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

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.

A simulation model of kenaf for assisting fibre industry planning in northern Australia. V. Impact of different crop traits

1993 ◽  
Vol 44 (4) ◽  
pp. 731 ◽  
Author(s):  
RC Muchow ◽  
PS Carberry
Keyword(s):  
Simulation Model ◽  
Plant Breeding ◽  
Plant Traits ◽  
Rainfall Variability ◽  
Crop Improvement ◽  
Yield Potential ◽  
Yield Response ◽  
Climatic Data ◽  
Transpiration Efficiency ◽  
Growth Simulation

The production potential of rainfed kenaf in the Northern Territory (NT) (latitude 12-15�S.) has been assessed using a growth simulation model for the cultivar Guatemala 4. However, this raises the important question of how well-suited is this cultivar, and what are the likely yield gains which might be obtained by breeding or selecting a different cultivar. Answering these questions with conventional experimentation would be expensive, given the variable yield response among seasons associated with rainfall variability in the NT. Accordingly, the kenaf growth simulation model NTKENAF was used in conjunction with long-term climatic data for two sites in the NT to assess the value of different plant traits relative to Guatemala 4, that are potential selection criteria in plant breeding. Extending the duration from sowing to flowering resulted in relatively small gains in stem yield over Guatemala 4, but substantial yield losses were predicted by using an earlier flowering cultivar. Increasing the efficiency of water use (higher transpiration efficiency) greatly increased yield, and was the most risk-efficient crop improvement strategy. Unfortunately, the prospects for improving transpiration efficiency of kenaf by plant breeding remain uncertain. Increasing the amount of water available for crop growth by greater extent of soil water extraction had little effect on yield in this water-limited environment. Changing the yield potential of kenaf by altering the photosynthetic capacity (higher radiation use efficiency) was risk-efficient in some situations, but the mean yield change was relatively small. It is concluded from the simulation analysis, that the standard cultivar Guatemala 4 is well-suited to the NT environment.

scholarly journals Climate Change Projection In Kashmir Valley (J&K)

2017 ◽  
Vol 12 (1) ◽  
pp. 107-115 ◽  
Author(s):  
Saqib Parvaze ◽  
Latief Ahmad ◽  
Sabah Parvaze ◽  
Raihana Kanth
Keyword(s):  
21St Century ◽  
Minimum Temperature ◽  
Decision Support Tool ◽  
Annual Precipitation ◽  
Maximum Temperature ◽  
Climatic Data ◽  
Climate Data ◽  
Kashmir Valley ◽  
Support Tool ◽  
Period Increase

The decision support tool viz. SDSM (Statistical Downscaling Model) was used to downscale climate data of future years for Kashmir province of Jammu & Kashmir state. The 21st century projected data for the A1B scenario was adjusted by using observed climatic data recorded during the period 1985-2015 for the region. The data from the same period was taken as the baseline for the analysis. This data was thereon analyzed for monthly, seasonal, cropping season and annual periods to enumerate the variation of maximum temperature, minimum temperature and precipitation in Kashmir valley of Jammu & Kashmir state in the 21st century. The modelled data obtained exhibited no significant change in maximum and minimum temperature for the period 2021-2050 but for the same period increase in annual precipitation was exhibited. For the period2051-2100, decreasing trend of annual temperature was exhibited whereas for annual precipitation, an increasing trend was exhibited.

scholarly journals Evaluating Climate Change Impact of Rainfed Maize Production Yield in Southern Ethiopia

2022 ◽  
Author(s):  
Kinde Negessa Disasa ◽  
Haofang Yan
Keyword(s):  
Climate Change ◽  
Time Horizon ◽  
Crop Production ◽  
Rainfall Variability ◽  
Maximum Temperature ◽  
Yield Response ◽  
Southern Ethiopia ◽  
Maize Production ◽  
Mitigation And Adaptation ◽  
Study Results

Abstract A developing country like Ethiopia suffers a lot from the effects of climate change due to its limited economic capability to build irrigation projects to combat climate change's impact on crop production. This study evaluates climate change's impact on rainfed maize production in the Southern part of Ethiopia. AquaCrop, developed by FAO that simulates the crop yield response to water deficit conditions, is employed to assess potential rainfed maize production in the study area with and without climate change. The Stochastic weather generators model LARS-WG of the latest version is used to simulate local-scale level climate variables based on low-resolution GCM outputs. The expected monthly percentage change of rainfall during these two-time horizons (2040 and 2060) ranges from -23.18 to 20.23% and -14.8 to 36.66 respectively. Moreover, the monthly mean of the minimum and maximum temperature are estimated to increase in the range of 1.296 0C to 2.192 0C and 0.98 0C to 1.84 0C for the first time horizon (2031-2050) and from 1.860C to 3.40C and 1.560C to 3.180C in the second time horizon (2051-2070), respectively. Maize yields are expected to increase with the range of 4.13–7% and 6.36–9.32% for the respective time horizon in the study area provided that all other parameters were kept the same. In conclusion, the study results suggest that rainfed maize yield responds positively to climate change if all field management, soil fertility, and crop variety improve were kept the same to baseline; but since there is intermodal rainfall variability among the seasons planting date should be scheduled well to combat water stress on crops. The authors believe that this study is very likely important for regional development agents (DA) and policymakers to cope up with the climate change phenomenon and take some mitigation and adaptation strategies.

