scholarly journals Effects of Climate Variability on Crop Yield and its Implications for Smallholder Farmers and Precision Agriculture in Guinea Savanna of Nigeria

2020 ◽  
pp. 1-13
Author(s):  
I. M. Sule ◽  
I. Ibrahim ◽  
J. Mayaki ◽  
S. Saidu
Keyword(s):  
Climate Variability ◽  
Crop Yield ◽  
Precision Agriculture ◽  
Crop Yields ◽  
Smallholder Farmers ◽  
The State ◽  
Maximum Temperature ◽  
Climate Data ◽  
Guinea Savanna ◽  
The Impact

This study aimed to examine the effects of climate variability on annual crop yield at smallholder farmers’ level in the Guinea Savanna Region of Nigeria, using Niger State as a case study.  Climate data (rainfall and maximum temperature) for a period of 38 years (1971-2008) was acquired from National Cereals Research Institute, Bida and Nigeria Meteorological Agency, while crop yield data was acquired from Niger State Agricultural Mechanization Development Authority (NAMDA). Focused Group Discussions (FGDs) were undertaken in 18 communities in six local government areas in Niger State spread across the three agricultural zones in the State to validate the impact of climate change and variability. The climate data was analyzed with the aid of charts. Results showed a generally rising trend in both temperature and rainfall across the State. It shows that rainfall is not only more variable, but its onset and cessation patterns have shifted and its occurrence very inconsistent. Linear relationships between climatic variables and the major crops showed moderate to strong positive and negative relationships. However, when crop yields were regressed with the climate variables, only maize (.032), bambara groundnut (.029) and groundnut (.007) were very significant at .05 confidence level (95%). The policy implication of this finding is the need to provide the farmers with local climate information and the need for vigorous pursuance of the development of high yield crop varieties better suited to changing climate conditions in the Guinea Savanna ecological zone by research institutes and other relevant agencies.

scholarly journals Modeling the Impact of Climate Changes on Crop Yield: Irrigated vs. Non-Irrigated Zones in Mississippi

2021 ◽  
Vol 13 (12) ◽  
pp. 2249
Author(s):  
Sadia Alam Shammi ◽  
Qingmin Meng
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Crop Yields ◽  
Negative Impact ◽  
Positive Impact ◽  
Information Criterion ◽  
Growing Season ◽  
Maximum Temperature ◽  
Linear Regression Models ◽  
The Impact

Climate change and its impact on agriculture are challenging issues regarding food production and food security. Many researchers have been trying to show the direct and indirect impacts of climate change on agriculture using different methods. In this study, we used linear regression models to assess the impact of climate on crop yield spatially and temporally by managing irrigated and non-irrigated crop fields. The climate data used in this study are Tmax (maximum temperature), Tmean (mean temperature), Tmin (minimum temperature), precipitation, and soybean annual yields, at county scale for Mississippi, USA, from 1980 to 2019. We fit a series of linear models that were evaluated based on statistical measurements of adjusted R-square, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). According to the statistical model evaluation, the 1980–1992 model Y[Tmax,Tmin,Precipitation]92i (BIC = 120.2) for irrigated zones and the 1993–2002 model Y[Tmax,Tmean,Precipitation]02ni (BIC = 1128.9) for non-irrigated zones showed the best fit for the 10-year period of climatic impacts on crop yields. These models showed about 2 to 7% significant negative impact of Tmax increase on the crop yield for irrigated and non-irrigated regions. Besides, the models for different agricultural districts also explained the changes of Tmax, Tmean, Tmin, and precipitation in the irrigated (adjusted R-square: 13–28%) and non-irrigated zones (adjusted R-square: 8–73%). About 2–10% negative impact of Tmax was estimated across different agricultural districts, whereas about −2 to +17% impacts of precipitation were observed for different districts. The modeling of 40-year periods of the whole state of Mississippi estimated a negative impact of Tmax (about 2.7 to 8.34%) but a positive impact of Tmean (+8.9%) on crop yield during the crop growing season, for both irrigated and non-irrigated regions. Overall, we assessed that crop yields were negatively affected (about 2–8%) by the increase of Tmax during the growing season, for both irrigated and non-irrigated zones. Both positive and negative impacts on crop yields were observed for the increases of Tmean, Tmin, and precipitation, respectively, for irrigated and non-irrigated zones. This study showed the pattern and extent of Tmax, Tmean, Tmin, and precipitation and their impacts on soybean yield at local and regional scales. The methods and the models proposed in this study could be helpful to quantify the climate change impacts on crop yields by considering irrigation conditions for different regions and periods.

