Small-scale irrigation (SSI) farming as a climate-smart agriculture (CSA) practice and its influence on livelihood improvement in Offa District, Southern Ethiopia

Bojago, Elias; Abrham, Yitbarek

doi:10.1016/j.jafr.2023.100534

Cited by 21 publications

(13 citation statements)

References 52 publications

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“…On the other hand, keeping livestock can help households alleviate household food insecurity in multiple ways; first by consuming livestock products (milk, eggs, meat) directly, secondly by selling livestock products for income, which can be used to buy other consumption items including food. Lastly, livestock provides manure that can be used in crop farms to increase biomass production, which can be consumed directly or indirectly through market sales for income [45][46][47][48]. Therefore, it is not surprising that using organic waste as livestock feed within households that generate such organic waste is strongly associated with reductions in food insecurity within such households.…”

Section: Regression and Propensity Score Matching Resultsmentioning

confidence: 99%

Circular bioeconomy practices and their associations with household food security in four RUNRES African city regions

Sekabira,

Feleke,

Manyong

et al. 2024

PLOS Sustain Transform

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Achieving the United Nation’s 2030 agenda which aims, among other goals, to ensure sustainable consumption and production patterns, requires a sustainable resource use model deployed at scale across global food systems. A circular bioeconomy (CBE) model of resource use has been proposed to reuse of organic waste in agricultural production to enhance food security. However, despite several initiatives recently introduced towards establishing a CBE in sub-Saharan Africa (SSA), minimal scientific efforts have been dedicated to understanding the association of CBE practices and food security. This study use data from 777 smallholder farm households from DRC, Ethiopia, Rwanda, and South Africa, to examine associations between three CBE practices (use of organic waste as compost, as livestock feed, and sorting waste) and household food security. Using different regression and propensity score matching models (PSM). Result reveal that using CBE practices more likely adds a 0.203 score of food insecurity access prevalence (HFIAP), 1.283 food insecurity access scale (HFIAS-score) and 0.277 for household dietary diversity score (HDDS) among households using CBE practiced groups. Associations regarding using organic waste as compost are generally positive but insignificant, while those with sorting waste are significantly and consistently negative. Thus, CBE innovations aiming to enhance household food security could prioritize organic waste valorization into livestock feed consider socio economic aspects such as access to land, access to market, education level, using mobile phone, income and city regions where interventions took place. However, prior sorting of waste is necessary to enable effective waste valorization.

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Section: Regression and Propensity Score Matching Resultsmentioning

confidence: 99%

Circular bioeconomy practices and their associations with household food security in four RUNRES African city regions

Sekabira,

Feleke,

Manyong

et al. 2024

PLOS Sustain Transform

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“…In a literature review, the author discusses how the logistic regression is applied in precision agriculture and the benefits and drawbacks of such an application. The purpose of the review is to investigate the application of the logistic regression in different areas, such as crop classification, disease detection, and yield prediction as well as usage for a decision support system [9], [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Machine learning and Sensor-Cloud Based Precision Agriculture for Intelligent Water Management for Enhanced Crop Productivity

Abhishek Sharma

2024

jes

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The combination of Machine Learning algorithms with Internet of Things devices is emerging as an effective solution to redefining precision agriculture for better water management and crop cultivation. The purpose of this study is to use different learning models, such as Artificial Neural Networks , Support Vector Machines , Decision Trees , and Random Forest , to predict the irrigation need based on real-world sensor data. To retrieve the output variable, which is the irrigation requirement, data from temperature, soil moisture, water level, and humidity sensors that are available in a tomato cultivation facility are used. The dataset consists of 3422 readings, which are split into training and testing sets. A designated percentage of 70% is used for training the models, while the remaining 30% are used to test the outcomes. As the results of the study show, the ANN model is the most accurate predictor of irrigation need with the classification rate of 97.6%, followed by SVM , DT , and RF with 95.4%, 91.3%, and 88.9% correspondingly. The differences in the outcomes are demonstrated in confusion matrices, which identify the classification of the cases and indicate the percentage of correct predictions. Evidence of the predictive power of ML models implies that farmers can independently determine when to activate the pump when real-world data serve as the input. Additionally, the ability to collect real-world data using IoT sensors is beneficial, as it empowers farmers with up-to-date information to make a timely decision about pump activation. The limitations are associated with the type of crop and agricultural facility and, for this reason, future studies may investigate the generality of the conclusion with regard to other crop types and facilities.

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“…Even though the decision tree model is simple, it generally overperforms and should be used if there is a transparency requirement for the nature of the training task. At the same time, support vector machine models are one of the most recent applications in agriculture for predictive analytics and classification task purposes [7], [8]. It works well when analyzing complex datasets by making generally good predictions.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the machine learning based Efficacy of Decision Tree and Support Vector Machines in Smart Irrigation Systems for Precise Irrigation Status Classification for Optimizing Water Management in Agriculture

Shaik Mabasha

2024

jes

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The research paper deals with the improvement of water management practices in agriculture through the use of machine learning in smart irrigation systems. The efficiency of the Decision Tree and the Support Vector Machine models with real-time sensor data was analyzed. The Internet of Things system was developed to analyze the data collected at the field with the help of various sensors, including temperature, humidity, and soil moisture sensors. The layout and choice of sensors were based on the official parameters required for most average plants and corps. After data collection and development of classifiers, the Decision Tree and Support Vector Machine models were used and analyzed regarding their efficiency for making irrigation-related decisions. As the result, it was found that both models showed a good performance, but the SVM model was slightly better due to a smaller number of false positives and false negatives, which allow it to divide the data into the corresponding categories more accurately. At the same time, both models use history data, and the more recent data are better for decision as it was also noted during the development when the use of models based on history data for irrigation decision result in regular irrigation, which in long term would cause the loss of productivity of plans, due to the issue that ancient and no crops are watered too. As the final results, it can be stated that the models show good performance, and the algorithms that were tested can be stated viable for the use at practice. At the same time, the SVM model shows slightly better performance than DT.

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Small-scale irrigation (SSI) farming as a climate-smart agriculture (CSA) practice and its influence on livelihood improvement in Offa District, Southern Ethiopia

Cited by 21 publications

References 52 publications

Circular bioeconomy practices and their associations with household food security in four RUNRES African city regions

Circular bioeconomy practices and their associations with household food security in four RUNRES African city regions

Machine learning and Sensor-Cloud Based Precision Agriculture for Intelligent Water Management for Enhanced Crop Productivity

Evaluating the machine learning based Efficacy of Decision Tree and Support Vector Machines in Smart Irrigation Systems for Precise Irrigation Status Classification for Optimizing Water Management in Agriculture

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