Determining an optimum cropping pattern for Egypt

Agriculture is considered to be the major economic activity in Egypt despite the government policies that favored other sectors since the second half of the 20th century. However, Egypt currently faces a food security challenge that stems from the increasing demand for food in light of huge population growth and the inability of the agricultural sector to fulfill the abovementioned increasing demand. This research focuses on the vertical expansion of the agricultural sector through attempting to determine the optimum cropping mix for Egypt in the year 2017. A fuzzy goal programming (FGP) approach for optimal land allocation is utilized. In the model formulation, five goals were modeled; namely crop production, net profit, investment, fertilizers and water requirements. A tolerance based FGP technique was employed to account for the fuzziness of the selected goals. Without imposing any constraints to ensure food security, results show that it is not optimal to grow strategic crops, including wheat, broad beans, and maize. Accordingly, constraints were set on the minimum land allocations to strategic crops. Results of the model indicate that achieving food security has some costs in terms of profitability and fertilizers utilization. Yet, it is possible for the government to target higher levels of self-sufficiency of strategic items as the costs are tolerable. The resulting land allocations indicated that the profit goal was fuzzily achieved only in the winter season, yielding a level of profit that is lower than the target by only 0.68%. As for the fertilizers requirements goals, they were partially achieved in both the winter and the summer seasons. As a measure of sensitivity, the model was solved using different weight structures, and setting different constraints on essential crops stemming from the potential of a population growth rate that is greater than expected

Advances in Deep Learning Algorithms for Agricultural Monitoring and Management

This study examines the transformative role of deep learning algorithms in agricultural monitoring and management. Deep learning has shown remarkable progress in predicting crop yields based on historical weather, soil, and crop data, thereby enabling optimized planting and harvesting strategies. In disease and pest detection, image recognition technologies such as Convolutional Neural Networks (CNNs) can analyze high-resolution images of crops to identify early signs of diseases or pest infestations, allowing for swift and effective interventions. In the context of precision agriculture, these advanced techniques offer resource efficiency by enabling targeted treatments within specific field areas, significantly reducing waste. The paper also sheds light on the application of deep learning in analyzing vast amounts of remote sensing and satellite imagery data, aiding in real-time monitoring of crop growth, soil moisture, and other critical environmental factors. In the face of climate change, advanced algorithms provide valuable insights into its potential impact on agriculture, thereby aiding the formulation of effective adaptation strategies. Automated harvesting and sorting, facilitated by robotics powered by deep learning, are also investigated, as they promise increased efficiency and reduced labor costs. Moreover, machine learning models have shown potential in optimizing the entire agricultural supply chain, ensuring minimal waste and optimum product quality. Lastly, the study highlights the power of deep learning in integrating multi-source data, from weather stations to satellites, to form comprehensive monitoring systems that allow real-time decision-making

A nutrient recommendation system for soil fertilization based on evolutionary computation

In agricultural production, soil characteristics play a vital role in maintaining fertility by allowing crops to develop better through root nutrition with minimal energy inputs. Nitrogen (N), Phosphorus (P), and Potassium (K) are all important nitrogen fertilizers extensively utilized in crops to supply a sufficient level of nutrients and keep their production level high. However, the application is generally limited to specific crops because of the global variability in these essential nutrients. Stability in fertilizer application, growth, and root growth rate increases crop fertility and crop production. To predict the suitable nutrients for different crops and provide nutrients recommendations by analyzing the crop fertility and yield production, this paper proposes nutrient recommendations through an improved genetic algorithm (IGA) that uses time-series sensor data and recommends various crop settings. A neighborhood-based strategy is then presented to handle exploration and exploitation for optimizing the parameters to obtain the maximum yield. The method can expand knowledge by using the population exploration strategy. The final recommendation is made by using the similarity between recommended patterns and real-time sensor data. With time, crop fertility decreases due to the low level of nutrients. This crop model will help to increase yield by analysis of the seasonal fertility performance of the soil. The proposed method is also a useful tool to improve soil fertility performance by providing the nutrient recommendation of optimal conditions for crop development. Experimental results show that the proposed model can recommend optimizing patterns and increasing the yearly yield efficiently. The method can help identify the region to assess crop suitability under certain nutrients levels and give insight into nutrient suitability assessments concerning specific crops in a climate-changing world.publishedVersio

