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Income (BDT/dec) of the selected farmers from fish production and sale during different seasons and phases. Findings are shown in (a) the pilot phase and (b) the extended phase. Here, 1 USD = 105.00 BDT. Values are presented as mean ± standard error (SE). Differences in small letters presented above or near each error bar indicate significant differences in income between different farmers (p < 0.05).
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Waterlogging is a major problem in the south-western region of Bangladesh; this study was conducted in the eight most affected areas in order to enhance agricultural production by applying Land- and Water-based adaptive and alternative Farming Practices (LWFP). The study was designed to support target (research) farmers by raising one part of their...
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Recently, development has taken center stage as it encapsulates an intrinsic aspect of human life. Development is a fundamental requirement for improving the material conditions of communities that contend with abject poverty across all spheres of their lives. It is conversely justifiable that understanding development within the context of service...
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... On the contrary, the FCN models with high P-scores and without jagged WDPS error are capable for these applications, such as monitoring the dynamics of the WDPS pattern or spatiotemporal mapping [7,22,52]. Another application example is water-land structure (dike to pond ratio), which can reflect the ecological value of dikepond system [53][54][55][56][57]. The FCN models can clearly separate adjacent WDPS, preserving the integrity of the separation between WDPS and ensuring the correct proportion of the land part (dike). ...
A type of aquaculture pond called a dike-pond system is distributed in the low-lying river delta of China’s eastern coast. Along with the swift growth of the coastal economy, the water surfaces of the dike-pond system (WDPS) play a major role attributed to pond aquaculture yielding more profits than dike agriculture. This study aims to explore the performance of deep learning methods for extracting WDPS from high spatial resolution remote sensing images. We developed three fully convolutional network (FCN) models: SegNet, UNet, and UNet++, which are compared with two traditional methods in the same testing regions from the Guangdong–Hong Kong–Macao Greater Bay Area. The extraction results of the five methods are evaluated in three parts. The first part is a general comparison that shows the biggest advantage of the FCN models over the traditional methods is the P-score, with an average lead of 13%, but the R-score is not ideal. Our analysis reveals that the low R-score problem is due to the omission of the outer ring of WDPS rather than the omission of the quantity of WDPS. We also analyzed the reasons behind it and provided potential solutions. The second part is extraction error, which demonstrates the extraction results of the FCN models have few connected, jagged, or perforated WDPS, which is beneficial for assessing fishery production, pattern changes, ecological value, and other applications of WDPS. The extracted WDPS by the FCN models are visually close to the ground truth, which is one of the most significant improvements over the traditional methods. The third part is special scenarios, including various shape types, intricate spatial configurations, and multiple pond conditions. WDPS with irregular shapes or juxtaposed with other land types increases the difficulty of extraction, but the FCN models still achieve P-scores above 0.95 in the first two scenarios, while WDPS in multiple pond conditions causes a sharp drop in the indicators of all the methods, which requires further improvement to solve it. We integrated the performances of the methods to provide recommendations for their use. This study offers valuable insights for enhancing deep learning methods and leveraging extraction results in practical applications.
... Soil is a mixture of minerals, living and dead organisms (organic matter), water, and air. These four components interact remarkably, making soil one of our most important natural resources [Rahman et al., 2023]. However, the world faces soil depletion, or degradation [Klevinskas, 2018]. ...
... The natural attributes of contracted land are production combined with labor to form agricultural output income [54,55]. The natural attribute of homestead land is life, mainly providing safe shelter for farmers, more manifested as an asset function and ethical function [56,57]. ...
Because of the increased expansion of the non-agricultural industry spurred on by vigorous urbanization, labor migration or transfer from farm to urban regions is to become more predominant in China. Studying the effect of labor transfer on farmers’ willingness to withdraw from land is conducive to deepening the understanding of the reality of the “separation of human and farmland”. As most rural livelihoods, directly and indirectly, depend upon farming, the socio-economic impact of leaving the homestead fosters profound research value. Moreover, it would provide a decision-making reference for the government to improve the design of the rural land withdrawal system and related support policies. This article uses the survey data of 953 farmers in Shaanxi, Sichuan, and Anhui, China, to empirically analyze labor transfer’s effect on farmers’ willingness to withdraw from farmland. We construct a bivariate Probit model by eliminating the endogenous issue to craft its findings. This study outlines its findings: (i) 61.805% of the farmers were unwilling, and 18.048% were willing to withdraw from the contracted land and homestead. While 12.067% of the farmers were only willing to withdraw from the contracted land, 8.080% of the farmers were only willing to withdraw from the homestead. Further testing found a positive correlation between farmers’ willingness to withdraw from contracted land and the homestead. (ii) The overall labor transfer of households can increase the willingness of farmers to quit contracted land and homestead farming. The incomplete labor transfer of households can improve the willingness of farmers to quit contracted land. Still, it has no significant impact on the willingness of farmers to quit their homesteads. The family’s complete labor transfer incentivizes farmers’ willingness to withdraw from contracted land and the homestead, which is more potent than incomplete family labor transfer. (iii) Incomplete labor transfer of female households has an incentive effect on farmers’ willingness to quit contracted land, and the effect is more robust than that of incomplete household labor transfer. Seemingly, complete female labor transfer of households has an incentive effect on farmers’ willingness to quit contracted land and the homestead, and the effect is stronger than the complete labor transfer of the family. Because of this, the government should respect the wishes of farmers and strengthen the effective connection and mutual promotion between the homestead and contracted land withdrawal policy. Moreover, pay concentrated attention to the vital role of different types of labor transfer, and targeted labor transfer mechanisms should be used to guide farmers in an orderly manner.