scholarly journals Simulating the Response of Drought–Tolerant Maize Varieties to Nitrogen Application in Contrasting Environments in the Nigeria Savannas Using the APSIM Model

Agronomy ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 76
Author(s):  
Aloysius Beah ◽  
Alpha Y. Kamara ◽  
Jibrin M. Jibrin ◽  
Folorunso M. Akinseye ◽  
Abdullahi I. Tofa ◽  
...  
Keyword(s):  
Grain Yield ◽  
Field Experiments ◽  
Production Systems ◽  
Agricultural Practices ◽  
Net Income ◽  
Nitrogen Application ◽  
N Application ◽  
Area Index ◽  
Support Tool ◽  
Maize Varieties

This paper assessed the application of the Agricultural Production Systems sIMulator (APSIM)–maize module as a decision support tool for optimizing nitrogen application to determine yield and net return of maize production under current agricultural practices in the Nigeria savannas. The model was calibrated for two maize varieties using data from field experiments conducted under optimum conditions in three locations during the 2017 and 2018 cropping seasons. The model was evaluated using an independent dataset from an experiment conducted under different nitrogen (N) levels in two locations within Southern and Northern Guinea savannas. The results show that model accurately predicted days to 50% anthesis and physiological maturity, leaf area index (LAI), grain yield and total dry matter (TDM) of both varieties with low RMSE and RMSEn (%) values within the range of acceptable statistics indices. Based on 31-year seasonal simulation, optimum mean grain yield of 3941 kg ha−1 for Abuja, and 4549 for Kano was simulated at N rate of 120 kg ha–1 for the early maturing variety 2009EVDT. Meanwhile in Zaria, optimum mean yield of 4173 kg ha–1 was simulated at N rate of 90 kg ha−1. For the intermediate maturing variety, IWDC2SYNF2 mean optimum yields of 5152, 5462, and 4849 kg ha−1, were simulated at N application of 120 kg ha−1 for all the locations. The probability of exceeding attainable mean grain yield of 3000 and 4000 kg ha−1 for 2009EVDT and IWDC2SYNF2, respectively would be expected in 95% of the years with application of 90 kg N ha−1 across the three sites. Following the profitability scenarios analysis, the realistic net incomes of US$ 536 ha–1 for Abuja, and US$ 657 ha−1 for Zaria were estimated at N rate of 90 kg ha−1 and at Kano site, realistic net income of US$ 720 ha–1was estimated at N rate of 120 kg ha−1 for 2009EVDT.For IWDC2SYNF2, realistic net incomes of US$ 870, 974, and 818 ha−1 were estimated at N application of 120 kg ha−1 for Abuja, Zaria, and Kano respectively. The result of this study suggests that 90 kg N ha−1 can be recommended for 2009EVDT and 120 kg N ha–1 for IWDC2SYNF2 in Abuja and Zaria while in Kano, 120 kg N ha−1 should be applied to both varieties to attain optimum yield and profit.

scholarly journals Development of random forest model as decision support tool in water resources management of Ogun headwater catchments

2021 ◽  
Vol 11 (7) ◽  
Author(s):  
O. O. Aiyelokun ◽  
O. A. Agbede
Keyword(s):  
Random Forest ◽  
Water Resources ◽  
Potential Evapotranspiration ◽  
Decision Support Tool ◽  
Maximum Temperature ◽  
Computational Technique ◽  
Percentage Increase ◽  
Support Tool ◽  
Headwater Catchments ◽  
Minimal Depth

AbstractWater resources cannot be effectively managed unless potential evapotranspiration is determined with high accuracy at headwater catchments. The study presents the most suitable feature combinations for building a reliable potential evapotranspiration (PET) model in the headwater catchments of Ogun River Basin, Southwest Nigeria. Using rainfall (R), wind speed (U2), sunshine hour (S), relative humidity (Rh), minimum temperature (Tmin) and maximum temperature (Tmax) as input features, a Random Forest (RF) model was developed to predict PET. Although the model yielded satisfactory results, it was subjected to the minimal depth and percentage increase in mean square error (%IncMSE). This was done to reduce the input features and to increase model accuracy. Thereafter various combinations of important input features were examined in order to establish the best combinations required to yield optimum results. The study revealed that although Tmax (%IncMSE of 652.09, p value < 0.05) and Rh (%IncMSE of 254.36, p value < 0.05) were the most important predictors of PET, a more reliable RF model was achieved when S and U2 were combined with them. Consequently, this study presents RF with a combination of four parameters (Tmax, Rh, S and U2) as an excellent computational technique for the prediction of PET in headwater catchments.