Applications of Emerging Smart Technologies in Farming Systems: A Review

The future of farming systems depends mainly on adopting innovative intelligent and smart technologies The agricultural sector s growth and progress are more critical to human survival than any other industry Extensive multidisciplinary research is happening worldwide for adopting intelligent technologies in farming systems Nevertheless when it comes to handling realistic challenges in making autonomous decisions and predictive solutions in farming applications of Information Communications Technologies ICT need to be utilized more Information derived from data worked best on year-to-year outcomes disease risk market patterns prices or customer needs and ultimately facilitated farmers in decision-making to increase crop and livestock production Innovative technologies allow the analysis and correlation of information on seed quality soil types infestation agents weather conditions etc This review analysis highlights the concept methods and applications of various futuristic cognitive innovative technologies along with their critical roles played in different aspects of farming systems like Artificial Intelligence AI IoT Neural Networks utilization of unmanned vehicles UAV Big data analytics Blok chain technology et

A survey of image processing techniques for agriculture

Computer technologies have been shown to improve agricultural productivity in a number of ways. One technique which is emerging as a useful tool is image processing. This paper presents a short survey on using image processing techniques to assist researchers and farmers to improve agricultural practices. Image processing has been used to assist with precision agriculture practices, weed and herbicide technologies, monitoring plant growth and plant nutrition management. This paper highlights the future potential for image processing for different agricultural industry contexts

Rice Production, Income Diversification and Rural Development in Myanmar

Among agricultural produce, rice is still dominating the Myanmar's agriculture sector, as it is a staple food crop and a principal export crop. Although previous market reforms and major investment in the agriculture sector have led to an increase in rice production, there have been challenges, such as limited availability of loans, poor infrastructure, application of farm inputs and the quality of seeds. My thesis comprises three essays that, together, fill the gaps in the existing literature on most of the key issues affect rural development in Myanmar. The first essay analyses the source and extent of potential productivity and efficiency gains, and investigates how Myanmar can increase its rice productivity. The data used in this thesis is taken from 634 farm households in the main rice growing regions, specifically Ayeyarwaddy, Bago and Sagaing. The stochastic production frontier and technical inefficiency models are applied to capture which farm-specific factors determine efficiency gains. The findings show that rice production in the selected regions can be improved through farm workers with better education, agricultural extension services, and efficient fertilizer and pesticide application. This essay also analyses the impact of land reforms and market reforms on rice production in Myanmar and Vietnam. Although there are differences between the two countries, especially in terms of government policy and institutions, both share some similarities when it comes to rice production. Vietnam is a more efficient rice producer than Myanmar, due to its better irrigation system, use of better quality seeds, higher application rate of fertilizers, and more intensive cropping. There may be many lessons for Myanmar to learn Vietnam's to increase the quantity and quality of its rice production by applying certified seeds and efficient use of fertilizers, and using sufficient irrigated water. The second essay examines the impact of credit policy on rice production in the selected regions. The provision of agricultural credit is used as a major tool to develop rural areas and reduce poverty in Myanmar. Despite the rapid expansion of agricultural credit by the Myanmar Agricultural Development Bank (MADB), there are some limitations on applying for credits, such as the credit amount per acre and the landholding size. A fuzzy regression discontinuity design approach is applied to identify the effects of agricultural credit, making use of the MADB's credit rule based on landholding size up to 10 acres. Although the subsidized credit scheme shows little impact on rice output and rice income, the credit program has some positive effects on total household income, suggesting a positive spillover effect on other farm income activities. The third essay assesses the determinants of income diversification from different sources and its impact upon the rural economy of Myanmar. Despite the fact that rice production still plays a major role in the rural economy, the diversity of income from both agricultural and non-agricultural activities has been part of an important strategy for rural livelihoods among farm households since the late 1980s. This essay analyses the factors determining income diversification from different sources on rural households' income, and their contribution to income inequality amongst farm households. The findings show that household's demographic characteristics, total land size, ownership of assets are the main factors leading towards income diversification. The results of the decomposition of Gini coefficient indicate that aggregate income from non-rice crops, especially pulses and beans, helps to significantly reduce income inequality among farm households in the Bago and Sagaing Regions. Overall, the results reveal that the cropping patterns for producing rice and different type of pulses and beans, as well as participation in livestock farming, are the most important factors in decreasing income inequality

