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The adoption of climate-smart agriculture to address wildfires in the Maya Golden Landscape of Belize: Smallholder farmers' perceptions
Abstract
Ecosystems around the globe are enduring wildfires with greater frequency, intensity, and severity and this trend is projected to continue as a result of climate change. Climate-smart agriculture (CSA) has been proposed as a strategy to prevent wildfires and mitigate climate change impacts; however, it remains poorly understood as a strategy to prevent wildfires. Therefore, the authors propose a multimethod approach that combines mapping of wildfire susceptibility and social surveys to identify priority areas, main factors influencing the adoption of CSA practices, barriers to their implementation, and the best CSA practices that can be implemented to mitigate wildfires in Belize's Maya Golden Landscape (MGL). Farmers ranked slash and mulch, crop diversification, and agroforestry as the main CSA practices that can be implemented to address wildfires caused by agriculture in the MGL. In order to reduce wildfire risk, these practices should, be implemented in agricultural areas near wildlands with high wildfire susceptibility and during the fire season (February-May), in the case of slash and mulch. However, socio-demographic and economic characteristics, together with a lack of training and extension services support, inadequate consultation by agencies, and limited financial resources, hinder the broader adoption of CSA practices in the MGL. Our research produced actionable and valuable information that can be used to design policies and programs to mitigate the impacts of climate change and wildfire risk in the MGL. This approach can also be used in other regions where wildfires are caused by agricultural practices to identify priority areas, barriers and suitable CSA practices that can be implemented to mitigate wildfires.
Adopting climate-smart agricultural (CSA) practices is a long-term solution for enhancing agricultural sustainability and food security under the changing climate. However, the penetration rate of CSA practices remains low worldwide. Understanding the key factors driving the adoption of CSA practices is key to increasing its penetration. This study provides a systematic review of the literature comprising 190 studies published between 2013 and 2023. Based on the reviewed literature, we provide comprehensive definitions of CSA practices from broad and narrow perspectives. We also discuss the factors influencing farmers’ decisions to adopt CSA practices from four categories: socio-demographic factors, institutional factors, resource endowment factors, and socio-economic factors. Our literature review reveals that most of the factors (e.g., age, gender, education, risk perception and preference, access to credit, farm size, production conditions, off-farm income, and labor allocation) discussed in the literature have a dual (either positive or negative) impact on CSA practice adoption. The variables such as labor endowment, land tenure security, access to extension services, access to agricultural training, membership in farmers’ organizations, non-governmental organization (NGO) support, climate conditions, and access to information consistently and positively impact CSA practice adoption. These findings provide solid evidence for designing appropriate policy instruments that help accelerate CSA diffusion and transmission. We also find gaps in CSA practice measurements, influencing factor identification, and econometric methods used for empirical analysis, which should be explored by future research.
Transformative innovation policies are gaining currency worldwide, but have been mainly studied in a Global North context and in the energy sector. This paper focuses empirically on Costa Rica's Climate Smart Agriculture policy mix. It addresses key knowledge gaps on the dynamics of transformative policy development in the agrifood sector in a Global South policy context. Results show Costa Rica's policy mix's transformative potential was inhibited by weak implementation capacity and internal and external incoherence between sectors and governance levels, leading to tensions resulting from policy-element interactions such as conflicting goals and interventions with overlapping purposes. The broader implication for theory and practice is that successful transformative policy mixes require close scrutiny of both the balance of the mix and how to fundamentally transform the mix. This includes paying more attention to the phasing out of legacy policy instruments and to how countries' particular institutional contexts and policy cultures influence transformative policymaking and implementation.
Climate change has the greatest negative impact on low-income countries, which burdens agricultural systems. Climate change and extreme weather events have caused Ethiopia’s agricultural production to decline and exacerbated food insecurity over the last few decades. This study investigates whether farmers’ awareness and perceptions of climate change play a role in climate change adaptation using climate-smart agricultural practices. To collect data, 385 households in Southern Ethiopia were sampled using a multistage sampling. A Heckman probit two-stage selection model was applied to investigate the factors influencing farmers’ perceptions to climate change and adaptation measures through adoption of climate-smart agriculture practices, complemented with key informant interviews and focused group discussions. The results indicated that most farmers (81.80%) perceived that the local climate is changing, with 71.9% reporting increased temperature and 53.15% reporting decreasing rainfall distribution. Therefore, farmers attempted to apply some adaptation practices, including soil and water conservation with biological measures, improved crop varieties, agroforestry, improved breeds, cut and carry system, controlled grazing, and residue incorporation. The empirical results revealed that farmers adaptation to climate change through adoptions of CSA practices was significantly influenced by education, family size, gender, landholding size, farming experience, access to climate information, training received, social membership, livestock ownership, farm income and extension services. The study found that farmers’ perceptions of climate change and variability were significantly influenced by their age, level of education, farming experience, and access to climate information, hence, the need to focus on enhancing the accuracy of weather information, strengthening extension services, and considering a gender-sensitive adaptation approach toward improving farmers’ knowledge and aspirations. Agricultural policies should support the efforts of farmers to increase the reliance on climate risk and alleviate farmers’ difficulties in adopting climate-smart agriculture practices.
Crop residue burning contributes to poor air quality and imposes a health burden on India. Despite government bans and other interventions, this practice remains widespread. Here we estimate the impact of changes in agricultural emissions on air quality across India and quantify the potential benefit of district-level actions using an adjoint modeling approach. From 2003 to 2019, we find that agricultural residue burning caused 44,000–98,000 particulate matter exposure-related premature deaths annually, of which Punjab, Haryana, and Uttar Pradesh contribute 67–90%. Due to a combination of relatively high downwind population density, agricultural output, and cultivation of residue-intensive crops, six districts in Punjab alone contribute to 40% of India-wide annual air quality impacts from residue burning. Burning two hours earlier in Punjab alone could avert premature deaths up to 9600 (95% CI: 8000–11,000) each year, valued at 3.2 (95% CI: 0.49–7.3) billion US dollars. Our findings support the use of targeted and potentially low-cost interventions to mitigate crop residue burning in India, pending further research regarding cost-effectiveness and feasibility.
Climate change is a global event that poses one of humanity’s most serious threats. Consequently, climate-smart agriculture (CSA) provides a once-in-a-lifetime opportunity to adapt to the effects of global climate change while lowering greenhouse gas emissions. The purpose of this study was to investigate the factors that influence farmers’ adoption of CSA practices in the Wondo Genet Woreda in southern Ethiopia. The study employed a mixed-methods approach with 213 randomly selected households (HHs). In the study, descriptive statistics, a composite score index, and an ordered logit regression model were used. Farmers’ awareness of CSA practices was high, which aided them in increasing farm income and farmland productivity. Tree planting, the use of organic manure, and the use of irrigation systems were the most CSA practices in the study area. Furthermore, the findings revealed that education, HH size, income, climate change perception, and farmland size all had statistically significant effects on farmers’ adoption of CSA. Meanwhile, the distance between the farm and the homestead had a negative and significant effect on the level of CSA adoption. Finally, socioeconomic factors should be considered when developing and implementing CSA programs for farmers.
