cattle, climate

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Evaluation of the impacts of climate variability on rainfed maize production over the Wami-Ruvu basin of Tanzania

Journal of Water and Climate Change ◽

10.2166/wcc.2017.036 ◽

2017 ◽

Vol 9 (1) ◽

pp. 207-222 ◽

Cited By ~ 1

Author(s):

Philbert Luhunga

Keyword(s):

Climate Variability ◽

Climate Models ◽

Pearson Correlation ◽

Regional Climate ◽

Maximum Temperature ◽

Climate Projections ◽

Historical Climate ◽

Climate Simulations ◽

Maize Yields ◽

The Impact

AbstractIn this study, the impact of inter-seasonal climate variability on rainfed maize (Zea mays) production over the Wami-Ruvu basin of Tanzania is evaluated. Daily high-resolution climate simulations from the Coordinated Regional Climate Downscaling Experiment_Regional Climate Models (CORDEX_RCMs) are used to drive the Decision Support System for Agro-technological Transfer (DSSAT) to simulate maize yields. Climate simulations for the base period of 35 years (1971–2005) are used to drive DSSAT to simulate maize yields during the historical climate. On the other hand, climate projections for the period 2010–2039 (current), 2040–2069 (mid), and 2070–2099 centuries for two Representative Concentration Pathway (RCP45 and 85) emission scenarios are used to drive DSSAT to simulate maize yields in respective centuries. Statistical approaches based on Pearson correlation coefficient and the coefficients of determination are used in the analysis. Results show that rainfall, maximum temperature, and solar radiation are the most important climate variables that determine variation in rainfed maize yields over the Wami-Ruvu basin of Tanzania. They explain the variability in maize yields in historical climate condition (1971–2005), present century under RCP 4.5, and mid and end centuries under both RCP 4.5 and RCP 8.5.

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Simulating the Impact of Climate Change on Maize Productivity in Trans-gangetic Plains using Info Crop Model

Agricultural Science Digest - A Research Journal ◽

10.18805/ag.d-5079 ◽

2020 ◽

Author(s):

Baljeet Kaur ◽

Som Pal Singh ◽

P.K. Kingra

Keyword(s):

Climate Change ◽

Maize Yield ◽

Maximum Temperature ◽

Climatic Data ◽

Data Set ◽

Gangetic Plains ◽

Impact Of Climate Change ◽

Model Average ◽

In Trans ◽

The Impact

Background: Climate change is a nonpareil threat to the food security of hundred millions of people who depends on agriculture for their livelihood. A change in climate affects agricultural production as climate and agriculture are intensely interrelated global processes. Global warming is one of such changes which is projected to have significant impacts on environment affecting agriculture. Agriculture is the mainstay economy in trans-gangetic plains of India and maize is the third most important crop after wheat and rice. Heat stress in maize cause several changes viz. morphological, anatomical and physiological and biochemical changes. Methods: In this study during 2014-2018, impact of climate change on maize yield in future scenarios was simulated using the InfoCrop model. Average maize yield from 2001-15 was collected for Punjab, Haryana and Delhi to calibrate and validate the model. Future climatic data set from 2020 to 2050 was used in the study to analyse the trends in climatic parameters.Result: Analysis of future data revealed increasing trends in maximum temperature and minimum temperature. Rainfall would likely follow the erratic behaviour in Punjab, Haryana and Delhi. Increase in temperature was predicted to have negative impact on maize yield under future climatic scenario.

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Effect of weather on growth and yield of cotton grown in the dry farming tract of peninsular India

MAUSAM ◽

10.54302/mausam.v59i3.1265 ◽

2021 ◽

Vol 59 (3) ◽

pp. 339-346

Author(s):

N. CHATTOPADHYAY ◽

R. P. SAMUI ◽

S. K. BANERJEE

Keyword(s):

Relative Humidity ◽

Minimum Temperature ◽

Meteorological Parameters ◽

Growth And Yield ◽

Maximum Temperature ◽

Peninsular India ◽

Development Stages ◽

Sunshine Hours ◽

Dry Farming

In the present study the effect of meteorological parameters on cotton growth at three different stations in the dry farming tract of peninsular India were studied critically. Increase in minimum temperature (above normal) particularly at vegetative and flowering stages favoured the yield of three varieties of cotton (AHH - 468, MCU - 9 and MCU - 10) under study. Decrease in maximum temperature at flowering and boll development stages was found to be conducive for the higher yield of AHH – 468 variety of cotton at Akola. In general, relative humidity was positively correlated with the yield of AHH – 468 varieties at Akola and MCU – 10 varieties at Kovilpatti. Lower values of bright sunshine hours (<5 hours) during vegetative and flowering were found to be helpful for increased yield of cotton at Akola. Rainfall at the beginning of the season favoured the yield of the crop.