Sustainability of wheat production in Southwest Iran : A fuzzy-GIS based evaluation by ANFIS

Funding Information: The financial support provided by Jahrom University, Iran, is gratefully acknowledged. Acknowledgements Publisher Copyright: © 2017 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.Peer reviewedPublisher PD

Integrated Crop-Livestock-Forest systems : a Brazilian alternative for agriculture sustainability

Tese (doutorado)—Universidade de Brasília, Centro de Desenvolvimento Sustentável, Programa de Pós-Graduação em Desenvolvimento Sustentável, 2021.A intensificação sustentável da agricultura pode desempenhar um papel central no desafio global de satisfazer a crescente demanda mundial por alimentos e, ao mesmo tempo, conservar e restaurar os ecossistemas naturais. A adoção de sistemas agrícolas sustentáveis no Cerrado e na Amazônia é globalmente relevante, por um lado devido à quantidade de produtos produzidos nestas regiões, e por outro devido aos serviços ecossistêmicos vitais prestados por estes biomas. Uma inovadora tecnologia brasileira, que apresenta características de sistemas agrícolas sustentáveis, são os sistemas de integração lavoura-pecuária-floresta (ILPF) e os sistemas de integração lavoura-pecuária (ILP). Esses são sistemas agrícolas que intensificam o uso dos recursos integrando, na escala da propriedade, a produção agrícola, pecuária e florestal, e, portanto, têm sido considerados uma estratégia promissora para aumentar a sustentabilidade agrícola nas regiões do Cerrado e da Amazônia. No entanto, ainda existem lacunas de pesquisa sobre o potencial econômico, social e ambiental dos sistemas de integração em serem utilizados como uma estratégia eficaz para promover a agricultura sustentável na fronteira agro-florestal brasileira. O principal objetivo desta tese é avaliar as três dimensões da sustentabilidade: econômica, social e ambiental, em sistemas agrícolas típicos localizados no estado de Mato Grosso, o maior produtor de grãos e de carne bovina do Brasil, um dos estados que apresenta as maiores taxas de desmatamento do país, e que abrange os biomas Amazônia, Cerrado e Pantanal, concentrando-se na geração de informação, ao nível da fazenda, para potenciar a adopção de sistemas de integração no Brasil. Inicialmente, apresentamos uma análise econômica e comparamos o desempenho econômico de um sistema de integração lavoura-pecuária com um sistema de lavoura de larga escala (soja/milho) e um sistema de pecuária extensiva (gado de corte) no período de 2005-2012. No capítulo seguinte, utilizamos a síntese emergética para avaliar e comparar o desempenho ambiental de um sistema de integração lavoura-pecuária com um sistema de lavoura de larga escala e um sistema de pecuária extensiva. Essa análise utilizou dados primários e secundários para a safra 2017/18. Indicadores econômicos como receita bruta, custos de produção e rentabilidade foram construídos para complementar as avaliações de sustentabilidade. Finalmente, no capítulo quatro, utilizamos a lógica difusa (fuzzy logic approach) para construir indicadores parciais para as dimensões econômica, ambiental e social de diferentes sistemas agrícolas, que foram sumarizados num índice de sustentabilidade global considerando dados reais para a safra 2018/2019. Realizamos uma pesquisa semiestruturada em 22 fazendas que foram classificadas considerando os três sistemas de produção agropecuária mais representativos utilizados no Cerrado e na Amazônia: i) lavoura soja/milho, ii) pecuária extensiva, e iii) sistemas de integração (lavoura-pecuária e pecuária-floresta). Nossos resultados demonstraram que os sistemas ILPF são economicamente competitivos, mesmo numa região altamente especializada na produção de grãos em larga escala. A análise de emergia evidenciou as principais contradições do sistema de agricultura em larga escala: os benefícios sociais são menores do que os custos sociais. O sistema de pecuária tradicional mostrou baixa rentabilidade e elevados impactos ambientais negativos, sugerindo que esta atividade depende de políticas públicas específicas para melhorar o seu desempenho. Por outro lado, os sistemas ILPF provaram ser uma alternativa eficiente para aumentar a produtividade da pecuária e, simultaneamente, reduzir as emissões de GEE, bem como reduzir a pressão para a abertura de novas áreas de pastagem. Além disso, os sistemas ILPF demonstraram uma maior eficiência na utilização de insumos e um desempenho equilibrado entre as dimensões econômica, social e ambiental. Estes resultados fornecem sustentação à proposta do Brasil em