In the last decades, natural fire regimes have experienced significant alterations in terms of intensity, frequency and severity in fire prone regions of the world. Modelling forest fire susceptibility has been essential in identifying areas of high risk to minimize threats to natural resources, biodiversity and life. There have been significant improvements in forest fire susceptibility modelling over the past two decades 2001–2021. In this study, we conducted a systematic literature review of literature covering forest fire susceptibility modelling published during this period. The review provides insights on the main themes of forest fire susceptibility modelling research, the main base input factors used in models to map forest fire susceptibility, the main researchers, the areas where this type of research were implemented, technology and models used. It also highlights collaboration opportunities, and regions, such as Central America and Africa, where mapping of forest fire susceptibility is needed. We argue that such knowledge is crucial in order to identify critical factors and opportunities which can aid in improving factor selection and forest fire management.
Wildfires are serious threats to Belize’s protected areas and ecosystems. In Belize the spatial variability of wildfire susceptibility and influencing factors at a national scale are poorly understood which hinders wildfire management interventions. Hence, in this research we conducted a joint application and performance comparison of AHP (Analytical Hierarchical Process), RF (Random Forest) and FAHP (Fuzzy Analytical Hierarchical Process). The analysis revealed that RF (AUC = 83.1%) is the model with better predictive accuracy followed by FAHP (AUC = 71.2) and AHP (AUC = 66.8). The RF, AHP and FAHP models classified 22%, 32% and 37% of the country as having high and very high wildfire susceptibility, respectively. These susceptible areas are located mainly in lowland savanna and lowland broad-leaved moist forest; especially, in areas that are unprotected, the outer boundaries of protected areas and small and isolated protected areas. The main factors that are influencing wildfire susceptibility in Belize are distance to agriculture, landcover and temperature. The comparison of these methods provides a better understanding of the implementation and performance of knowledge-based methods (AHP and FAHP) in comparison with a well-established machine learning method (RF) in a country where local data availability, accessibility and reliability are an issue. Our study also provides new wildfire susceptibility information to Belize’s wildfire managers and demonstrates the need to improve wildfire management.
Agriculture is a sector with a long tradition and an important contribution to economic growth in Vietnam, providing food security and generating revenue for the country's exports. However, agricultural activities in Vietnam are significantly affected by climate change. In Vietnam, the central coastal area in general and Ninh Thuan province in particular are the most vulnerable areas to natural disasters. In that context, climate-smart agriculture (CSA) is a good strategy to help local famers coping with climate change while securing food security. This study has two main objectives: to assess farmers' awareness on disasters and to analyze factors affecting the decision to apply CSA practices of farming households in Ninh Thuan. The study conducted focus group discussion, in-depth interview and survey of 436 households in 3 districts of Ninh Thuan to collect data. Research results showed that local farmers have a high awareness of natural disasters trends and impacts on agricultural livelihoods. Facing with disasters in the context of climate change, farming households have applied some models of CSA such as minimal tillage, intercropping, crop rotation, crop transformation, and the use of plant varieties. Binary logit regression analysis indicated that age, education level, household income, membership of local associations, training attendance, and support policies were variables significantly influencing the farmers' decision to apply CSA. Challenges for CSA adoption include lack of capital, information, technical support and land ownership institutions in Vietnam. The study also made recommendations to promote CSA in Vietnam, including reforming the legal system, strengthening agricultural extension services, and providing technical and capital support to farmers.
With climate change complicating South African households’ ability in attaining food security, climate-smart agriculture has been a significant recommendation for small-scale farming households. This paper examined the contribution of CSA adaptation to household food security in King Cetshwayo District Municipality’s small-scale farming system. Also, the paper investigated the relative popularities of specific CSA practices and the factors that determined ‘ ‘household’s choices among them. It involved comparing two local municipalities—Mthojaneni and uMhlathuze; selected based on their agricultural potentials. Data collection was done, using structured questionnaires, from 327 small-scale farmers sampled through a multi-stage technique. The data were analyzed with descriptive statistics, composite score index, the binary logistic and multinomial logistic regression models. Findings reveal that adaptation of CSA practices has a significant and positive influence on household food security. Further analysis shows that gender significantly and positively influenced household food security in Mthonjaneni but had no significant influence in uMhlathuze. Household food security was significantly and positively influenced by farm income, income from non-farm sources, farming experience and household participation in Mthonjaneni and uMhlathuze. By contrast, household size and dependency ratio significantly but negatively influenced household food security in Mthonjaneni and uMhlathuze. This paper argues that small-scale farmers should incorporate CSA practices as much as possible to maximize their benefits and improve their productivity and chances of being food secure.
Purpose
This study aims to examine smallholder farmers’ perceptions toward the adoption of climate-smart agriculture (CSA) in smallholder farmers in the Upper Blue Nile Highlands of Ethiopia. Available research focused on profitability and economic constraints alone, disregarding the farmers’ perception of the adoption of CSA innovations. There is relatively little empirical work on farmers’ perceptions of innovations. Hence, a critical research gap that will strengthen CSA innovation research and practice includes understanding farmers’ perceptions about CSA innovations and how these perceptions interact with their adoption.
Design/methodology/approach
A cross-sectional household survey was conducted among 424 smallholder farmers selected from five agro-ecosystems. A structured questionnaire was used to collect primary data and a review of literature and documents was used to collect secondary data. The study used a multivariate probit model to examine perception factors affecting the likelihood of adopting multiple CSA innovations. The dependent variables were eight CSA innovations, while the independent variables were crafted from the three pillars of CSA.
Findings
Major CSA innovations adopted by farmers include improved variety, crop residue management, crop rotation, compost, row planting, soil and water conservation, intercropping and agroforestry. Farmers’ perception toward CSA innovations includes: CSA innovations sustainably increase productivity and income; enhance soil fertility; diversify livestock feed and energy sources; reduce soil erosion, weed infestation and crop failure; enhance soil organic matter, reduce chemical fertilizer use and rehabilitate land. Farmers’ positive perceptions of the benefits of CSA innovations for increasing crop productivity, reducing agricultural vulnerability to climate change and lowering farm greenhouse gas emissions have boosted adoption.
Practical implications
Farmers’ perceptions toward CSA innovations must be enhanced to increase the adoption of CSA innovations in the smallholder agriculture system. The CSA innovation scale-up strategies should focus on farmers’ perception of CSA innovation benefits toward food security, climate change adaption and mitigation outcomes. Awareness of CSA needs the close collaboration of public extension as well as local institutions such as farmers’ training centers.
