scholarly journals Evaluating the Yield Response of Maize (Zea mays L.) and Rice (Oryza sativa L.) to Future Climate Variability in The Gambia

2019 ◽  
Vol 7 (2) ◽  
pp. 11
Author(s):  
Ebrima Sonko ◽  
Sampson K. Agodzo ◽  
Philip Antwi-Agyei
Keyword(s):  
Climate Variability ◽  
Rice Yield ◽  
Oryza Sativa L ◽  
Maize Yield ◽  
Future Climate ◽  
Yield Response ◽  
Climate Data ◽  
Negative Effects ◽  
Yield Data ◽  
Maize Cultivars

Climate change and variability impact on staple food crops present a daunting challenge in the 21st century. The study assesses future climate variability on maize and rice yield over a 30-year period by comparing the outcomes under two GCM models, namely, CSIRO_RCP4.5 and NOAA_RCP4.5 of Australia’s Commonwealth Scientific and National Oceanic and Atmospheric Administration respectively. Historical climate data and yield data were used to establish correlations and then subsequently used to project future yields between 2021 and 2050. Using the average yield data for the period 1987-2016 as baseline yield data, future yield predictions for 2021-2030, 2031-2040 and 2041-2050 were then compared with the baseline data. The results showed that the future maize and rice yield would be vulnerable to climate variability with CSIRO_RCP4.5 showing increase in maize yield whilst CSIRO_RCP4.5 gives a better projection for rice yield. Furthermore, the results estimated the percentage mean yield gain for maize under CSIRO_RCP4.5 and NOAA_ RCP4.5 by about 17 %, 31 % and 48 % for the period 2021-2030, 2031-2040 and 2041-2050 respectively. Mean rice yield lossess of -23 %, -19 % and -23 % were expected for the same period respectively. The study recommended the use of improved rice and maize cultivars to offset the negative effects of climate variability in future.

scholarly journals Assessment of Impacts of Climate Change and Adaptation Measures for Maize Production in East Sikkim, India

2016 ◽  
Vol 9 (1) ◽  
pp. 15-27
Author(s):  
Proloy Deb ◽  
S. Babel
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Global Climate Model ◽  
Maize Yield ◽  
Future Climate ◽  
Yield Response ◽  
Climate Data ◽  
North East ◽  
The Impact ◽  
Impacts Of Climate Change

An investigation was carried out to assess the impacts of climate change on rainfed maize yield using a yield response to water stress model (AquaCrop) and to identify suitable adaptation options to minimize the negative impacts on maize yield in East Sikkim, North East India. Crop management and yield data was collected from the field experimental plots for calibration and validation of the model for the study area. The future climate data was developed for two IPCC emission scenarios A2 and B2 based on the global climate model HadCM3 with downscaling of climate to finer spatial resolution using the statistical downscaling model, SDSM. The impact study revealed that there is an expected reduction in maize yield of 12.8, 28.3 and 33.9% for the A2 scenario and 7.5, 19.9 and 29.9% for the B2 scenario during 2012-40, 2041-70 and 2071-99 respectively compared to the average yield simulated during the period of 1961-1990 with observed climate data. The maize yield of same variety under future climate can be maintained or improved from current level by changing planting dates, providing supplement irrigation and managing optimum nutrient.Journal of Hydrology and Meteorology, Vol. 9(1) 2015, p.15-27

scholarly journals Effect of Climate Change on Maize Yield in the Growing Season: A Case Study of the Songliao Plain Maize Belt

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2108 ◽  
Author(s):  
Ari Guna ◽  
Jiquan Zhang ◽  
Siqin Tong ◽  
Yongbin Bao ◽  
Aru Han ◽  
...  
Keyword(s):  
Global Warming ◽  
Coupled Model ◽  
Growing Season ◽  
Maize Yield ◽  
Climate Data ◽  
Yield Data ◽  
Increasing Temperature ◽  
Songliao Plain ◽  
The Relationship

