Impact of Climate Variability on Maize Yield in Semi-Arid Region of Tamil Nadu, India

Utilizing Machine Learning Framework to Evaluate the Effect of Climate Change on Maize and Soybean Yield

Impact of climate variability on grain yields of spring and summer maize

Air temperature is a key index reflecting climate change. Air temperature extremes are very important because they strongly influence the natural environment and societal activities. The Arctic air temperature extremes north of 60°N are investigated in the winter. Daily data from 238 stations at north of 60°N from the global summary of the day for the period 1979–2015 are used to study the trends of cold days, cold nights, warm days and warm nights during the wintertime. The results show a decreasing trend of cold days and nights (rate of–0.2 to–0.3 d/a) and an increasing trend of warm days and nights (rate of +0.2 to +0.3 d/a) in the Arctic. The mean temperature increases, which contributes to the increasing (decreasing) occurrence of warm (cold) days and nights. On the other hand, the variance at most stations decreased, leading to a reduced number of cold events. A positive AO (Arctic Oscillation) index leads to an increased (decreased) number of warm (cold) days and nights over northern Europe and western Russia and an increased (decreased) number of cold (warm) days and nights over the Bering Strait and Greenland. The lower extent of Arctic autumn sea ice leads to a decreased number of cold days and nights. The occurrences of abrupt changes are detected using the Mann-Kendall method for cold nights occurring in Canada in 1998 and for warm nights occurring in northwestern Eurasia in 1988. This abrupt change mainly resulted from the mean warming induced by south winds and an increased North Atlantic sea surface temperature.

The changes in frequency and intensity of rainfall, variation in temperature, increasing extreme weather events, and rising greenhouse gas emissions can together have a varying impact on food grain production, which then leads to significant impacts on food security in the future. The purpose of this study is to quantify how maize productivity might be affected due to climate change in Southern India. The present study examines how the projected changes to the northeast monsoon will affect maize yield in Tamil Nadu during the rabi season, which spans from September to December, by using a three-step methodology. Firstly, global climate models that accurately represent the large-scale features of the mean monsoon were chosen. Secondly, baseline and future climate data were extracted from the selected global models and the baseline data were compared with observations. Thirdly, the panel data regression model was fitted with the India Meteorological Department’s (IMD) observed climate data to generate the baseline coefficients and projected the maize production using future climate data generated from the global climate model. The Representative Concentration Pathways (RCPs) of RCP4.5 and RCP8.5 were used from two global climate model outputs, namely GFDL_CM3 and HadGEM2_CC, to predict the climate change variability on maize yields during the middle (2021–2050) and the end (2071–2100) of this century. The maize yield is predicted to increase by 3 to 5.47 per cent during the mid-century period and it varies from 7.25 to 14.53 per cent during the end of the century for the medium- (RCP4.5) and high-emission (RCP8.5) climate change scenarios. The maize grain yield increasing during the future periods indicated that the increase in rainfall and temperature during winter in Southern India reduced the possibility of a negative impact of temperature on the maize yield.

The beneficial effects of crop rotation have long been researched and recognised in the world. Research results on crop rotation in South Africa are limited. The objective of this study was to determine the effect of crop rotation with groundnut, soyabean or sunflower on the yield and yield variability of first and second year maize and sorgbum on a sandy soil in the north western Free State. The 7-year and 4-year study for maize and sorghum respectively, was managed for optimal production. The yield of first year maize after groundnut was 16% and after soyabean 12% higher than the yield of monocropped maize, while the yield of maize after sunflower was unaffected. The yield of second year maize was unaffected by crop rotation. Yield variability was slightly higher for first year maize (coefficient of variation = 54%) than for the second year and monocropped maize (coefficient of variation = 46%). However, the probability of obtaining a yield below a disaster target level with first year maize, is equal to or less than that of monocropped maize depending on the level of the disaster target yield. First year sorghum yields were unaffected by crop rotation. The yield of second year sorghum after groundnut and sunflower was lower than that of first year and monocropped sorghum. Yield variability for second year sorghum was also slightly higher than that of the first year and monocropped sorghum. Groundnut yield was higher after maize than after sorghum, while soyabean yield was higher after sorghum than after maize. The yield of sunflower was unaffected by crop rotation although the yield variability was higher after sorghum than after maize.