Originality/value
The study adopts a multivariate probit model that models farmers’ simultaneous CSA innovation choices. Hence, this study contributes to the literature in four significant areas. First, it argues for differential treatment of the perception of smallholder farmers about innovations is needed. Second, it recognizes the interdependence of the adoption of innovations. Third, it directly assesses the farmers’ perception, while others use proxies to measure it. Finally, there are limited or no studies that address the perception of innovations within the lens of adopter perception theory.
Climate change threatens rural livelihoods by adversely affecting agricultural production through reduced crop yields, harvest loss, and increased cost of production. Climate-smart agriculture (CSA) practices have emerged as a possible solution to ensure food security by adapting to climate risks on the one hand, and mitigating GHG emissions from agriculture, on the other hand. However, resource-poor farmers often face both financial and knowledge constraints to adopt CSA practices, and thus, institutional support plays a crucial role in overcoming those barriers. Therefore, this study uses primary data collected from 248 farm households in the Eastern Indian state of Odisha to examine the role of institutional factors in CSA adoption. Almost 95% of the sample farmers experienced the effects of climate change, and many have adopted CSA practices such as rescheduling planting (78%), crop rotation (56%), crop diversification (35%), micro-irrigation (17%), and drought-resistant seeds (16%). Probit models are estimated to explore the key determinants of the adoption of these five major practices. Results show that factors such as government extension service, farmer field school participation, subsidies, access to energy, and perception of climate shocks are the major determinants. Further, the coefficient of interaction between landholding and credit availability on the decision to adopt CSA is positive. Thus, agricultural policies to improve institutional support, such as subsidies on farm machinery, extension support, credit facility, and field demonstration of technologies, are crucial to upscale CSA adoption in the region.
This paper reviews the progress in climate change adaptation and mitigation actions in sub-Saharan Africa farming systems. Farmers, organizations and Governments in the region have developed policies and innovations to adapt to and mitigate the impacts of climate change. It appears that the developed and implemented innovative adaptive farming systems and technologies have culminated into resultant overall productivity improvement in farming systems, necessitating scaling up in order to widely strengthen the resilience and adaptive capacity of vulnerable communities to the impacts of climate change. Additionally, climate governance instruments that are aligned to the ratified international treaties have been developed and related programs have been rolled out in different countries. This offers hope for well-coordinated efforts and interventions for the mitigation and adaptation to the adverse impacts of climate change on the environment and livelihoods. Observably, there is a pressing need to scale up climate smart innovations sustainably through creation of an enabling policy environment, capacity building, and conducting climate change related research and outreach, and effective dissemination of climate technologies and information, especially in remote areas in the region. Since climate change is a global issue, local initiatives and actions for mitigating and adapting to the adverse impacts ought to be well integrated into the broader international context.
Rigorous efforts should be channeled to the current low adoption of climate-smart agricultural practices (CSAPs) in sub-Saharan African countries to improve food production. What determines the adoption level and intensity of CSAPs among smallholder farmers in Kenya? While considering their joint adoption, smallholder farmers' CSAPs adoption determinants were assessed based on a sample size of 300 smallholder farmers in Western Kenya. The CSAPs considered were animal manure, soil water conservation, agroforestry, crop diversification, and crop-livestock integration. A multivariate and ordered probit model was used to assess the determinants of joint adoption of CSAPs in Western Kenya. Both complements and substitutes between CSAPs were established. The multivariate probit analysis revealed that household head's gender, education, age, family size, contact with extension agents, access to weather information, arable land, livestock owned, perceived climate change, infertile soil, and persistent soil erosion influenced CSAPs adoption. The ordered probit model revealed that gender, arable land, livestock owned, soil fertility, and constant soil erosion were crucial determinants of CSAPs adoption. The findings implied that policymakers and relevant stakeholders should consider farmer, institutional, and biophysical factors in upscaling or promoting the adoption of CSAPs.
Agricultural production is increasingly vulnerable to risks and uncertainties associated with climate changes. Climate-smart agriculture (CSA) has been proposed to address challenges in agricultural production such as food security, water shortage, drought, and soil erosion, etc. The benefits of CSA adoption for farmers have been debated. Many previous studies have indicated that impacts seem to be affected by selection bias. However, controlling such selection bias has not been considered in studies on the CSA adoption. Thus, in this study, we analyzed the impacts of CSA adoption on major economic indicators for rice farmers. The Propensity Score Matching (PSM) method was employed to address such selection bias for a case study on rice farmers in Thai Nguyen province, Vietnam. Comparing main economic indicators, we found significant differences in rice yields and used seed inputs between CSA and non-CSA adopters. Ignoring selection bias control resulted in overestimation of economic returns. The results also indicate limited contribution of CSA adoption to a reduction in pesticide and herbicide usage, and an increase in use of organic fertilizers. Some implications for further research are also discussed.
CONTEXT
Climate uncertainty challenges the livelihoods of smallholder farmers in sub-Saharan Africa. Awareness of climate-smart agricultural (CSA) practices and access to climate-smart technologies are key factors in determining the utilization of farm and land management practices that may simultaneously decrease greenhouse gas emissions, increase the adaptive capacity of farmers, and improve food security.
OBJECTIVE
Understanding how biophysical and socio-economic constraints affect the adoption of CSA practices and technologies plays an essential role in policy and intervention planning. Our objective was to identify these constraints among smallholder farmers in Taita Taveta County of Southeast Kenya across varying agro-ecological zones.
METHODS
We conducted a Climate-Smart Agriculture Rapid Appraisal that consisted of four mostly gender-disaggregated smallholder farmer workshops (102 participants), a household survey (65 participants), key-informant interviews (16 informants), and four transect walks.
RESULTS AND CONCLUSIONS
Our results indicate a dissonance in the perceived awareness of CSA practices and utilization of CSA technologies between state actors and farmers. State actors emphasize lack of awareness as a barrier to adoption, while farmers express knowledgeability regarding environmental change and climate-smart practices but are confined by limitations and restrictions posed by e.g. market mechanisms, land tenure issues, and lack of resources. These restrictions include e.g. uncertainty in product prices, lack of land ownership, scarcity of arable land, and simply lack of capital or willingness to invest. Farmers are further challenged by the emergence of new pests and human–wildlife conflicts. Our research findings are based on the contextual settings of Taita Taveta County, but the results indicate that adopting CSA practices and utilizing technologies, especially in sub-Saharan regions that are heavily based on subsistence agriculture with heterogenous agro-ecological zones, require localized and gender-responsive solutions in policy formation and planning of both agricultural extension services and development interventions that take into account the agency of the farmers.
SIGNIFICANCE
This study contributes to existing climate change adaptation research by increasing our understanding of how physical and socio-economic constraints can affect the adoption of new farm and land management practices, and how CSA-based intervention strategies could be restructured by local stakeholders to be more inclusive.