Based on the 1965–2017 climate data of 18 meteorological stations in the Songliao Plain maize belt, the Coupled Model Intercomparision Project (CMIP5) data, and the 1998–2017 maize yield data, the drought change characteristics in the study area were analyzed by using the standardized precipitation evapotranspiration index (SPEI) and the Mann–Kendall mutation test; furthermore, the relationship between meteorological factors, drought index, and maize climate yield was determined. Finally, the maize climate yields under 1.5 °C and 2.0 °C global warming scenarios were predicted. The results revealed that: (1) from 1965 to 2017, the study area experienced increasing temperature, decreasing precipitation, and intensifying drought trends; (2) the yield of the study area showed a downward trend from 1998 to 2017. Furthermore, the climate yield was negatively correlated with temperature, positively correlated with precipitation, and positively correlated with SPEI-1 and SPEI-3; and (3) under the 1.5 °C and the 2.0 °C global warming scenarios, the temperature and the precipitation increased in the maize growing season. Furthermore, under the studied global warming scenarios, the yield changes predicted by multiple regression were −7.7% and −15.9%, respectively, and the yield changes predicted by one-variable regression were −12.2% and −21.8%, respectively.

scholarly journals Irrigation Requirements for Grape Crop under Climate Changes Conditions in Egypt

2013 ◽  
Vol 19 (1-2) ◽  
Author(s):  
F. A. Hashem ◽  
M. K. Hassanein ◽  
A. A. Khalil ◽  
É. Domokos-Szabolcsy ◽  
M. Fári
Keyword(s):  
Climate Changes ◽  
Reference Evapotranspiration ◽  
Future Climate ◽  
Climate Data ◽  
Irrigation Requirement ◽  
Negative Effects ◽  
The Sustainable Development ◽  
Irrigation Demand ◽  
Monteith Equation ◽  
Irrigation Requirements

The present work is mainly directed to discuss sensitivity of climate changes upon the irrigation demand for grape crop in Egypt. The Penman Monteith equation was used to calculate reference Evapotranspiration (ETo) under current and future climate for the two locations (El Menya and El Beheira). The historical climate data for ten years from (2000 – 2010) was used as current climate to calculate irrigation requirement for grape crop under Egyptian conditions. Two climate changes scenarios have been applied as changes in temperature. The first scenario supposed that increasing in temperature of 1.5°C would happen, and the second scenario supposed that increasing of 3.5°C would happen to calculate reference Evapotranspiration and irrigation requirement for future climate. The results showed that the evapotranspiration and irrigation requirement for grape crop at El Menya location higher than El Beheira location. Irrigation demand for grape plant under two climate changes scenario will increase in El Menya and El Beheira locations. El Menya location will take the highest irrigation demand under climate changes. Therefore, possible adaptation countermeasures should be developed to mitigate the negative effects of climate changes for the sustainable development of agro-ecosystems in Egypt.

scholarly journals Future climate variability impacts on potential erosion and soil organic carbon in European croplands

2014 ◽  
Vol 11 (1) ◽  
pp. 1561-1585 ◽  
Author(s):  
M. van der Velde ◽  
J. Balkovič ◽  
C. Beer ◽  
N. Khabarov ◽  
M. Kuhnert ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Climate Variability ◽  
Future Climate ◽  
Climate Data ◽  
Climate Trend ◽  
Erosion Rates ◽  
Soc Stock ◽  
The Impact ◽  
Earth System Models

Abstract. We investigate the impact of future climate variability on the potential vulnerability of soils to erosion and the consequences for soil organic carbon (SOC) in European croplands. Soil erosion is an important carbon flux not characterized in Earth System Models. We use a~European implementation of EPIC, driven by reference climate data (CNTRL), and climate data with reduced variability (REDVAR). Whether erosion regimes will change across European cropland depends on the spatial conjunction of expected changes in climate variability and physiographic conditions conducive to erosion. We isolated the effect of erosion by performing simulations with and without erosion. Median CNTRL and REDVAR erosion rates equalled 14.4 and 9.1 ton ha−1, and 19.1 and 9.7, for 1981–2010 and 2071–2100, respectively. The total amount of carbon lost from European cropland due to erosion was estimated at 769 Tg C for 1981–2010 (from a total storage of 6197 Tg C without erosion) under CNTRL climate. Climate trend impacts reduce the European cropland SOC stock by 578 Tg C without – and by 683 Tg C with erosion, from 1981 to 2100. Climate variability compounds these impacts and decreases the stock by an estimated 170 Tg without erosion and by 314 Tg C with erosion, by the end of the century. Future climate variability and erosion will thus compound impacts on SOC stocks arising from gradual climate change alone.

