scholarly journals Crop Yield Sensitivity to Climatic Variability as the Basis for Creating Climate Resilient Agriculture

2016 ◽  
Vol 05 (01) ◽  
pp. 69-76 ◽  
Author(s):  
David Chikodzi
Keyword(s):  
Crop Yield ◽  
Climatic Variability ◽  
Yield Sensitivity

The Impact of Spatial Soil Variability on Simulation of Regional Maize Yield

2017 ◽  
Vol 60 (6) ◽  
pp. 2137-2148 ◽  
Author(s):  
Vaishali Sharda ◽  
Cameron Handyside ◽  
Bernardo Chaves ◽  
Richard T. McNider ◽  
Gerrit Hoogenboom
Keyword(s):  
Climate Variability ◽  
Soil Properties ◽  
Crop Yield ◽  
Climatic Factors ◽  
Southern Oscillation ◽  
Climatic Variability ◽  
Maize Yield ◽  
Soil Variability ◽  
Large Variability ◽  
The Impact

Abstract. The study of climate variability and its impacts on crop production has become a continuous effort for the scientific community over the past two decades. However, the impact of spatial soil variability along with climatic factors on crop yield remains uncertain. The objective of this study was to determine the impact of soil and climatic variability on maize yield. We used Alabama as a case study because the agriculture is predominantly rainfed and there is a large variability in growing season precipitation due to the influence of climate variability signals such as the El Niño Southern Oscillation (ENSO). The cropping system model CERES-Maize of the Decision Support System for Agrotechnology Transfer (DSSAT) was used to simulate growth, development, and grain yield for maize for the top ten maize-producing counties in Alabama under rainfed conditions during dry and wet ENSO years. Maize yield simulations were compared for one prominent agricultural soil in each county, the top three prominent agricultural soils in each county, and spatially distributed SSURGO soils in each county. Simulated yields were then compared with maize yields reported by the National Agricultural Statistical Services (NASS). The simulation results showed that maize yield was impacted by both climate variability and spatial soil variability. Statistical relationships were established between crop yield, yield changes, and soil properties. This simulation study established the clear importance of soil variability in crop-climate impact studies. Keywords: Crop Modeling, DSSAT, Database, Soil properties, Spatial variability.

scholarly journals MODELAGEM AGROMETEOROLÓGICA PARA ESTIMATIVA DE PRODUTIVIDADE DE SOJA PARA O VALE DO MÉDIO PARANAPANEMA-SP

Irriga ◽  
2015 ◽  
Vol 20 (3) ◽  
pp. 490-501 ◽  
Author(s):  
Daniela Fernanda da Silva-Fuzzo ◽  
Angélica Prela-Pantano ◽  
Marcelo Bento Paes de Camargo
Keyword(s):  
Crop Yield ◽  
Climatic Variability ◽  
Sao Paulo ◽  
Data Availability ◽  
São Paulo ◽  
Estimation Model ◽  
Climatological Data ◽  
Temperature And Precipitation ◽  
Using Data ◽  
Water Surplus

MODELAGEM AGROMETEOROLÓGICA PARA ESTIMATIVA DE PRODUTIVIDADE DE SOJA PARA O VALE DO MÉDIO PARANAPANEMA-SP  DANIELA FERNANDA DA SILVA-FUZZO¹ ; ANGELICA PRELA-PANTANO² E MARCELO BENTO PAES DE CAMARGO³ 1 Faculdade de Engenharia Agrícola – Feagri UNICAMP (LabGeo) [email protected], 3 Centro de Pesquisa e Desenvolvimento de Ecofisiologia e Biofísica, Instituto Agronômico, Campinas, SP [email protected], [email protected]  1 RESUMO Modelos agrometeorológicos de estimativa de safra utilizam dados climatológicos coletados em estações convencionais de superfície. A precipitação é o elemento meteorológico que apresenta maior variabilidade espacial, e desta forma com o intuito de obter uma maior cobertura e disponibilidade de dados de precipitação pluvial vem sendo utilizados dados estimados por satélite. Com isso melhora a qualidade das estimativas de produtividade da cultura da soja no Estado de São Paulo. O objetivo foi estimar a produtividade da cultura da soja, para a região do Vale do Médio Paranapanema-SP, utilizando dados de temperatura e precipitação de estações meteorológicas convencionais e dados de precipitação estimados pelo satélite TRMM para o período de 1998 a 2008, por meio de modelagem agrometeorológico. O modelo de estimativa de produtividade utilizado levou em consideração tanto deficiência hídrica como o excedente hídrico e apresentou desempenho satisfatório e melhoria significativa com a inserção dos dados do satélite TRMM, com valores de índice de concordância ‘d’ variando de 0,80 a 0,90. Palavras-chave: Precipitação pluvial, variabilidade climática, TRMM.  SILVA-FUZZO,D.F.; PRELA-PANTANO, A.; CAMARGO,M.B.P.AGROMETEOROLOGICAL MODELING TO  ESTIMATE  SOYBEAN YIELD IN THE VALLEY OF THE MIDDLE PARANAPANEMA-SP   2 ABSTRACT Agrometeorological models to estimate harvest use climatological data collected in conventional surface stations. Precipitation is the weather parameter   which presents the highest spatial variability. Therefore,  in order to achieve greater coverage and   rainfall data availability, data estimated by satellite have been used, and consequently, better estimates of soybean crop yield have been found in São Paulo state.     The objective of this study was to estimate crop yield of soybean in  the Valley of the Middle Paranapanema-SP, using data of temperature and precipitation from  conventional meteorological stations and precipitation data estimated by the TRMM satellite in  the period from 1998 to 2008 using agrometeorological modeling.   The estimation model of productivity considered both water stress and water surplus, and using data of the TRMM satellite caused a significant improvement with values of concordance index  ‘d’ of Willmott ranging from 0.80 to 0.90. Keywords: Rainfall, climatic variability, TRMM.