Transformational adaptation defines as 'changes the fundamental attributes of a system in response to climate and its effects.' Farmers deal with the natural environment and its components such as rainfall, temperature, humidity, and soil condition, which have a high range of variability and uncertainty for their cultivation. The present study focused on the impacts of climate change on the settler community who engage in agriculture as their mainstay and respond to the scenario. Quantitative and qualitative methods have been applied. Twenty samples from a village in the NCP have been selected. Primary outcomes of this study are (a) total awareness of perceptions on climate change; (b) the ambient temperature has been increasing and resulting in more heat stress; (c) frequent and severe occurrence of extreme rainfall anomalies and increasing trend of natural calamities. The area farmers have been adopting several strategies to overcome the negative impacts of climate change, such as transforming from intensification to more intensification that can be identified as Climate Smart Agriculture; crop diversification and adaptation of drought tolerance crops; transforming from agriculture to animal husbandry, and out-migration of unemployed or evicted youth from agriculture to non-agriculture. Institutional involvement is essential to strengthening the adaptative strategies of the people by providing an appropriate crop calendar and suitable crop combination and aware of the way of improving the use of the efficiency of available water for improving the living standard of the people.
Mountainous regions are highly hazardous, and these hazards often lead to loss of human life. The Hindu Kush Himalaya (HKH), like many mountainous regions, is the site of multiple and overlapping natural hazards, but the distribution of multi-hazard risk and the populations exposed to it are poorly understood. Here, we present high-resolution transboundary models describing susceptibility to floods, landslides, and wildfires to understand population exposure to multi-hazard risk across the HKH. These models are created from historical remotely sensed data and hazard catalogs by the maximum entropy (Maxent) machine learning technique. Our results show that human settlements in the HKH are disproportionately concentrated in areas of high multi-hazard risk. In contrast, low-hazard areas are disproportionately unpopulated. Nearly half of the population in the region lives in areas that are highly susceptible to more than one hazard. Warm low-altitude foothill areas with perennially moist soils were identified as highly susceptible to multiple hazards. This area comprises only 31% of the study region, but is home to 49% of its population. The results also show that areas susceptible to multiple hazards are also major corridors of current migration and urban expansion, suggesting that current rates and patterns of urbanization will continue to put more people at risk. This study establishes that the population in the HKH is concentrated in areas susceptible to multiple hazards and suggests that current patterns of human movement will continue to increase exposure to multi-hazards in the HKH.
Climate change poses significant challenges to agriculture, with serious impacts on smallholder farmers’ food security and livelihoods. Climate-smart agriculture (CSA) is being promoted to facilitate climate change adaptation and mitigation. While there is evidence that CSA supports smallholders’ adaption to climate change, the rate of CSA adoption remains low, particularly in sub-Saharan Africa. Previous studies have explained the low adoption based on generic factors such as farm, farmer, institutional and location characteristics, yet little is known about the role of farmers’ cognitive traits. This study investigates the influence of farmer entrepreneurial orientation, a cognitive trait reflecting a farmer’s innovativeness, proactiveness and propensity to take risks. We use data from smallholder potato farmers in Kenya and estimate a set of multivariate probit models to analyse the adoption decisions. Results show that risk-taking positively influences the adoption of irrigation, changing cultivation calendar, use of certified seed, crop rotation and soil testing. Proactiveness is positively related to the use of irrigation, changing the cultivation calendar and use of certified seed, while it is negatively related to intercropping. Contrary to our hypothesis that innovative farmers are more likely to adopt CSA practices, we find a negative relation between innovativeness and the use of certified seed. After categorizing CSA practices based on the main resources required, we find that risk-taking is positively associated with the adoption of practices that require high intensity of skilled labour and financial resources. Innovativeness is negatively associated with practices that require high intensity of financial resources. Lastly, we find proactiveness to be positively associated with the adoption of finance-intensive practices but negatively associated with unskilled-labour-intensive practices. These findings imply, first, that development practitioners should consider the interrelations among CSA practices and farmer entrepreneurial orientation in designing development interventions. Second, policy-makers need to create an environment conducive to farmer entrepreneurship as an indirect way to support the adoption of appropriate CSA practices.
The effects of climate change and variability cause a shift in climatic patterns and increasing shocks. These changes and shocks are affecting soil that is the backbone of many, particularly the farming communities. Climate-Smart soil (CSS) practices among farmers are known to rehabilitate and protect it. These practices will improve soil fertility, increase crop productivity and mitigate climate change as soil act as carbon sinks. The CSS practices uptake is low and varied among the farmers due to differences in farmer attributes, resource endowment, farm characteristics, CSS practice requirements, and climate change perceptions. This study examines the socio-economic factors that influence the adoption choice of CSS practices among farmers in three Counties: Kakamega, Bungoma, and Siaya, in western Kenya. Using a multi-stage sampling technique, the data was collected through structured interviews with the aid of a close-ended questionnaire. A multinomial logit model was used to analyze the socio-economic factors influencing the choice of CSS practice among farmers. The findings indicate that the household head age, education level, gender, farming experience, duration of the practice, farm size, plot size, number of plots in the farm, form of land ownership influenced the adoption of CSS practices. Therefore, development practitioners should consider these factors that inform the CSS practices adoption when rolling out programs that aim to increase the uptake. Policies, which improve agricultural education levels, climate change sensitization and financing, access to resources, and gender mainstreaming to address inequalities should be put in place to increase and bridge the gap of varied uptake of CSS practices among farmers.
Fine dead fuel load is one of the most significant components of wildfires without which ignition would fail. Several studies have previously investigated 1-h fuel load using standard fuel parameters or site-specific fuel parameters estimated ad hoc for the landscape. On the one hand, these methods have a large margin of error, while on the other their production times and costs are high. In response to this gap, a set of models was developed combining multi-source remote sensing data, field data and machine learning techniques to quantitatively estimate fine dead fuel load and understand its determining factors. Therefore, the objectives of the study were to: (1) estimate 1-h fuel loads using remote sensing predictors and machine learning techniques; (2) evaluate the performance of each machine learning technique compared to traditional linear regression models; (3) assess the importance of each remote sensing predictor; and (4) map the 1-h fuel load in a pilot area of the Apulia region (southern Italy). In pursuit of the above, fine dead fuel load estimation was performed by the integration of field inventory data (251 plots), Synthetic Aperture Radar (SAR, Sentinel-1), optical (Sentinel-2), and Light Detection and Ranging (LIDAR) data applying three different algorithms: Multiple Linear regression (MLR), Random Forest (RF), and Support Vector Machine (SVM). Model performances were evaluated using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), the coefficient of determination (R 2) and Pearson's correlation coefficient (r). The results showed that RF (RMSE: 0.09; MSE: 0.01; r: 0.71; R 2 : 0.50) had more predictive power compared to the other models, while SVM (RMSE: 0.10; MSE: 0.01; r: 0.63; R 2 : 0.39) and MLR (RMSE: 0.11; MSE: 0.01; r: 0.63; R 2 : 0.40) showed similar performances. LIDAR variables (Canopy Height Model and Canopy cover) were more important in fuel estimation than optical and radar variables. In fact, the results highlighted a positive relationship between 1-h fuel load and the presence of the tree component. Conversely, the geomorphological variables appeared to have lower predictive power. Overall, the 1-h fuel load map developed by the RF model can be a valuable tool to support decision making and can be used in regional wildfire risk management.