scholarly journals Effect of Climate Variables in Rice Yield in Nueva Ecija, Philippines

2020 ◽  
Vol 9 (1) ◽  
pp. 29-44
Author(s):  
Andro M. Enovejas ◽  
◽  
Sharmaine Maldia ◽  
Nurul Amri Komarudin ◽  
Dante Gideon K. Vergara ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Climate Variability ◽  
Minimum Temperature ◽  
Rice Yield ◽  
Cropping System ◽  
The Philippines ◽  
Climate Variables ◽  
Yield Data ◽  
Mean Temperature ◽  
Rainfed Farming

Climate variability is one of the factors that directly and greatly affect cropping system and plant yield. It is therefore very important to obtain a good understanding about climate variability or changes in the climate and the effect of these changes to clearly understand the vulnerability of food crops as well as its agronomic impacts for us to create and implement adaptive strategies to mitigate its negative effects. This study assessed the effect of climate in rice crop yield in both irrigated and rainfed ecotype farming system in Nueva Ecija Province in the Philippines using semi-annual yield data and the different climate variables such as seasonal rainfall, mean temperature, minimum temperature, and relative humidity by using empirical/statistical method through time series analysis, and correlation analysis. Results indicated that rice yield for irrigated and rainfed ecosystem type of farming in Nueva Ecija show an overall increasing trend from year 1991-2018, although there are observed decline and fluctuations in some years. The different climate variables (i.e., rainfall, temperature, and humidity) show fluctuating trends and irregularities spanning from the year 1991-2018. But it showed overall decreasing trends for relative humidity and increasing trends for rainfall, minimum temperature, and mean temperature. There are significant correlations between rice yield the all the climate variables in both irrigated and rainfed farming ecosystem types.

scholarly journals Effect of Rainfall Variability on the Maize Varieties Grown in a Changing Climate: A Case of Smallholder Farming in Hwedza, Zimbabwe

2018 ◽  
Author(s):  
Hillary Mugiyo ◽  
Tedious Mhizha ◽  
Tafadzwanashe Mabhaudhi
Keyword(s):  
Rainfall Variability ◽  
Maize Yield ◽  
Maximum Temperature ◽  
Yield Response ◽  
Climatic Data ◽  
Smallholder Farming ◽  
Yield Data ◽  
Support Tool ◽  
Maize Cultivar ◽  
Maize Varieties

Rain-fed maize production has significantly declined in Zimbabwe especially in semi-arid and arid areas causing food insecurity. Erratic rainfall received associated with mid-season dry spells largely contribute to low and variable maize yields. This study involved a survey of current farmers’ cropping practices, analyses of climatic data (daily rainfall and daily minimum and maximum temperature) of Hwedza station and simulation of maize yield response to climate change using DSSAT CERES crop growth simulation model. The climatic and maize yield data was analysed using mean correlation and regression analyses to establish relationships between rainfall characteristics and maize yield in the study area. Survey results showed that maize was the staple food grown by 100% of the farming households while 8.7% also grew sorghum. The survey concludes that 56.2% of the farmers grew short season varieties, 40.2% medium season varieties and 3.6% long season varieties. The result of the correlation analysis of climatic data and maize yield showed that number of rain days had strong positive relationship (r = 0.7) with maize yield. Non-significant yield differences (p > 0.05) between maize cultivar and planting date criteria were obtained. Highest yields were obtained under the combination of medium season maize cultivar and the DEPTH criterion in all simulations. The range of simulated district average yields of 0.4 t/ha to 1.8 t/ha formed the basis for the development of an operational decision support tool (cropping calendar).

scholarly journals Robustly forecasting maize yields in Tanzania based on climatic predictors

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rahel Laudien ◽  
Bernhard Schauberger ◽  
David Makowski ◽  
Christoph Gornott
Keyword(s):  
Agricultural Development ◽  
Mean Squared Error ◽  
Global Climate ◽  
National Level ◽  
Maize Yield ◽  
Weather Data ◽  
Efficiency Coefficient ◽  
Climate Data ◽  
Yield Data ◽  
Out Of Sample