scholarly journals Climate Variability and Change Affect Crops Yield under Rainfed Conditions: A Case Study in Gedaref State, Sudan

Agronomy ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1680
Author(s):  
Maysoon A. A. Osman ◽  
Joshua Orungo Onono ◽  
Lydia A. Olaka ◽  
Muna M. Elhag ◽  
Elfatih M. Abdel-Rahman
Keyword(s):  
Linear Regression ◽  
Climate Variability ◽  
Crop Yield ◽  
Rainy Season ◽  
Annual Rainfall ◽  
Maximum Temperature ◽  
Simple Linear Regression ◽  
Climate Variability And Change ◽  
Crops Yield ◽  
The Impact

It is projected that, on average, annual temperature will increase between 2 °C to 6 °C under high emission scenarios by the end of the 21st century, with serious consequences in food and nutrition security, especially within semi-arid regions of sub-Saharan Africa. This study aimed to investigate the impact of historical long-term climate (temperature and rainfall) variables on the yield of five major crops viz., sorghum, sesame, cotton, sunflower, and millet in Gedaref state, Sudan over the last 35 years. Mann–Kendall trend analysis was used to determine the existing positive or negative trends in temperature and rainfall, while simple linear regression was used to assess trends in crop yield over time. The first difference approach was used to remove the effect of non-climatic factors on crop yield. On the other hand, the standardized anomaly index was calculated to assess the variability in both rainfall and temperature over the study period (i.e., 35 years). Correlation and multiple linear regression (MLR) analyses were employed to determine the relationships between climatic variables and crops yield. Similarly, a simple linear regression was used to determine the relationship between the length of the rainy season and crop yield. The results showed that the annual maximum temperature (Tmax) increased by 0.03 °C per year between the years 1984 and 2018, while the minimum temperature (Tmin) increased by 0.05 °C per year, leading to a narrow range in diurnal temperature (DTR). In contrast, annual rainfall fluctuated with no evidence of a significant (p > 0.05) increasing or decreasing trend. The yields for all selected crops were negatively correlated with Tmin, Tmax (r ranged between −0.09 and −0.76), and DTR (r ranged between −0.10 and −0.70). However, the annual rainfall had a strong positive correlation with yield of sorghum (r = 0.64), sesame (r = 0.58), and sunflower (r = 0.75). Furthermore, the results showed that a longer rainy season had significant (p < 0.05) direct relationships with the yield of most crops, while Tmax, Tmin, DTR, and amount of rainfall explained more than 50% of the variability in the yield of sorghum (R2 = 0.70), sunflower (R2 = 0.61), and millet (R2 = 0.54). Our results call for increased awareness among different stakeholders and policymakers on the impact of climate change on crop yield, and the need to upscale adaptation measures to mitigate the negative impacts of climate variability and change.

scholarly journals Prediction of Crop Yields under a Changing Climate

2021 ◽  
Author(s):  
Godfrey Shem Juma ◽  
Festus Kelonye Beru
Keyword(s):  
Crop Yield ◽  
Crop Yields ◽  
Model Verification ◽  
Climate Variables ◽  
Climate Data ◽  
Data Quality Control ◽  
Multilinear Regression ◽  
Changing Climate ◽  
Methodological Aspects ◽  
The Impact

The impact of increasing climate variability on crop yield is now evident. Predicting the potential effects of climate change on crops prompts the use of statistical models to measure how the crop responds to climate variables. This chapter examines the usage of regression analysis in predicting crop yield under a changing climate. Data quality control is explained and application of descriptive statistics, correlation analysis and contingency tables discussed. Methodological aspects of crop yield modeling and prediction using climate variables are described. Estimation of yield via a multilinear regression approach is outlined and an overview of statistical model verification introduced. The study recommends the usage of regression models in estimating crop yield in consideration of many other externalities that can contribute to yield change.