There are disproportionate adverse impacts related to climate change on rural subsistence farmers in southern Belize, Central America who depend directly on natural resources for their food and livelihood security. Promoting a more resilient farming system with key climate-smart agriculture (CSA) adaptations can improve productivity, sustainability, and food security for Mayan milpa farming communities. Once a sustainable system, the milpa has become less reliable in the last half century due to hydroclimatic changes (i.e., droughts, flooding, hurricanes), forest loss, soil degradation, and other factors. Using interviews with both milpa farmers and Extension officers in southern Belize. This qualitative study finds several socio-ecological system linkages of environmental, economic, socio-cultural, and adaptive technology factors, which influence the capacity for increasing CSA practices. Agriculture Extension, a government service of Belize, can facilitate effective CSA adaptations, specifically, an increase in mulching, soil nutrient enrichment, and soil cover, while working as partners within Maya farming traditions. These CSA practices can facilitate more equitable increases in crop production, milpa farm system sustainability, and resilience to climate change. However, there are several institutional and operational barriers in Extension which challenge their efficacy. Recommendations are presented in this study to reduce Extension barriers and promote an increase in CSA practices to positively influence food and livelihood security for milpa communities in southern Belize.
Climate change (CC) effects on agriculture development and households’ vulnerability are widely recognized. Being aware of the induced effects of climatic risks, farmers have adopted adaptation strategies to build resilience. Analyzes the determinants of choice of adaptation strategies using binary models can lead to an estimation bias, since the number of adopted strategies may be correlated. This paper analyzes farm households’ perception of CC, the determinants of choice of the number of adopted practices, and correlation between the most used climate-smart strategies in subsistence agriculture. Zero-inflated Poisson regression and multivariate analysis are employed using data collected from 704 farm households in Northern Togo. Households’ minimum consumption needs, gender, land, access to credit and extension services are the main determinants of the choice of the number of adopted strategies. The use of resistance and high yielding varieties, crops and livestock integration, soil and water conservation practice, the use of organic fertilizer, and adjustment of sowing time are the most adopted farming practices. A strong complementarity between the adopted practices for agriculture development was found. Factors that influence households’ choice of adaptation strategies include gender, household location, education level, family size, and allocated labor. Institutional factors including market access, access to credit, and extension services are also key determinants in promoting the use of climate-smart practices that are environmentally friendly.
Forest fires are a major environmental issue because they are increasing as a consequence of climate change and global warming. The present study was aimed to model forest fire hazard using the ordered weighted averaging (OWA) multi-criteria evaluation algorithm and to determine the role of human, climatic, and environmental factors in forest fire occurrence within the Golestan National Park (GNP), Iran. The database used for the present study was created according to daily classification of climate changes, environmental basic maps, and human-made influential forest fire factors. In the study area, the forest fires were registered using GPS. Expert opinions were applied through the analytic hierarchy process (AHP) to determine the importance of effective factors. Fuzzy membership functions were used to standardize the thematic layers. The fire risk maps were prepared using different OWA scenarios for man-made, climatic, and environment factors. The findings revealed that roads (weight = 0.288), rainfalls (weight = 0.288), and aspects (weight = 0.255) are the major factors that contribute to the occurrence of forest fire in the study area. The forest fire maps prepared from different scenarios were validated using the relative operating characteristic (ROC) curve. Values of forest fire maps acquired from scenarios of human, environment, climate factors and their combination were 0.87, 0.731, 0.773 and 0.819, respectively.
Extreme wildfires are increasing in frequency globally, prompting new efforts to mitigate risk. The ecological appropriateness of risk mitigation strategies, however, depends on what factors are driving these increases. While regional syntheses attribute increases in fire activity to both climate change and fuel accumulation through fire exclusion, they have not disaggregated causal drivers at scales where land management is implemented. Recent advances in fire regime modeling can help us understand which drivers dominate at management-relevant scales. We conducted fire regime simulations using historical climate and fire exclusion scenarios across two watersheds in the Inland Northwestern U.S., which occur at different positions along an aridity continuum. In one watershed, climate change was the key driver increasing burn probability and the frequency of large fires; in the other, fire exclusion dominated in some locations. We also demonstrate that some areas become more fuel-limited as fire-season aridity increases due to climate change. Thus, even within watersheds, fuel management must be spatially and temporally explicit to optimize effectiveness. To guide management, we show that spatial estimates of soil aridity (or temporally averaged soil moisture) can provide a relatively simple, first-order indicator of where in a watershed fire regime is climate vs. fuel-limited and where fire regimes are most vulnerable to change.
The problem of forest fires in Yakutia is not as well studied as in other countries. Two methods of machine learning classifications were implemented to determine the risk of fire: MaxENT and random forest. The initial materials to define fire risk factors were satellite images and their products of various spatial and spectral resolution (Landsat TM, Modis TERRA, GMTED2010, VIIRS), vector data (OSM), and bioclimatic variables (WORLDCLIM). The results of the research showed a strong human influence on the risk in this region, despite the low population density. Anthropogenic factors showed a high correlation with the occurrence of wildfires, more than climatic or topographical factors. Other factors affect the risk of fires at the macroscale and microscale, which should be considered when modeling. The random forest method showed better results in the macroscale, however, the maximum entropy model was better in the microscale. The exclusion of variables that do not show a high correlation, does not always improve the modeling results. The random forest presence prediction model is a more accurate method and significantly reduces the risk territory. The reverse is the method of maximum entropy, which is not as accurate and classifies very large areas as endangered. Further study of this topic requires a clearer and conceptually developed approach to the application of remote sensing data. Therefore, this work makes sense to lay the foundations of the future, which is a completely automated fire risk assessment application in the Republic of Sakha. The results can be used in fire prophylactics and planning fire prevention. In the future, to determine the risk well, it is necessary to combine the obtained maps with the seasonal risk determined using indices (for example, the Nesterov index 1949) and the periodic dynamics of forest fires, which Isaev and Utkin studied in 1963. Such actions can help to build an application, with which it will be possible to determine the risk of wildfire and the spread of fire during extreme events.