... Addressing the multifaceted challenges faced by rural communities in Bangladesh is crucial for promoting sustainable livelihoods and resilience in the face of climate change impacts. The waterlogging community in Bangladesh, in particular the southwestern part, is affected by seasonal flooding and waterlogging, [3], [4], which often result in damage to crops, [5], loss of livestock, [6], and limited access to clean water and sanitation facilities, [7]. The prolonged waterlogging has resulted in the roots of trees rotting, fruit trees dying off, and vegetation dying off, [8]. ...
Bangladesh's deltaic geography makes it highly subject to natural disasters, with the southwest region being especially vulnerable to cyclones, storm surges, waterlogging during the monsoon, and soil salinity during the dry season. Despite being primarily an agricultural country, frequent natural disasters have severely impacted crop production and biodiversity, making it difficult for small coastal farmers to earn a livelihood. This study sought to identify the various income-generating activities and effective strategies that could help the waterlogged community become more resilient to the challenges posed by climate change. A mixed method, including a household survey, field visit, in-depth interview, and key informant interview, was used to collect data based on the purposive sampling technique. The collected quantitative and qualitative data were analyzed using percentage measures and narrative processes, respectively, and interpreted in the socio-cultural context to give a specific form and basis to the study. The study revealed that marginal farmers in Malopara village are particularly vulnerable to biodiversity losses that threaten their lives and livelihoods. To address these challenges, the study also found multiple income-generating activities as a way of community-based adaptation. This approach would help reduce food insecurity and provide alternative sources of income for small farmers, who are most affected by the changing climate.
... In 2018, the Government proposed the separation of homestead ownership, qualification rights, and use rights, providing a policy basis for house sharing [18][19][20][21]. The new "Land Management Law", first introduced in 2019, allows rural villagers who have settled in cities to withdraw from their homesteads with voluntarily compensation, as well as encouraging rural collective economic organizations and their members to revitalize and utilize idle homesteads and houses [22][23][24]. This policy will not only affect the willingness of a particular farmer to revitalize the idle house but also have a significant impact on other members of his kinship network. ...
China is vigorously promoting the strategy of rural revitalization, encouraging farmers to revitalize their idle houses and developing rural tourism. In rural China, kinship networks are essential in farmers’ willingness and decision–making tools. It is significant to explore the influence of kinship networks on farmers’ willingness to revitalize idle houses. This study constructs a research framework of “kinship networks–revitalization willingness–revitalization action”. It describes farmers’ kinship networks from five aspects: kinship networks structure, kinship networks relationship, kinship networks cognition, kinship networks members’ sense of belonging, and their social participation enthusiasm. Taking Bishan Village, a typical rural tourism–type ancient village, as an example, this study surveyed 197 farmers to demonstrate the influence of kinship networks on farmers willingness to revitalize idle houses. This paper uses a multiple regression model to empirically study the influence of kinship networks on farmers’ willingness to revitalize idle houses. The results show that: (1) In addition to the kinship networks structure having no significant positive impact on farmers’ willingness to revitalize idle houses, kinship networks relationship, kinship networks cognition, kinship networks members’ sense of belonging, and kinship networks members’ social participation enthusiasm all have positive effects on farmers’ willingness. (2) Considering the critical influence of kinship networks on farmers’ willingness to revitalize idle houses, the government should use the structure of kinship networks to formulate relevant policies to guide farmers to increase their willingness to revitalize their idle houses.
... Over 700 million people go hungry every day and do not have access to the basic foodstuffs needed for their existence [1]. Due to global warming, finding methods and strategies which may increase crop tolerance under different types of stress is of significance [2][3][4]. Furthermore, since arable area is limited and the need for food is growing year by year, the application of more efficient methods of food production is crucial for survival [5,6]. ...