Extreme weather and climatic phenomena, such as heatwaves, cold waves, floods and droughts, are expected to become more common and have a significant impact on ecosystems, biodiversity, and society. Devastating disasters are mostly caused by record-breaking extreme events, which are becoming more frequent throughout the world, including Tanzania. A clear global signal of an increase in warm days and nights and a decrease in cold days and nights has been observed. The present study assessed the trends of annual extreme temperature indices during the period of 1982 to 2022 from 29 meteorological stations in which the daily minimum and maximum data were obtained from NASA/POWER. The Mann-Kendall and Sen slope estimator were employed for trend analysis calculation over the study area. The analyzed data have indicated for the most parts, the country has an increase in warm days and nights, extreme warm days and nights and a decrease in cold days and nights, extreme cold days and nights. It has been disclosed that the number of warm nights and days is on the rise, with the number of warm nights trending significantly faster than the number of warm days. The percentile-based extreme temperature indices exhibited more noticeable changes than the absolute extreme temperature indices. Specifically, 66% and 97% of stations demonstrated positive increasing trends in warm days (TX90p) and nights (TN90p), respectively. Conversely, the cold indices demonstrated 41% and 97% negative decreasing trends in TX10p and TN10p, respectively. The results are seemingly consistent with the observed temperature extreme trends in various parts of the world as indicated in IPCC reports.

This paper investigates the impact of climate variability on maize yield in the Limpopo Basin of South Africa using the generalized maximum entropy (GME) estimator and maximum entropy leuven estimator (MELE). Precipitation and temperature were used as proxies for climate variability, which were combined with traditional input variables (i.e. labour, fertilizer, seed and irrigation). Based on pseudo R-squared, we found that the GME fits the data better than MELE. In addition, increased precipitation, increased temperature and irrigation have a positive impact on yield. Furthermore, the results of the GME show that the impact of precipitation on maize yield is weaker than that of temperature. However, the impact of climate variability on maize yield could be negative if it increases temperature marginally but reduces precipitation to a very large extent simultaneously. Moreover, the impact of irrigation on yield is positive and with a higher elasticity coefficient than that of precipitation, which supposes that the present system of irrigation could mitigate the impact of reduced precipitation on yield.

This study aimed at understanding the impacts of the seasonal hydroclimatic variables on maize yield and developing of statistical crop model for future maize yield prediction over Tanzania. The food security of the country is basically determined by availability of maize. Unfortunately, agriculture over the country is mainly rain fed hence highly endangered by the detrimental consequences of climate change and variability. Observed climate data was acquired from Tanzania Meteorological Authority (TMA) and Maize yield data from Food and Agriculture Organization (FAO). The study used the Mann-Kendall test and Sen’s slope for trend and magnitude detection in minimum, maximum temperature and rainfall at the 95% confidence level. The results have shown that rainfall is decreasing over the country and especially during the growing season but increasing during short rains season. Characteristics of seasonal climatic variables, cycle during growing period were linked to maize yield, and high (low) yield was reported during anomalous wet (dry) growing seasons. This portrays seasonal dependence of maize production. Statistical crop model was built by aggregating spatial regions that have statistically significant relation with maize yield. Results show that, 58.8% of yield variance is linked to seasonal hydroclimate variability. Rainfall emerged as the dominant predictor variable for maize yield since it accounts for 44.1% of yield variance. The modeled and observed yields exhibit statistically substantial relationship (r = 0.78) hence depicting high credence of the built statistical crop model. Also, the results revealed a decreasing trend in Maize yield with further Lessing trend is projected to proceed in the future. This calls for adaptation and implementation of appropriate regional measures to raise maize production in order to feed the burgeoning human population amidst climate change.

Ridge and furrow systems with film cover increase maize yields and mitigate climate risks of cold and drought stress in continental climates

Climate variability continues to threaten agricultural productivity worldwide, especially in Togo where crop production is mostly rainfed-based. This study aims to investigate the impact of climate variables on maize yields in the Maritim region of Togo. Climatic data from 1980 to 2022 and maize yield data from 1990 to 2021 were collected. The Mann-Kendall test and Sen's slope estimator were applied to analyze the trends of climatic variables such as minimum and maximum temperatures and precipitation. Multiple linear regression analysis was used to test maize yields against historical climatic variables. The results of the study showed increasing trends in annual temperatures. The minimum temperature increased by 0.05C per year in Tabligbo and 0.03C in Lom; whereas the maximum temperature showed an increasing trend of 0.03C per year in both locations. The rainfall significantly increased (p<0.05) in Lom and Tabligbo by 2.29 and 1.6 mm per year, respectively. Any significant impact of climate variables on maize yields was not observed at all locations. A non-significant positive relationship was observed between rainfall and maize yields in Tabligbo. Likewise, a non-significant positive relationship between minimum temperature and maize yields in Tabligbo was noticed. The results of our study indicate that policymakers should actively endorse sustainable agricultural practices. This support would enhance the resilience of maize production in the face of climate variability and change, simultaneously boosting farmers' adaptive abilities and their income prospects.