AbstractSeasonal yield forecasts are important to support agricultural development programs and can contribute to improved food security in developing countries. Despite their importance, no operational forecasting system on sub-national level is yet in place in Tanzania. We develop a statistical maize yield forecast based on regional yield statistics in Tanzania and climatic predictors, covering the period 2009–2019. We forecast both yield anomalies and absolute yields at the sub-national scale about 6 weeks before the harvest. The forecasted yield anomalies (absolute yields) have a median Nash–Sutcliffe efficiency coefficient of 0.72 (0.79) in the out-of-sample cross validation, which corresponds to a median root mean squared error of 0.13 t/ha for absolute yields. In addition, we perform an out-of-sample variable selection and produce completely independent yield forecasts for the harvest year 2019. Our study is potentially applicable to other countries with short time series of yield data and inaccessible or low quality weather data due to the usage of only global climate data and a strict and transparent assessment of the forecasting skill.

Modelling the response of Mediterranean maize yields to projected climate change

2020 ◽  
Author(s):  
Simona Bassu ◽  
Davide Fumagalli ◽  
Andrea Toreti ◽  
Andrej Ceglar ◽  
Francesco Giunta ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Crop Yields ◽  
Regional Climate ◽  
Maize Yield ◽  
Climatic Conditions ◽  
Future Climate ◽  
Yield Response ◽  
Climate Conditions ◽  
Sowing Dates

<p>Understanding the effects of different combinations of sowing dates and choice of cultivars on maize yield is essential to develop appropriate climate change adaptation strategies. In this study, we explore the maize yield response of two models to changes in sowing dates and cultivars. In particular, we assess whether crop conditions around flowering can explain the variability of irrigated, potential crop yields across sowing dates and cultivars in Mediterranean climatic conditions where high temperatures may change the length of the grain filling period. Then, we investigate these responses under future climate projected conditions till 2060 by using Euro-CORDEX regional climate model simulations.</p><p>Main findings show that the approach based on anthesis conditions outperforms the model based on partitioning. This holds both under current and future climate conditions. Finally, both approaches agree on a warmer climate translating into lower yields (13-18%, average reduction with respect to the current climate conditions) than can only be partially offset by changes in phenology and sowing dates.</p>

scholarly journals Prediction of Maize Yield at the City Level in China Using Multi-Source Data

2021 ◽  
Vol 13 (1) ◽  
pp. 146
Author(s):  
Xinxin Chen ◽  
Lan Feng ◽  
Rui Yao ◽  
Xiaojun Wu ◽  
Jia Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Vegetation Index ◽  
Maize Yield ◽  
Machine Learning Algorithms ◽  
Shortwave Radiation ◽  
Growth Stages ◽  
Climate Data ◽  
Early Peak ◽  
The City

Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.

scholarly journals Determining future aflatoxin contamination risk scenarios for corn in Southern Georgia, USA using spatio-temporal modelling and future climate simulations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruth Kerry ◽  
Ben Ingram ◽  
Esther Garcia-Cela ◽  
Naresh Magan ◽  
Brenda V. Ortiz ◽  
...  
Keyword(s):  
Strong Association ◽  
Growth Period ◽  
Aflatoxin Contamination ◽  
Adaptation Strategies ◽  
Future Climate ◽  
Climate Data ◽  
Emissions Scenarios ◽  
Rcp 8.5 ◽  
Maximum Temperatures ◽  
Southern Georgia

AbstractAflatoxins (AFs) are produced by fungi in crops and can cause liver cancer. Permitted levels are legislated and batches of grain are rejected based on average concentrations. Corn grown in Southern Georgia (GA), USA, which experiences drought during the mid-silk growth period in June, is particularly susceptible to infection by Aspergillus section Flavi species which produce AFs. Previous studies showed strong association between AFs and June weather. Risk factors were developed: June maximum temperatures > 33 °C and June rainfall < 50 mm, the 30-year normals for the region. Future climate data were estimated for each year (2000–2100) and county in southern GA using the RCP 4.5 and RCP 8.5 emissions scenarios. The number of counties with June maximum temperatures > 33 °C and rainfall < 50 mm increased and then plateaued for both emissions scenarios. The percentage of years thresholds were exceeded was greater for RCP 8.5 than RCP 4.5. The spatial distribution of high-risk counties changed over time. Results suggest corn growth distribution should be changed or adaptation strategies employed like planting resistant varieties, irrigating and planting earlier. There were significantly more counties exceeding thresholds in 2010–2040 compared to 2000–2030 suggesting that adaptation strategies should be employed as soon as possible.

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