scholarly journals Assessing the Sensitivity of Main Crop Yields to Climate Change Impacts in China

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 172
Author(s):  
Yuan Xu ◽  
Jieming Chou ◽  
Fan Yang ◽  
Mingyang Sun ◽  
Weixing Zhao ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Food Production ◽  
Crop Yields ◽  
Climatic Factors ◽  
Climatic Data ◽  
The South ◽  
The North ◽  
Output Elasticity ◽  
The Impact

Quantitatively assessing the spatial divergence of the sensitivity of crop yield to climate change is of great significance for reducing the climate change risk to food production. We use socio-economic and climatic data from 1981 to 2015 to examine how climate variability led to variation in yield, as simulated by an economy–climate model (C-D-C). The sensitivity of crop yield to the impact of climate change refers to the change in yield caused by changing climatic factors under the condition of constant non-climatic factors. An ‘output elasticity of comprehensive climate factor (CCF)’ approach determines the sensitivity, using the yields per hectare for grain, rice, wheat and maize in China’s main grain-producing areas as a case study. The results show that the CCF has a negative trend at a rate of −0.84/(10a) in the North region, while a positive trend of 0.79/(10a) is observed for the South region. Climate change promotes the ensemble increase in yields, and the contribution of agricultural labor force and total mechanical power to yields are greater, indicating that the yield in major grain-producing areas mainly depends on labor resources and the level of mechanization. However, the sensitivities to climate change of different crop yields to climate change present obvious regional differences: the sensitivity to climate change of the yield per hectare for maize in the North region was stronger than that in the South region. Therefore, the increase in the yield per hectare for maize in the North region due to the positive impacts of climate change was greater than that in the South region. In contrast, the sensitivity to climate change of the yield per hectare for rice in the South region was stronger than that in the North region. Furthermore, the sensitivity to climate change of maize per hectare yield was stronger than that of rice and wheat in the North region, and that of rice was the highest of the three crop yields in the South region. Finally, the economy–climate sensitivity zones of different crops were determined by the output elasticity of the CCF to help adapt to climate change and prevent food production risks.

scholarly journals The Pitfalls of Relating Weeds, Herbicide Use, and Crop Yield: Don't Fall Into the Trap! A Critical Review

2020 ◽  
Vol 2 ◽  
Author(s):  
Nathalie Colbach ◽  
Sandrine Petit ◽  
Bruno Chauvel ◽  
Violaine Deytieux ◽  
Martin Lechenet ◽  
...  
Keyword(s):  
Crop Yield ◽  
Weed Management ◽  
Crop Production ◽  
Yield Loss ◽  
Crop Yields ◽  
Cropping Systems ◽  
Crop Productivity ◽  
Arable Farming ◽  
Herbicide Use ◽  
The Impact

The growing recognition of the environmental and health issues associated to pesticide use requires to investigate how to manage weeds with less or no herbicides in arable farming while maintaining crop productivity. The questions of weed harmfulness, herbicide efficacy, the effects of herbicide use on crop yields, and the effect of reducing herbicides on crop production have been addressed over the years but results and interpretations often appear contradictory. In this paper, we critically analyze studies that have focused on the herbicide use, weeds and crop yield nexus. We identified many inconsistencies in the published results and demonstrate that these often stem from differences in the methodologies used and in the choice of the conceptual model that links the three items. Our main findings are: (1) although our review confirms that herbicide reduction increases weed infestation if not compensated by other cultural techniques, there are many shortcomings in the different methods used to assess the impact of weeds on crop production; (2) Reducing herbicide use rarely results in increased crop yield loss due to weeds if farmers compensate low herbicide use by other efficient cultural practices; (3) There is a need for comprehensive studies describing the effect of cropping systems on crop production that explicitly include weeds and disentangle the impact of herbicides from the effect of other practices on weeds and on crop production. We propose a framework that presents all the links and feed-backs that must be considered when analyzing the herbicide-weed-crop yield nexus. We then provide a number of methodological recommendations for future studies. We conclude that, since weeds are causing yield loss, reduced herbicide use and maintained crop productivity necessarily requires a redesign of cropping systems. These new systems should include both agronomic and biodiversity-based levers acting in concert to deliver sustainable weed management.