Since corn is the second most widespread crop globally and its production has an impact on all industries, from animal husbandry to sweeteners, modern agriculture meets the task of preserving yield quality and detecting corn stress. Application of remote sensing techniques enabled more efficient crop monitoring due to the ability to cover large areas and perform non-destructive and non-invasive measurements. By using vegetation indices, it is possible to effectively measure the status of surface vegetation and detect stress on the field. This study describes the methodology for corn stress detection using red-green-blue (RGB) imagery and vegetation indices. Using the Excess Green vegetation index and calculated vegetation index histogram for healthy crop, corn stress has been effectively detected. The obtained results showed higher than 89% accuracy on both experimental plots, confirming that the proposed methodology can be used for corn stress detection using images acquired only with the RGB sensor. The proposed method does not depend on the sensor used for image acquisition and vegetation index used for stress detection, so it can be used in various different setups.
Climate change impacts create survival challenges for people in coastal areas of Bangladesh. Government responses are exercised through top-down adaptation governance, reflecting a neocolonial perspective evident in externally funded water development projects such as the Flood Control, Drainage and Irrigation (FCDI) scheme. Problematically, this form of donor ‘climate coloniality’ creates novel ecological debts that increase localised socioeconomic vulnerabilities. These vulnerabilities are concentrated within marginalised groups, although the impacts of one climate-related ecological debt, waterlogging, are not widely understood. Two critical research questions emerge from this context: (i) in what ways does waterlogging impact marginalised groups in coastal regions?; (ii) how could adaptation institutions be decolonised to reduce resultant vulnerabilities? Primary data from sociological research conducted in Jessore District in south western Bangladesh is utilised in answering these questions. The findings show that marginalised groups disproportionately endure the impacts of historically path dependent, climate-related ecological debts through multiple vulnerabilities including declining crop production, loss of domestic animals, unemployment, price increases, gendered inequalities and health impacts, linked to their exclusion from adaptation decision-making. In response to this neocolonial perspective, such structural domination needs to be challenged by decolonising adaptation institutions through integrating recognition and procedural justice. Decolonised institutions based on this justice perspective could provide a governance space for recognising community voices related to coastal ecosystems and agricultural practices.
Climate change impacts create survival challenges for local people in the coastal areas of Bangladesh. Government responses are typically exercised through top-down adaptation governance structures reflecting a neo-colonial perspective, evident in externally funded water development projects such as the Flood Control, Drainage and Irrigation (FCDI) scheme. Problematically, this form of donor ‘climate coloniality’ creates novel ecological debts that in turn increase localised socio-economic vulnerabilities. These vulnerabilities are concentrated within marginalised, poorer groups, although the attendant impacts of one climate-related ecological debt, waterlogging, are not widely understood. Two critical research questions emerge from this context: (i) in what ways does waterlogging impact marginalised groups in coastal regions?; (ii) how could adaptation institutions be decolonised to reduce resultant vulnerabilities? Primary data from research conducted in Jessore District in south western Bangladesh is utilised in answering these questions. The findings show that marginalised groups disproportionately endure the impacts of historically path dependent, climate-related ecological debts through multiple vulnerabilities such as declining crop production, loss of domestic animals and income, unemployment, price hikes for daily essentials, gendered inequalities and increasing crime, primarily resulting from their exclusion from adaptation decision-making. In response to this neo-colonial perspective, such structural domination needs to be challenged by decolonizing adaptation institutions through the integration of recognition and procedural justice interventions. Decolonized institutions based on this justice perspective could provide a governance space for recognizing local community voices related to coastal ecosystems and agricultural practices.
Waterlogging in agriculture poses severe threats to soil properties, crop yields, and farm profitability. Remote sensing data coupled with drainage systems offer solutions to monitor and manage waterlogging in agricultural systems. However, implementing agricultural projects such as drainage is associated with high uncertainty and risk, with substantial negative impacts on farm profitability if not well planned. Cost–benefit analyses can help allocate resources more effectively; however, data scarcity, high uncertainty, and risks in the agricultural sector make it difficult to use traditional approaches. Here, we combined a wide range of field and remote sensing data, unsupervised machine learning, and Bayesian probabilistic models to: (1) identify potential sites susceptible to waterlogging at the farm scale, and (2) test whether the installation of drainage systems would yield a positive benefit for the farmer. Using the K-means clustering algorithm on water and vegetation indices derived from Sentinel-2 multispectral imagery, we were able to detect potential waterlogging sites in the investigated field (elbow point = 2, silhouette coefficient = 0.46). Using a combination of the Bayesian statistical model and the A/B test, we show that the installation of a drainage system can increase farm profitability by 1.7 times per year compared to the existing farm management. The posterior effect size associated with yield, cropping area, and time (year) was 0.5, 1.5, and 1.9, respectively. Altogether, our results emphasize the importance of data-driven decision-making for agriculture project planning and resource management in the wake of smart agriculture for food security and adaptation to climate change.