Forest fires have increased at an alarming rate in recent years, with multiple consequences in Nepal's forest ecosystem and landscapes. The research used remote sensing and GIS technology as well as statistical tools for developing forest fires risk models in two major landscapes of Nepal, i.e., Terai Arc Landscape (TAL) and Chitwan Annapurna Landscape (CHAL). A multi-parametric weighted index model was adopted to derive and demarcate the forest fire-risk map with risk variables such as vegetation, topographic factors, land surface temperature, and proximity to the road and settlements. To enhance the use of a fire risk map, collinearity between variables was checked (VIF <2) and validated with the Moderate Resolution Imaging Spectroradiometer (MODIS) hotspots and Kernel Density Estimation (KDE) method. The MODIS hotspot data from 2001 to 2018 was also evaluated which indicates that the number of fire counts has a strong relation (R² =0.82) with the burn area. Broadleaved forest in the pre-monsoon season is highly vulnerable to forest fire. More than half of the total forested area (65%) is in high fire risk, particularly in the TAL region. The study results could assist the decision-makers to implement preventive measures by minimizing the risk and impacts of forest fires.
The optimization of forest management in roadsides is a necessary task in terms of wildfire prevention in order to mitigate their effects. Forest fire risk assessment identifies high-risk locations, while providing a decision-making support about vegetation management for firefighting. In this study, nine relevant parameters: elevation, slope, aspect, road distance, settlement distance, fuel model types, normalized difference vegetation index (NDVI), fire weather index (FWI), and historical fire regimes, were considered as indicators of the likelihood of a forest fire occurrence. The parameters were grouped in five categories: topography, vegetation, FWI, historical fire regimes, and anthropogenic issues. This paper presents a novel approach to forest fire risk mapping the classification of vegetation in fuel model types based on the analysis of light detection and ranging (LiDAR) was incorporated. The criteria weights that lead to fire risk were computed by the analytic hierarchy process (AHP) and applied to two datasets located in NW Spain. Results show that approximately 50% of the study area A and 65% of the study area B are characterized as a 3-moderate fire risk zone. The methodology presented in this study will allow road managers to determine appropriate vegetation measures with regards to fire risk. The automation of this methodology is transferable to other regions for forest prevention planning and fire mitigation.
Background
The adoption of climate-smart agricultural (CSA) practices is expected to improve farmers’ adaptation to climate change and also increase yields while simultaneously curbing greenhouse gas (GHG) emissions. This paper explores the determinants of smallholder farmers’ participation in GHG-emitting activities. It also estimates the impact of CSA activities on reducing GHG emissions.
Methods
The findings are based on survey data obtained from 350 smallholder farmers in the East Gonja district of Northern Ghana. We adopted the generalized Poisson regression model in identifying factors influencing farmers’ participation in the GHG emission practices and inverse-probability-weighted regression adjustment (IPWRA) to estimate the impact of CSA adoption on GHG emissions.
Results
Most farming households engaged in at least one emission activity. The findings of the generalized Poisson model found that wealthier households, higher education, and households with access to extension services were less likely to participate in GHG emission activities. There was also evidence that CSA adoption significantly reduces GHG emissions.
Conclusion
Advocacy in CSA adoption could be a necessary condition for environmental protection through the reduction of GHG emissions.
Forest fires are a natural phenomenon which might have severe implications on natural and anthropogenic ecosystems. Future projections predict that, under a climate change environment, the fire season would be lengthier with higher levels of droughts, leading to higher fire severity. The main aim of this paper is to perform a spatiotemporal analysis and explore the variability of fire hazard in a small Greek island, Skiathos (a prototype case of fragile environment) where the land uses mixture is very high. First, a comparative assessment of two robust modeling techniques was examined, namely, the Analytical Hierarchy Process (AHP) knowledge-based and the fuzzy logic AHP to estimate the fire hazard in a timeframe of 20 years (1996-2016). The former technique was proven more representative after the comparative assessment with the real fire perimeters recorded on the island (1984-2016). Next, we explored the spatiotemporal dynamics of fire hazard, highlighting the risk changes in space and time through the individual and collective contribution of the most significant factors (topography, vegetation features, anthropogenic influence). The fire hazard changes were not dramatic, however, some changes have been observed in the southwestern and northern part of the island. The geostatistical analysis revealed a significant clustering process of high-risk values in the southwestern and northern part of the study area, whereas some clusters of low-risk values have been located in the northern territory. The degree of spatial autocorrelation tends to be greater for 1996 rather than for 2016, indicating the potential higher transmission of fires at the most susceptible regions in the past. The knowledge of long-term fire hazard dynamics, based on multiple types of remotely sensed data, may provide the fire and land managers with valuable fire prevention and land use planning tools.
Forest fires are one of the major environmental problems that cause harmful economic and ecological damage. The Algerian forest and in particular the Tlemcen forest massif where the national park is located are very affected by the repetition and the danger of fires. We count 1600 fire starts recorded during the period from 1980 to 2015. This situation is influenced by the ecotourism status and the high human accessibility, in the forest area as well as the significant development of road infrastructure in the middle of the forest. Noting also the characteristics of the plant formations and topographical in the area seriously promote the flammability and combustibility of the vegetation. Faced with this problem, it is necessary to protect the Tlemcen forest massif by mapping the risk zoning of forest fires. In order to achieve this objective, we followed an approach inspired by the Turkish model developed by Erten et al. (In: XXth ISPRS congress, youth forum, July 12–23, 2004, Istanbul, Turkey, pp 33–39, 2004) and based on the multi-criteria analysis method AHP with a hierarchical combination of six parameters, ecological, topographic and human on the one hand, and a classification of these parameters according to their sensitivities to fires on the other hand. This uses remote sensing data and GIS tools. The results obtained gave five risk classes (very low, low, medium, high and very high); the high and very high risk occupy 48.27% of the total surface studied. The validation of these results was made by the multitemporal analysis of satellite images dating from 2001 to 2017 and the superimposition of the burnt spaces detected on the fire risk map. The final documents provide valuable assistance to decision-makers and facilitate the protection and sustainable management of forests.
The forest fire hazard mapping using the accurate models in the fire-prone areas has particular importance to predict the future fire occurrence and allocate the resources for preventing the fire ignition. This research aimed to compare the accuracy of some individual models including boosted regression tree (BRT), classification and regression trees (CART), functional discriminant analysis (FDA), generalized linear model (GLM), mixture discriminant analysis (MDA), random forest (RF) and two new hybrid models including FDA-GLM-MDA and RF-CART-BRT for predicting the fire hazard in a fire-prone area in the northeast Iran, Golestan Province. For this purpose, a comprehensive dataset from ten effective parameters including digital elevation model (DEM), slope angle (SA), plan curvature (PC), topographic wetness index (TWI), annual rainfall mean (ARM), annual temperature mean (ATM), wind effect (WE), distance to urban areas (DTU), distance to streams (DTS) and distance to roads (DTR) was created in GIS. Furthermore, 3705 historical fire locations in the Golestan Province from 2002 to 2017 were obtained from MODIS fire product dataset. Then, the variable importance was assessed using the XGBoost machine learning (ML) technique. Finally, the individual and hybrid models were evaluated using the ROC-AUC method. The results showed that the DTU was the most important factor in modeling and mapping the fire hazard in the Golestan Province. Also, the results demonstrated that the individual random forest (RF) (AUC = 0.855) and hybrid RF-CART-BRT algorithms (AUC = 0.854) were the most accurate predictive models for mapping the fire hazard in the Golestan Province, respectively. Considering the high significance of DTU in fire occurrence in this study, the area of fire hazard classes in fourteen different counties of the Golestan Province was calculated using the most accurate model (RF model). The final results indicated that Minudasht County had the most area (35.55%) of fire hazard in the very high fire hazard class. The results of this study are very useful for local forest managers to control the future fires using the best model in the natural areas of the Golestan Province, especially in Minudasht County. The protective management of the natural areas of the Golestan Province would be performed based on the fire hazard maps produced by RF and RF-CART-BRT algorithms. We recommend applying these models for fire danger mapping in fire-prone areas around the world which have semi-arid conditions. More comparative assessment of individual and ensemble models for fire danger mapping in semi-arid areas around the world could provide a baseline for monitoring fire danger in similar conditions.