aprofundar o uso de sistemas de integração como parte dos seus planos de mitigação das mudanças do clima e de desenvolvimento agrícola sustentável, oferecendo informação de qualidade aos formuladores de políticas públicas para apoiar a implementação de políticas para lidar com os impactos ambientais da intensificação agrícola, ao mesmo tempo em que aumentam a produção de alimentos e promovem o desenvolvimento socioeconômico na fronteira agro-florestal brasileira.Agricultural intensification can play an essential role in the global challenge of meeting increasing global food demand while conserving and restoring natural ecosystems. The adoption of sustainable agricultural systems in the Brazilian Cerrado and Amazon is globally relevant, on the one hand due to the amount of commodities produced in these regions, and on the other by the critically important ecosystem services provided by these biomes. A Brazilian innovative technology with encompass the features of sustainable agricultural systems are the integrated crop-livestock-forest (ICLF) and the integrated crop-livestock (ICL) systems, which re-couple crop, livestock and forest production at the farm scale, and, therefore, have been considered a promising strategy to increase agricultural sustainability on Cerrado and Amazon regions. However, there are knowledge gaps about the economic, social and environmental potential of integrated systems to be used as an effective strategy to promote sustainable agriculture in the Brazilian agricultural-forest frontier. The main objective of this thesis is to assess the three dimensions of sustainability: economic, social and environmental, in typical agricultural systems located in Mato Grosso, Brazil, the largest grain and beef producer in the country, which spans the ecologically diverse biomes - Amazon, Cerrado and Pantanal -, focusing on information generation, on the farm level, to enhance adoption of integrated systems. First, we presented an economic analysis and compared the economic performance of an integrated crop-livestock system to a continuous crop (soybean/corn) system and a continuous livestock (beef cattle) production system from 2005-2012. In the next chapter, we used the emergy synthesis approach to assess and compare the environmental performance of an ICL system to a continuous crop and a continuous livestock system. Our analysis used survey and empirical case study data from the 2017/18 crop. Economic indicators such as gross revenue, production costs and profitability were calculated to complement the sustainability assessments. Finally, in the chapter four, we applied a fuzzy logic approach to build partial indicators for the economic, environmental, and social dimensions of agricultural performance, further integrated in an overall sustainability index considering actual farm data for the 2018/2019 cropping season. We surveyed 22 farms categorized among the three most representative agricultural production systems used in the Cerrado and Amazon, as follows: i) continuous crop rotation (soybean - corn), ii) continuous livestock, and iii) integrated systems (crop-livestock and livestock-forest). Our results demonstrated that the ILPF systems are economically competitive even in a region highly specialized in large-scale crop production. The emergy analysis highlighted the main contradictions of the large-scale farming system: the social benefits are lesser than the social costs. The traditional livestock system showed low profitability and high negative environmental impacts suggesting that this activity depends on specific public policies to improve its performance. In contrast, the ILPF systems proved to be an efficient alternative to increase livestock production and, simultaneously, reduce GHG emissions as well as the pressure over natural forest. Moreover, the ILPF systems showed greater efficiency in the use of inputs and a balanced performance between economic, social and environmental dimensions. These results provide further support for Brazil’s investment in integrated systems as part of its climate mitigation and sustainable agricultural development plans, and offer quality information to policy makers to support implementation of policies to deal with the environmental impacts of agricultural intensification, while simultaneously increasing food production and socioeconomic development in the Brazilian agricultural forest frontier

Precision Agriculture for Crop and Livestock Farming—Brief Review

In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.info:eu-repo/semantics/publishedVersio