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Spring Weather and COVID-19 Deaths in the U.S.

10.1101/2020.06.20.20136259 ◽

2020 ◽

Author(s):

Seyed M. Karimi ◽

Mahdi Majbouri ◽

Kelsey White ◽

Bert Little ◽

W. Paul McKinney ◽

...

Keyword(s):

Ultraviolet Light ◽

Minimum Temperature ◽

Robust Regression ◽

Fine Particulate Matter ◽

State Level ◽

Maximum Temperature ◽

Large Set ◽

Death Rates ◽

Average Minimum Temperature ◽

The Impact

AbstractThis study used statistically robust regression models to control for a large set of confounders (including county-level time-invariant factors and time trends, regional-level daily variation, state-level social distancing measures, ultraviolet light, and levels of ozone and fine particulate matter, PM2.5) to estimate a reliable rather than simple regression for the impact of weather on the most accurately measured outcome of COVID-19, death. When the average minimum temperature within a five-day window increased by one degree Fahrenheit in spring 2020, daily death rates in northern U.S. counties increased by an estimated 5.1%. When ozone concentration over a five-day window rose by one part per billion, daily death rates in southern U.S. counties declined by approximately 2.0%. Maximum temperature, precipitation, PM2.5, and ultraviolet light did not significantly associate with COVID-19 mortality. The mechanism that may drive the observed association of minimum temperature on COVID-19 deaths in spring months may be increased mobility and contacts. The effect of ozone may be related to its disinfectant properties, but this requires further confirmation.

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A Comparative Study of Ensemble Classifiers for Paddy Blast Disease Prediction Model

International Journal on Recent and Innovation Trends in Computing and Communication ◽

10.17762/ijritcc.v8i7.5435 ◽

2020 ◽

Vol 8 (7) ◽

pp. 09-17

Author(s):

Varsha M., Dr. Poornima B.

Keyword(s):

Prediction Model ◽

Minimum Temperature ◽

Blast Disease ◽

Weather Data ◽

Maximum Temperature ◽

Research Station ◽

Disease Prediction ◽

Features Selection ◽

Ensemble Classifiers ◽

Epidemic Disease

Paddy blast has become most epidemic disease in many rice growing countries. Various statistical methods have been used for the prediction of paddy blast but previously used methods failed in predicting diseases with good accuracy. However the need to develop new model that considers both weather factors and non weather data called blast disease data that influences paddy disease to grow. Given this point we developed ensemble classifier based paddy disease prediction model taking weather data from January 2013 to December 2019 from Agricultural and Horticulture Research Station Kathalgere Davangere District. For the predictive model we collected 7 kinds of weather data and 7 kinds of disease related data that includes Minimum Temperature, Maximum Temperature, Temperautre Difference,Relative Humidity, Stages of Paddy Cultivation, Varities of seeds, Season of cropping and so on. It is observed and analyzed that Minimum Temperature, Humidity and Rainfall has huge correlation with occurrence of disease. Since some of the variables are non numeric to convert them to numeric data one hot encoding approach is followed and to improve efficiency of ensemble classifiers 4 different filter based features selection methods are used such as Pearson’s correlation, Mutual information, ANNOVA F Value, Chi Square. Three different ensemble classifiers are used as predictive models and classifiers are compared it is observed that Bagging ensemble technique has achieved accuracy of 98% compared to Adaboost of 97% and Voting classifier of 88%. Other classification metrics are used evaluate different classifiers like precision, recall, F1 Score, ROC and precision recall score. Our proposed ensemble classifers for paddy blast disease prediction has achieved high precision and high recall but when the solutions of model are closely looked bagging classifier is better compared to other ensemble classifers that are proposed in predicting paddy blast disease.

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