scholarly journals Comparison of crop yield sensitivity to ozone between open-top chamber and free-air experiments

2018 ◽  
Vol 24 (6) ◽  
pp. 2231-2238 ◽  
Author(s):  
Zhaozhong Feng ◽  
Johan Uddling ◽  
Haoye Tang ◽  
Jianguo Zhu ◽  
Kazuhiko Kobayashi
Keyword(s):  
Crop Yield ◽  
Open Top Chamber ◽  
Free Air ◽  
Yield Sensitivity

scholarly journals EVALUATION AND IMPACT OF CLIMATIC VARIABILITY ON GUINEA CORN (SORGHUM BICOLOR) IN SELECTED STATE IN NIGERIA

2020 ◽  
Vol 4 (1) ◽  
pp. 10-14
Author(s):  
Ibrahim Sufiyan ◽  
J.I. Magajia ◽  
A.T. Ogah ◽  
K. Karagama
Keyword(s):  
Climate Variability ◽  
Crop Yield ◽  
Agricultural Production ◽  
Climatic Variability ◽  
Climatic Variable ◽  
Microsoft Excel ◽  
Correlation And Regression Analysis ◽  
The World ◽  
Farm Inputs ◽  
General Wellbeing

Climate variability is one of the serious environmental challenges that have received a lot of public outcry in most parts of the world due to its consequence on agricultural production and other sectors of the national economy and general wellbeing. This study, therefore, sought to examine the effects of climate variability on crops production in the Bakori Local Government Area of Kastina State, Nigeria. Rainfall, temperature and selected crops (Sorghum) data from the farmers living in Bkori and cultivate Guinea corn every year. The data were analyzed using correlation and regression analysis in SPSS and the trend the function of Microsoft Excel.). The study identified positive crop yield while comparing temperature trend sorghum temperature characteristics, the most important climatic variable that influences the yields of Sorghum in Bakori is temperature and rainfall. This has beeachieved by monitoring 100 farmers at different locations in the study area and the use of farm inputs and monitoring of crop-climate relationships to achieve improved crop yield.

Macro-scale influence of climate on crop production in the Fitzroy catchment of Central Queensland

1999 ◽  
Vol 50 (4) ◽  
pp. 529
Author(s):  
F. M. Amirul Islam ◽  
Saleh A. Wasimi ◽  
Graham R. Wood
Keyword(s):  
Crop Yield ◽  
General Circulation ◽  
Crop Production ◽  
Climatic Variability ◽  
Climatic Conditions ◽  
Response Variable ◽  
Explanatory Variables ◽  
Genetic Gains ◽  
Macro Scale ◽  
Expected Values

When the dynamics of a system is too complex to be analytically modelled, it has been found useful to assume that expected values of explanatory variables generate expected values of the response variable, and hence, deviations from the expected value of the response variable can be modelled by a Linear Perturbation Model (LPM) of the explanatory variables. This method is used in this study to develop a technique to update crop forecasts where climate is a major factor in crop production. The study is important because modern cultivars, which are the result of genetic gains, are sensitive to climatic variability, and recent studies with general circulation models suggest that one of the consequences of an increase in greenhouse gases may be greater variability in the climate of a region. The usefulness of the LPM technique in the study of agriculture–climate relationships is tested through application to the Fitzroy catchment in Central Queensland. Since no reported climatic change is yet occurring in the region, the expected values for climatic conditions are obtained through averaging. By contrast, the expected values of crop yield are obtained from trend analysis; such trends are mainly attributable to genetic gains in the recent past. Three crops (wheat, barley, and sunflower) have been studied. Deviations (or perturbations) in crop yields are related, in the framework of LPM, to deviations in minimum, maximum, and average values of rainfall, temperature, and humidity at planting, flowering, and harvesting time. The most significant climatic factors affecting deviations in crop yield are identified. Regression models are developed which are capable of filtering and updating crop forecasts due to any unexpected climatic conditions, assuming consistent genetic trends and management practices.

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

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