South Africa’s increasing climate variability and its effect on food production

2021 ◽  
pp. 003072702110049
Author(s):  
Mashudu Tshikovhi ◽  
Roscoe Bertrum van Wyk
Keyword(s):  
South Africa ◽  
Climate Variability ◽  
Food Production ◽  
Fixed Effects ◽  
Crop Yields ◽  
Negative Impact ◽  
Secondary Data ◽  
Mean Annual Temperature ◽  
Fixed Effects Panel Regression ◽  
The Impact

This study examines the impact of increasing climate variability on food production in South Africa, focusing on maize and wheat yields. A two-way fixed effects panel regression model was used to assess the climate variability impacts, analysing secondary data for the period 2000 to 2019 for nine provinces in South Africa. The study found that increasing climate variability has a negative impact on maize and wheat production in South Africa. Specifically, the results indicated a negative correlation between mean annual temperature with both maize and wheat yields. A decrease in precipitation affected maize yields negatively, while the impact on wheat yields was positive, although insignificant. This analysis, therefore, depicted that crop yields generally increase with more annual precipitation and decrease with higher temperatures. The study recommends that funding initiatives to educate farmers on increasing climate variability and its effects on farming activities in South Africa should be prioritised.

scholarly journals Using the anomaly forcing Community Land Model (CLM 4.5) for crop yield projections

2020 ◽  
Author(s):  
Yaqiong Lu ◽  
Xianyu Yang
Keyword(s):  
Crop Yield ◽  
Temporal Resolution ◽  
Land Surface ◽  
Crop Yields ◽  
Surface Model ◽  
High Temporal Resolution ◽  
Climate Scenarios ◽  
Climate Data ◽  
Land Surface Models ◽  
Surface Models

Abstract. Crop growth in land surface models normally requires high temporal resolution climate data (3-hourly or 6-hourly), but such high temporal resolution climate data are not provided by many climate model simulations due to expensive storage, which limits modeling choice if there is an interest in a particular climate simulation that only saved monthly outputs. The Community Land Surface Model (CLM) has proposed an alternative approach for utilizing monthly climate outputs as forcing data since version 4.5, and it is called the anomaly forcing CLM. However, such an approach has never been validated for crop yield projections. In our work, we created anomaly forcing datasets for three climate scenarios (1.5 °C warming, 2.0 °C warming, and RCP4.5) and validated crop yields against the standard CLM forcing with the same climate scenarios using 3-hourly data. We found that the anomaly forcing CLM could not produce crop yields identical to the standard CLM due to the different submonthly variations, and crop yields were underestimated by 5–8 % across the three scenarios (1.5 °C, 2.0 °C, and RCP4.5) for the global average, and 28–41 % of cropland showed significantly different yields. However, the anomaly forcing CLM effectively captured the relative changes between scenarios and over time, as well as regional crop yield variations. We recommend that such an approach be used for qualitative analysis of crop yields when only monthly outputs are available. Our approach can be adopted by other land surface models to expand their capabilities for utilizing monthly climate data.

scholarly journals Modeling the Impacts of Climate Change on Crop Yield and Irrigation in the Monocacy River Watershed, USA

Climate ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 139
Author(s):  
Manashi Paul ◽  
Sijal Dangol ◽  
Vitaly Kholodovsky ◽  
Amy R. Sapkota ◽  
Masoud Negahban-Azar ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Crop Yields ◽  
Swat Model ◽  
Global Climate Models ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
River Watershed ◽  
Temperature And Precipitation ◽  
The Impact