Current research focuses disproportionately on the characteristics of farmers to understand the factors that influence the introduction of climate-smart agriculture (CSA). As a result, there has been a failure to take a holistic view of the range of drivers and barriers to CSA implementation. Many aspects of technologies or practices that may encourage or inhibit the implementation of CSA and define its applicability are, therefore, not systematically considered in the design of interventions. The uptake of any practice should depend on both farmers' characteristics and factors inherent in the practice itself. This paper, therefore, examines procedures for incorporating the applicability of CSA practices in a farm-level analysis based on the investigations conducted in King Cetshwayo District Municipality (KCDM) of the KwaZulu-Natal (KZN) Province of South Africa. How the farmers perceived the social, technical, economic, and environmental compatibility of the practices constituted the key goal of the inquiry. Data were collected through structured interviews using close-ended questionnaires, from a sample of 327 small-scale farmers (farmers with farm sizes of less than or equal to 5 hectares). The analysis made use of the Acceptance Level Index (ALI) and Composite Score Index (CSI). This paper establishes that, based on social compatibility, the farmers showed high acceptance for cultivation of cover crops (ALI = 574), agroforestry (ALI = 559), and diet improvement for animals (ALI = 554), based on technical compatibility, the use of organic manure (ALI = 545), rotational cropping (ALI = 529), mulching (ALI = 525) and cultivation of cover crops (ALI = 533) were highly accepted. With economic compatibility in perspective, the farmers showed high preference for mulching (ALI = 541), organic manure (ALI = 542) and rotational cropping (ALI = 515), while the use of organic manure (ALI = 524) was highly embraced based on environmental compatibility. Consequently, it is recommended that policies aimed at mainstreaming CSA technologies should pay adequate attention to their applicability in locations under consideration and emphasize the critical role of the provision of information on CSA technologies or practices.
Wildfires have always been an integral part of the ecology of many terrestrial ecosystems, but their frequency is increasing in many parts of the world. Wildfires were once a natural phenomenon, but after humans learned to control fire, it has been used as a management tool to increase soil fertility, to regenerate natural vegetation for grazing and to control competing vegetation. However, currently uncontrolled wildfires threaten not only natural vegetation, landscape biodiversity, communities and economies, but they also release large amounts of carbon dioxide, thus contributing to global temperature increase. Higher temperatures and drier summers have increased the risk of wildfires in biodiversity rich areas of European Mediterranean countries and have resulted in human casualties. The aim of this article is to investigate whether agroforestry, the practice of integrating woody vegetation and agricultural crops and/or livestock, could be a management tool to reduce wildfires in European Mediterranean countries. Fire events from 2008 to 2017 and data of land cover and land use were spatially correlated. Results indicated that agroforestry areas had fewer wildfire incidents than forests, shrublands or grasslands, providing evidence of the potential of agroforestry to reduce fire risk and protect ecosystems.
Forest wildfires usually occur due to natural processes such as lightning and volcanic eruptions, but at the same time they are also an effect of uncontrolled and illegal anthropogenic activities. Different factors can influence forest wildfires, like the type of vegetation, morphology, climate, and proximity to human activities. A precise evaluation of forest fire issues and of the countermeasures needed to limit their impact could be satisfactory especially when forest fire risk (FFR) mapping is available. Here, we proposed an FFR evaluation methodology based on Geographic Information System (GIS) and the analytic hierarchy process (AHP). The study area is the Campania region (Southern Italy) that, for the last 30 years, has been affected by numerous wildfires. The proposed methodology analyzed 12 factors, and AHP was used for weight assignment, offering a new approach to some parameters. The method divided the study area into five risk classes, from very low to very high. Validation with fire alerts showed a good correlation between observed and predicted fires (0.79 R 2). Analyzing the climate projections, a future FFR for 2040 was also assessed. The proposed methodology represents a reliable screening tool to identify areas under forest fire risk, and can help authorities to direct preventive actions.
During the last decades, climate and land use changes led to an increased prevalence of megafires in Mediterranean-type climate regions (MCRs). Here, we argue that current wildfire management policies in MCRs are destined to fail. Focused on fire suppression, these policies largely ignore ongoing climate warming and landscape-scale buildup of fuels. The result is a “firefighting trap” that contributes to ongoing fuel accumulation precluding suppression under extreme fire weather, and resulting in more severe and larger fires.
We believe that a “business as usual” approach to wildfire in MCRs will not solve the fire problem, and recommend that policy and expenditures be rebalanced between suppression and mitigation of the negative impacts of fire. This requires a paradigm shift: policy effectiveness should not be primarily measured as a function of area burned (as it usually is), but rather as a function of avoided socio-ecological damage and loss.
Forest fires have caused considerable losses to ecologies, societies, and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, we proposed a spatial prediction model for forest fire susceptibility using a CNN. Past forest fire locations in Yunnan Province, China, from 2002 to 2010, and a set of 14 forest fire influencing factors were mapped using a geographic information system. Oversampling was applied to eliminate the class imbalance, and proportional stratified sampling was used to construct the training/validation sample libraries. A CNN architecture that is suitable for the prediction of forest fire susceptibility was designed and hyperparameters were optimized to improve the prediction accuracy. Then, the test dataset was fed into the trained model to construct the spatial prediction map of forest fire susceptibility in Yunnan Province. Finally, the prediction performance of the proposed model was assessed using several statistical measures—Wilcoxon signed-rank test, receiver operating characteristic curve, and area under the curve (AUC). The results confirmed the higher accuracy of the proposed CNN model (AUC 0.86) than those of the random forests, support vector machine, multilayer perceptron neural network, and kernel logistic regression benchmark classifiers. The CNN has stronger fitting and classification abilities and can make full use of neighborhood information, which is a promising alternative for the spatial prediction of forest fire susceptibility. This research extends the application of CNN to the prediction of forest fire susceptibility.
