Predictors determining the potential of inland valleys for rice production development in West Africa

Djagba, Justin Fagnombo; Sintondji, Luc Ollivier; Kouyaté, Amadou Malé; Baggie, I.; Agbahungba, G.; Hamadoun, Abdoulaye; Zwart, Sander J.

doi:10.1016/j.apgeog.2018.05.003

Cited by 16 publications

(12 citation statements)

References 69 publications

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“…In the states of Niger and Kaduna in Nigeria, duration of surface water flow was a relevant predictor for drought occurrence in inland valleys rice-based production systems. A longer duration of surface water flow is often associated with a better opportunity for farmers to irrigate rice plants, thereby mitigating drought effects on rice production similar to the findings of Reference [40] in northern Benin.…”

Section: Discussionmentioning

confidence: 56%

Predictors of Drought in Inland Valley Landscapes and Enabling Factors for Rice Farmers’ Mitigation Measures in the Sudan-Sahel Zone

et al. 2018

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Drought is a noteworthy cause of low agricultural profitability and of crop production vulnerability, yet in numerous countries of Africa little to no consideration has been paid to readiness for drought calamity, particularly to spatial evaluation and indicators of drought occurrence. In this study, biophysical and socio-economic data, farmers’ community surveys and secondary data from remote sensing on soil characteristics and water demand were used to evaluate the predictors of drought in inland valley rice-based production systems and the factors affecting farmers’ mitigation measures. The study intervened in three West African countries located in the Sudan-Sahel zone, viz. Burkina Faso, Mali and Nigeria. Significant drying trends occurred at latitudes below 11°30′ whilst significant wetting trends were discerned at latitude above 11°30′. Droughts were more frequent and had their longest duration in the states of Niger and Kaduna located in Nigeria and in western Burkina Faso during the period 1995–2014. Among 21 candidate predictors, average annual standardized precipitation evapotranspiration index and duration of groundwater availability were the most important predictors of drought occurrence in inland valleys rice based-production systems. Land ownership and gender affected the commitment of rice farmers to use any mitigation measure against drought. Drought studies in inland valleys should include climatic water balance and groundwater data. Securing property rights and focusing on women’s association would improve farmers’ resilience and advance drought mitigation measures.

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Section: Discussionmentioning

confidence: 56%

Predictors of Drought in Inland Valley Landscapes and Enabling Factors for Rice Farmers’ Mitigation Measures in the Sudan-Sahel Zone

et al. 2018

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“…This section provides a summary of the steps taken to develop the geospatial dataset. [2] provides a full description of the methodology that was followed.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

A geospatial dataset of inland valleys in four zones in Benin, Sierra Leone and Mali

et al. 2019

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The dataset described in this data article represents four agricultural zones in West-Africa that are located in three countries: Benin, Mali and Sierra Leone. The dataset was created through a research collaboration between the Africa Rice Center (AfricaRice), Sierra Leone Agricultural Research Institute (SLARI) and the Institute for Rural Economy (IER). The dataset was compiled to investigate the potential for rice production in inland valleys of the three countries. The results of the investigation were published in Dossou-Yovo et al. (2017) and Djagba et al. (2018). The dataset describes the biophysical and socioeconomic conditions of 499 inland valleys in the four agricultural zones. In each inland valley data were collected through a focus group interview with a minimum of three farmers. In 499 interviews a total of 7496 farmers participated. The location of each inland valley was determined with handheld GPS devices. The geographic locations were used to extract additional parameters from digital maps on soils, elevation, population density, rainfall, flow accumulation, and distances to roads, market places, rice mills, chemical input stores, and settlements. The dataset contains 65 parameters in four themes (location, biophysical characteristics, socioeconomic characteristics, and inland valley land development and use). The GPS coordinates indicate the location of an inland valley, but they do not lead to the location of individual fields of farmers that were interviewed. The dataset is publicly shared as Supplementary data to this data article.

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“…Researchers have studied the factors that influence regionwise crop yield differences under technological, biological, and environmental categories [1]. For example, the Random Forest (RF) was used to assess the parameters related to biophysical and socioeconomic environments that affect the growth of paddy [2]. Among the contributory factors mentioned above, it has been found that weather factors account for more on productivity of crops than others due to their direct and indirect effects [3].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka

Ekanayake

Rankothge

Weliwatta

et al. 2021

Journal of Mathematics

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This paper presents the development of crop-weather models for the paddy yield in Sri Lanka based on nine weather indices, namely, rainfall, relative humidity (minimum and maximum), temperature (minimum and maximum), wind speed (morning and evening), evaporation, and sunshine hours. The statistics of seven geographical regions, which contribute to about two-thirds of the country’s total paddy production, were used for this study. The significance of the weather indices on the paddy yield was explored by employing Random Forest (RF) and the variable importance of each of them was determined. Pearson’s correlation and Spearman’s correlation were used to identify the behavior of correlation in a positive or negative direction. Further, the pairwise correlation among the weather indices was examined. The results indicate that the minimum relative humidity and the maximum temperature during the paddy cultivation period are the most influential weather indices. Moreover, RF was used to develop a paddy yield prediction model and four more techniques, namely, Power Regression (PR), Multiple Linear Regression (MLR) with stepwise selection, forward (step-up) selection, and backward (step-down) elimination, were used to benchmark the performance of the machine learning technique. Their performances were compared in terms of the Root Mean Squared Error (RMSE), Correlation Coefficient (R), Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE). As per the results, RF is a reliable and accurate model for the prediction of paddy yield in Sri Lanka, demonstrating a very high R of 0.99 and the least MAPE of 1.4%.

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Predictors determining the potential of inland valleys for rice production development in West Africa

Cited by 16 publications

References 69 publications

Predictors of Drought in Inland Valley Landscapes and Enabling Factors for Rice Farmers’ Mitigation Measures in the Sudan-Sahel Zone

Predictors of Drought in Inland Valley Landscapes and Enabling Factors for Rice Farmers’ Mitigation Measures in the Sudan-Sahel Zone

A geospatial dataset of inland valleys in four zones in Benin, Sierra Leone and Mali

Machine Learning Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka

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