The Role of Agriculture in Poverty Alleviation: Empirical Evidence from Pakistan

Hayat, Umar; Ali, Shahid; Mateen, Abdul; Bilal, Hazrat

doi:10.17582/journal.sja/2019/35.4.1309.1315

Cited by 6 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ArcGIS and the GEE platform were used for the bulk of the analysis. SVM is a supervised, non-parametric approach that uses marginal kernels [47]. When minimizing errors without overfitting the model, SVM regression uses a kernel function to map the input onto a high-dimensional feature space, where a linear regression model is subsequently built.…”

Section: A Data Preprocessingmentioning

confidence: 99%

“…2) Random Forest (RF) Random Forest (RF) is a nonparametric method for regression tree analysis and advanced categorization, known for its resilience against overfitting and effectiveness with highdimensional datasets [47,49]. On the other hand, SVM is a supervised learning model utilized for regression and classification tasks [49].…”

Section: A Data Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

Accurate Wheat Yield Prediction Using Machine Learning and Climate-NDVI Data Fusion

Ashfaq,

Khan,

Alzahrani

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Due to exponential population growth, climate change, and an increasing demand for food, there is an unprecedented need for a timely, precise, and dependable assessment of crop yield on a large scale. Wheat, a staple crop worldwide, requires accurate and prompt prediction of its output for global food security. Traditionally, the development of empirical models for crop yield forecasting has relied on climate data, satellite data, or a combination of both. Despite the enhanced performance achieved by integrating satellite and climate data, the contributions from various sources (Climate, Soil, Socioeconomic, and Remote sensing) remain unclear. The lack of well-defined comparisons between the performance of regression-based approaches and different Machine Learning (ML) methods in yield prediction necessitates further investigation. This study addresses the gaps by combining data from multiple sources to forecast wheat yield in the Multan region in the Punjab province of Pakistan. The findings are compared to the benchmark provided by Crop Report Services (CRS) Punjab, with three widely used ML techniques (support vector machine (SVM), Random Forest (RF), and Least Absolute Shrinkage and Selection Operator (LASSO)) by integrating publicly available data within the GEE (Google Earth Engine) platform, including climate, satellite, soil properties, and spatial information data to develop alternative empirical models for yield prediction using data from 2017 to 2022, selecting the best attribute subset related to crop output. The data set of district-level simulated yields was analyzed with three Machin Learning models (SVM, RF, and LASSO) as a function of seasonal weather, satellite, and soil. The results indicate that combining all datasets using three ML algorithms achieves better yield prediction performance (R 2 : 0.74-0.88). Incorporating spatial information and other properties into benchmark models can improve the prediction from 0.08 to 0.12. Random forest outperformed the competitor models with a Root Mean Square Error (RMSE) of 0.05 q/ha and R 2 of 0.88. Comparative analysis shows that random forest with 97% and SVM with 93% yielded better results in the study area.

show abstract

Section: A Data Preprocessingmentioning

confidence: 99%

Section: A Data Preprocessingmentioning

confidence: 99%

Accurate Wheat Yield Prediction Using Machine Learning and Climate-NDVI Data Fusion

Ashfaq,

Khan,

Alzahrani

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Lastly, it can bring the uneducated and non-skilled labor of the country to start earning by working in the agricultural activities and can reduce or alleviate poverty from the society. Hence, this sector can build up the economic capacity of a country and lead it towards sustainability (Ansari & Khan, 2018;Hayat, Ali, Mateen, & Bilal, 2019). However, the growth of Agriculture is confronted with three common challenges by the countries.…”

Section: Agricultural Growthmentioning

confidence: 99%

Regressing Climate Change, Agricultural Growth and Food Production on Economic Sustainability: Gathering and Analyzing Data for Asean Countries

Faqih¹,

Siswoyo²

2020

JSSI

View full text Add to dashboard Cite

The agricultural sector plays a great role in the economy of several countries and their economic sustainability is also majorly dependent on the agricultural performance of the country. There are various aspects related to agriculture sector that might have the impact on economic sustainability in one way or the other. In continuation of this issue, the researcher has conducted this study so that the impact casted by climate change, agricultural growth, and food production on the economic sustainability of the ASEAN countries can be studied effectively. For this research, the researcher has gathered relevant data from six ASEAN countries i.e. Brunei, Cambodia, Indonesia, Laos, Thailand, and Philippines. The period for which the data has been collected comprises of 29 years. For analysis of this data, the researcher has applied tests such as panel unit root test, panel cointegration test, AMG estimation and panel casualty test and has obtained the desired results. The results make it clear that the independent variables i.e. climate change, agricultural growth and food production have significant impact on economic sustainability for most of the selected ASEAN countries. Moreover, various variables have also shown unidirectional and bidirectional casualty among them.

show abstract

“…Pakistan's government is dedicated to enhancing agricultural production, bolstering marketing, and fostering trade competitiveness. Creating an enabling environment is pivotal for realizing these policy objectives, as increased agricultural income yields multifaceted benefits (Hayat et al, 2019). Agricultural advancements not only elevate household incomes surpassing other sectors but also disproportionately benefit impoverished households, as highlighted by the World Bank's estimation favoring the economically disadvantaged (Christiaensen et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Farm Efficiency Analysis of Apple Growers in Newley Merged District South Waziristan

Ullah,

Hassan,

Abbas

et al. 2024

J. Econ. Impact

View full text Add to dashboard Cite

In the apple-producing land of South Waziristan’s three tehsils, Wana, Bermal, and Tiarza, this research paper is designed to analyze farmers' efficiencies, e.g., production, allocative and technical dimensions. Through a Data Envelopment Analysis (DEA) framework, the study evaluates the performance trajectories of 304 apple growers. A multistage sampling technique was used for the farmer’s interview. To the researcher’s knowledge, there is no detailed efficiency analysis of South Waziristan’s apple growers in existing literature, so there is a need to investigate the factors affecting efficiencies in the targeted area. The researcher is therefore encouraged to conduct a study of the farm efficiency of apple growers in South Waziristan. Apple growers' efficiencies were categorized as technical, allocative, and economic efficiency using the DEA approach. Results of the study show that there were tremendous inefficiencies of apple growers, which need to be reduced. This study sheds light on optimal resource allocation, productivity dynamics, and efficiency frontiers within apple cultivation in South Waziristan. The research offers an understanding of agricultural practices, resource utilization patterns, and efficiency benchmarks. The empirical insights derived from this comprehensive analysis facilitate informed decision-making among farmers and provide policymakers, agricultural stakeholders, and development practitioners with actionable recommendations to enhance agricultural productivity, sustainability, and socio-economic well-being in the region. Through empirical evidence and contextual relevance, this paper contributes significantly to the evolving discourse on agricultural efficiency analysis and underscores the imperative of tailored interventions for optimizing apple cultivation practices in South Waziristan.

show abstract

The Role of Agriculture in Poverty Alleviation: Empirical Evidence from Pakistan

Cited by 6 publications

References 0 publications

Accurate Wheat Yield Prediction Using Machine Learning and Climate-NDVI Data Fusion

Accurate Wheat Yield Prediction Using Machine Learning and Climate-NDVI Data Fusion

Regressing Climate Change, Agricultural Growth and Food Production on Economic Sustainability: Gathering and Analyzing Data for Asean Countries

A Comprehensive Farm Efficiency Analysis of Apple Growers in Newley Merged District South Waziristan

Contact Info

Product

Resources

About