Rainfed maize yield response to management and climate covariability at large spatial scales

2018 ◽  
Vol 256-257 ◽  
pp. 242-252 ◽  
Author(s):  
Elizabeth K. Carter ◽  
Jeff Melkonian ◽  
Scott Steinschneider ◽  
Susan J. Riha
Keyword(s):  
Spatial Scales ◽  
Maize Yield ◽  
Yield Response

scholarly journals Identifying maize yield and precipitation gaps in Uganda

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Terence Epule Epule ◽  
Driss Dhiba ◽  
Daniel Etongo ◽  
Changhui Peng ◽  
Laurent Lepage
Keyword(s):  
Cropping Systems ◽  
Growing Season ◽  
Maize Yield ◽  
Sub Saharan Africa ◽  
World Bank Group ◽  
Precipitation Data ◽  
Yield Data ◽  
Yield Gaps ◽  
Maize Yields ◽  
Growing Season Precipitation

AbstractIn sub-Saharan Africa (SSA), precipitation is an important driver of agricultural production. In Uganda, maize production is essentially rain-fed. However, due to changes in climate, projected maize yield targets have not often been met as actual observed maize yields are often below simulated/projected yields. This outcome has often been attributed to parallel gaps in precipitation. This study aims at identifying maize yield and precipitation gaps in Uganda for the period 1998–2017. Time series historical actual observed maize yield data (hg/ha/year) for the period 1998–2017 were collected from FAOSTAT. Actual observed maize growing season precipitation data were also collected from the climate portal of World Bank Group for the period 1998–2017. The simulated or projected maize yield data and the simulated or projected growing season precipitation data were simulated using a simple linear regression approach. The actual maize yield and actual growing season precipitation data were now compared with the simulated maize yield data and simulated growing season precipitation to establish the yield gaps. The results show that three key periods of maize yield gaps were observed (period one: 1998, period two: 2004–2007 and period three: 2015–2017) with parallel precipitation gaps. However, in the entire series (1998–2017), the years 2008–2009 had no yield gaps yet, precipitation gaps were observed. This implies that precipitation is not the only driver of maize yields in Uganda. In fact, this is supported by a low correlation between precipitation gaps and maize yield gaps of about 6.3%. For a better understanding of cropping systems in SSA, other potential drivers of maize yield gaps in Uganda such as soils, farm inputs, crop pests and diseases, high yielding varieties, literacy, and poverty levels should be considered.

scholarly journals Impact of Climate Change on Production and Productivity: A Case Study of Maize Research and Development In Nepal

2010 ◽  
Vol 11 ◽  
pp. 59-69 ◽  
Author(s):  
Janak Lal Nayava ◽  
Dil Bahadur Gurung
Keyword(s):  
Climate Change ◽  
Maize Yield ◽  
Maximum Temperature ◽  
Rainfall Distribution ◽  
Maize Production ◽  
Growing Seasons ◽  
Agriculture And Environment ◽  
Present Rate ◽  
Annual Maximum Temperature

The relation between climate and maize production in Nepal was studied for the period 1970/71-2007/08. Due to the topographical differences within north-south span of the country, Nepal has wide variety of climatic condition. About 70 to 90% of the rainfall occurs during summer monsoon (June to September) and the rest of the months are almost dry. Maize is cultivated from March to May depending on the rainfall distribution. Due to the availability of improved seeds, the maize yield has been steadily increasing after 1987/1988. The national area and yield of maize is estimated to be 870,166ha and 2159kg/ha respectively in 2007/08. The present rate of annual increase of temperature is 0.04°C in Nepal. Trends of temperature rise are not uniform throughout Nepal. An increase of annual temperature at Rampur during 1968-2008 was only 0.039°C. However, at Rampur during the maize growing seasons, March/April - May, the trend of annual maximum temperature had not been changed, but during the month of June and July, the trend of increase of maximum temperature was 0.03°C to 0.04°C /year.Key words: Climate-change; Global-warming; Hill; Mountain; Nepal; TaraiThe Journal of AGRICULTURE AND ENVIRONMENT Vol. 11, 2010Page: 59-69Uploaded Date: 15 September, 2010

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