Crop yield depends on multiple factors, including climate conditions, soil characteristics, and available water. The objective of this study was to evaluate the impact of projected temperature and precipitation changes on crop yields in the Monocacy River Watershed in the Mid-Atlantic United States based on climate change scenarios. The Soil and Water Assessment Tool (SWAT) was applied to simulate watershed hydrology and crop yield. To evaluate the effect of future climate projections, four global climate models (GCMs) and three representative concentration pathways (RCP 4.5, 6, and 8.5) were used in the SWAT model. According to all GCMs and RCPs, a warmer climate with a wetter Autumn and Spring and a drier late Summer season is anticipated by mid and late century in this region. To evaluate future management strategies, water budget and crop yields were assessed for two scenarios: current rainfed and adaptive irrigated conditions. Irrigation would improve corn yields during mid-century across all scenarios. However, prolonged irrigation would have a negative impact due to nutrients runoff on both corn and soybean yields compared to rainfed condition. Decision tree analysis indicated that corn and soybean yields are most influenced by soil moisture, temperature, and precipitation as well as the water management practice used (i.e., rainfed or irrigated). The computed values from the SWAT modeling can be used as guidelines for water resource managers in this watershed to plan for projected water shortages and manage crop yields based on projected climate change conditions.

scholarly journals What Is the Consensus from Multiple Conclusions of Future Crop Yield Changes Affected by Climate Change in China?

2020 ◽  
Vol 17 (24) ◽  
pp. 9241
Author(s):  
Chengfang Huang ◽  
Ning Li ◽  
Zhengtao Zhang ◽  
Yuan Liu ◽  
Xi Chen ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Crop Yields ◽  
Positive Impact ◽  
Scientific Evidence ◽  
Future Climate Change ◽  
Systematic Review Methodology ◽  
Uncertainty Interval ◽  
The Impact ◽  
Yield Decrease

Many studies have shown that climate change has a significant impact on crop yield in China, while results have varied due to uncertain factors. This study has drawn a highly consistent consensus from the scientific evidence based on numerous existing studies. By a highly rational systematic review methodology, we obtained 737 result samples with the theme of climate change affecting China’s crop yields. Then, we used likelihood scale and trend analysis methods to quantify the consensus level and uncertainty interval of these samples. The results showed that: (i) The crop yield decrease in the second half of the 21st century will be greater than 5% of that in the first half. (ii) The crop most affected by climate change will be maize, with the decreased value exceeding −25% at the end of this century, followed by rice and wheat exceeding −10% and −5%. (iii) The positive impact of CO2 on crop yield will change by nearly 10%. Our conclusions clarify the consensus of the impact of future climate change on China’s crop yield, and this study helps exclude the differences and examine the policies and actions that China has taken and should take in response to climate change.

ECONOMIC AND PHYSICAL MODELING OF LAND USE IN GCAM 3.0 AND AN APPLICATION TO AGRICULTURAL PRODUCTIVITY, LAND, AND TERRESTRIAL CARBON

2014 ◽  
Vol 05 (02) ◽  
pp. 1450003 ◽  
Author(s):  
MARSHALL WISE ◽  
KATE CALVIN ◽  
PAGE KYLE ◽  
PATRICK LUCKOW ◽  
JAE EDMONDS
Keyword(s):  
Land Use ◽  
Crop Yield ◽  
Carbon Emissions ◽  
Crop Yields ◽  
Assessment Model ◽  
Agricultural Crop ◽  
Terrestrial Carbon ◽  
Global Change Assessment Model ◽  
The Impact

The release of the Global Change Assessment Model (GCAM) version 3.0 represents a major revision in the treatment of agriculture and land-use activities in a model of long-term, global human and physical Earth systems. GCAM 3.0 incorporates greater spatial and temporal resolution compared to GCAM 2.0. In this paper, we document the methods embodied in the new release, describe the motivation for the changes, compare GCAM 3.0 methods to those of other long-term, global agriculture-economy models and apply GCAM 3.0 to explore the impact of changes in agricultural crop yields on global land use and terrestrial carbon. In the absence of continued crop yield improvements throughout the century, not only are cumulative carbon emissions a major source of CO 2 emissions to the atmosphere, but bioenergy production remains trivial — land is needed for food. In contrast, the high crop yield improvement scenario cuts terrestrial carbon emissions dramatically and facilitates both food and energy production.

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