Trends in the frequencies of four temperature extremes (the occurrence of warm days, cold days, warm nights and cold nights) with respect to a modulated annual cycle (MAC), and those associated exclusively with weather-intraseasonal fluctuations (WIF) in eastern China were investigated based on an updated homogenized daily maximum and minimum temperature dataset for 1960–2008. The Ensemble Empirical Mode Decomposition (EEMD) method was used to isolate the WIF, MAC, and longer-term components from the temperature series. The annual, winter and summer occurrences of warm (cold) nights were found to have increased (decreased) significantly almost everywhere, while those of warm (cold) days have increased (decreased) in northern China (north of 40°N). However, the four temperature extremes associated exclusively with WIF for winter have decreased almost everywhere, while those for summer have decreased in the north but increased in the south. These characteristics agree with changes in the amplitude of WIF. In particular, winter WIF of maximum temperature tended to weaken almost everywhere, especially in eastern coastal areas (by 10%–20%); summer WIF tended to intensify in southern China by 10%–20%. It is notable that in northern China, the occurrence of warm days has increased, even where that associated with WIF has decreased significantly. This suggests that the recent increasing frequency of warm extremes is due to a considerable rise in the mean temperature level, which surpasses the effect of the weakening weather fluctuations in northern China.

Maize (Zea Mays) is the major food crop in Kenya. Its production variation has devastating consequences on people’s basic food availability. This study will investigate the relationships between climate variability and maize yield using observed weather data from Kenya Meteorological Department and national annual maize yield data from the Ministry of Agriculture for the period 1979–2012. Mann–Kendall test was used to detect a trend in precipitation, minimum, and maximum temperature. Location-wise correlation method was performed between each climate variable and maize yield in every station. Stations which had significant correlations were aggregated to form climate indices which were used to build multiple linear regression model. The results revealed that maize yield in Kenya was significantly decreasing at a rate of 0.07 tons/ha/decade at the 95% confidence level accompanied by high inter-annual variation, while world average was increasing at a rate of 0.6 tons/ha/decade. This reduction was accredited to a significant increasing temperature and reduction in seasonal rainfall. Empirical relationship derived from multiple regression models indicates that 67.53% of yield variance was attributed to varying seasonal climate indices. Precipitation is the dominant predictor accounting to 49.73% of yield variance. There is a significant correlation of 0.78 between the modeled and observed yield hence high credibility of the statistical model. A Continuous decrease of maize yield is expected under the influence of climate change which threatens national food security if effective measures to raise maize production are not endorsed. These findings form a framework for designing policies geared towards the reduction of climate-related vulnerability in many parts of the world.

Climate variability has become one of the most pressing issues of our time, affecting various aspects of the environment, including the agriculture sector. This study examines the impact of climate variability on Ghana's maize yield for all agro-ecological zones and administrative regions in Ghana using annual data from 1992 to 2019. The study also employs the stacking ensemble learning model (SELM) in predicting the maize yield in the different regions taking random forest (RF), support vector machine (SVM), gradient boosting (GB), decision tree (DT), and linear regression (LR) as base models. The findings of the study reveal that maize production in the regions of Ghana is inconsistent, with some regions having high variability. All the climate variables considered have positive impact on maize yield, with a lesser variability of temperature in the Guinea savanna zones and a higher temperature variability in the Volta Region. Carbon dioxide (CO2) also plays a significant role in predicting maize yield across all regions of Ghana. Among the machine learning models utilized, the stacking ensemble model consistently performed better in many regions such as in the Western, Upper East, Upper West, and Greater Accra regions. These findings are important in understanding the impact of climate variability on the yield of maize in Ghana, highlighting regional disparities in maize yield in the country, and highlighting the need for advanced techniques for forecasting, which are important for further investigation and interventions for agricultural planning and decision-making on food security in Ghana.

Projecting the excess mortality due to heatwave and its characteristics under climate change, population and adaptation scenarios.

This study examines the impacts of climate variability and adaptation strategies on staple crop productivity in the Hawassa Zuria and Boricha districts of Sidama, Ethiopia. Using household survey data, multiple linear regression, instrumental variable (2SLS), and multinomial logit models were employed to analyze the relationships between climate factors, adaptation strategies, and crop productivity. Findings reveal that rainfall variation strongly correlates with Enset (r = 0.667) and maize (r = 0.654) yields, emphasizing the importance of moisture availability. Rising temperatures, especially minimum temperatures, negatively impact maize yields, with significant declines during the Belg (r = - 0.547) and Kiremt (r = - 0.508) seasons. Regression analysis shows that climate factors explain 54% of Enset and 77% of maize yield variability. Adaptation strategies such as irrigation, agroforestry, improved crop varieties, and water conservation significantly enhance yields. Improved maize and Haricot bean varieties increase productivity, while the impact on Enset is less pronounced. Fertilizer use benefits Enset and maize, whereas livestock fattening negatively affects maize due to resource competition. Adaptation adoption varies by agroecological zone, with Kolla zones favoring irrigation and water conservation. Access to climate information, agricultural extension services, markets, credit, and livestock ownership also influence adaptation decisions. To enhance food security and resilience, the study recommends promoting sustainable irrigation, increasing climate information access, and strengthening extension services. Policymakers should improve market access, support improved crop varieties and fertilizers, and educate farmers on livestock management. Integrating crop-livestock systems and prioritizing gender-inclusive policies can optimize resource use and build resilience.