Tropical forests and the biodiversity they contain are declining at an alarming rate throughout the world. Although southern Belize is generally recognized as a highly forested landscape, it is becoming increasingly threatened by unsustainable agricultural practices. Deforestation data allow forest managers to efficiently allocate resources and inform decisions for proper conservation and management. This study utilized satellite imagery to analyze recent forest cover and deforestation in southern Belize to model vulnerability and identify the areas that are the most susceptible to future forest loss. A forest cover change analysis was conducted in Google Earth Engine using a supervised classification of Landsat 8 imagery with ground-truthed land cover points as training data. A multi-layer perceptron neural network model was performed to predict the potential spatial patterns and magnitude of forest loss based on the regional drivers of deforestation. The assessment indicates that the agricultural frontier will continue to expand into recently untouched forests, predicting a decrease from 75.0% mature forest cover in 2016 to 71.9% in 2026. This study represents the most up-to-date assessment of forest cover and the first vulnerability and prediction assessment in southern Belize with immediate applications in conservation planning, monitoring, and management.
Increasing communities’ adaptive capacity is crucial to enhancing the sustainability of livelihoods and landscapes in smallholder systems. This study evaluates the contributions of an asset-based community-driven local development project, which has an objective to enhance farmer livelihoods through context-specific agricultural and agroforestry training, in line with farmers’ identities, interests, and preferences. The project was implemented in two areas of the wider Nyando river basin: the Lower and Middle Nyando sites. The project effects on farmer livelihoods were evaluated by analyzing overall income enhancement through the adoption of climate-smart agricultural practices via the computation of total values of harvest. Socioeconomic data from 183 households, half of which were involved in the project, were considered. The findings showed that locality played an important role in the adoption and success of good agricultural practices. Additional significant positive factors included project participation, size of land operated, horticulture farming, livestock ownership, ownership of a title deed, hours worked, and crop species richness. The number of years farmed had a significant negative correlation with the value of harvest. Considering the stark differences in livelihood effects in both sites, researchers conclude that external support for climate-smart agriculture uptake needs to be considerate of, and respond to, biophysical and socioeconomic context.
Recent studies highlight a growing concern over the limited adoption of climate smart agricultural (CSA) practices despite their potential benefits on adaptation, mitigation and productivity. Literature indicates several factors behind the lack of adoption including socio-demographic and economic conditions, agro-ecological scales and the nature of the practices. This paper examines to what extent and under which conditions such factors influence the adoption of CSA practices at farm, household and community level across three study sites in different continents: Vietnam, Nicaragua and Uganda. While cost benefit analysis (CBA) is employed to assess the farm-level profitability of CSA practices, the aggregate community impact disaggregated by different groups of farmer typologies with specific socioeconomic features is derived from the adoption rate estimated by the relative advantage of practices and the income level of each group. Our main findings show great variation of farm-profitability of CSA practices across scales. Similar practices could generate different profitability depending on crop typologies, input access and prices, household types and local context. Regarding the aggregate profitability of CSA practices at regional scale, we found that under particular conditions, relevant factors of adoption matter to the adoption pattern and thereby affects the ranking. Such conditions include (i) high income inequality, (ii) large profitability gap of prioritized CSA practices, and (iii) large proportion of cost and benefit of the practices in the level of income. This study contributes to enhancing the prioriti-zation process of CSA practices and provides practical guidance for research and policy to tailor the investment to appropriate end-users to assure the greatest impact for the community. PLOS ONE | https://doi.org/10.1371/journal.pone.
Timely and accurate spatial explicit forest fire risk assessment and mapping is essential for forest fire prevention and suppression preparedness, firefighting resources allocation, and efficient multi-level fire management policies. This paper describes the application and validation of an approach for forest fire risk analysis and fire risk zoning over Romania in order to identify areas at national scale where fires are most likely to occur and to threat existing values, resources and assets. A modeling approach based on logistic regression using historical fire observations has been developed based on environmental, socio-economic and demographic data availability at national scale. Ignitions were positively related to south-western slopes and occurred mostly in fuel type of xerophyte oaks as well as in areas of heterogeneous (natural/agricultural) landscape. In addition to the human variables the pattern of ignitions was also significantly related to slope and temperature of the driest quarter. The risk zones produced by the multiple logistic regression model presented satisfactory accuracy when compared with historical fire perimeters extracted from MODIS imagery. The findings of this study could be used by fire managers to implement prevention measures at forest areas with high fire risk. Furthermore, attention should be given to areas with high fire ignition probability, where the vulnerability and potential impact is higher.
Climate change poses a major threat to agricultural production and food security in India, and climate‐smart agriculture (CSA) is crucial in addressing the potential impacts. Using survey data from 1,267 farm households in 25 villages from Bihar and Haryana in the Indo‐Gangetic Plains, this study analyzes the factors that determine the probability and level of adoption of multiple CSA practices, including seeds of stress‐tolerant varieties, minimum tillage, laser land leveling, site‐specific nutrient management and crop diversification. We applied a multivariate probit model for the simultaneous multiple adoption decisions, and ordered probit models for assessing the factors affecting the level of adoption. The adoption of the various CSA practices is interrelated, whereas several factors, including household characteristics, plot characteristics, market access and major climate risks are found to affect the probability and level of CSA adoption. Climate‐smart agriculture (CSA) adoption and its intensity also vary significantly between eastern Bihar, which is relatively poor and densely populated, and north‐western Haryana. Engaging multiple stakeholders such as farmers, agricultural institutions, agricultural service providers and concerned government departments at the local level is crucial for the large‐scale uptake of CSA. The study, therefore, calls for agricultural policy reforms so that most of the issues related to the uptake of CSA can be adequately addressed.
Agriculture in Africa is not only exposed to climate change impacts but is also a source of greenhouse gases (GHGs). While GHG emissions in Africa are relatively minimal in global dimensions, agriculture in the continent constitutes a major source of GHG emissions. In Ghana, agricultural emissions are accelerating, mainly due to ensuing deforestation of which smallholder cocoa farming is largely associated. The sector is also bedevilled by soil degradation, pests, diseases and poor yields coupled with poor agronomic practices. Climate Smart Agriculture (CSA) thus offers a way to reduce the sector’s GHG emissions and to adapt the sector to the adverse impacts of climate change. This study assesses the potential of CSA vis-à-vis conventional cocoa systems to enhance production, mitigate and/or remove GHG emissions and build resilience, in addition to understanding key determinants influencing CSA practices. Using a mixed methods approach, data was collected in Ghana’s Juabeso and Atwima Mponua districts through semi-structured household questionnaires administered to 80 household heads of cocoa farms, two focus group discussions and expert interviews. A farm budget analysis of productivity and economic performance for both scenarios show that CSA practitioners had a 29% higher income per ha compared to the conventional farmers. Estimations using the FAO Ex-Ante Carbon-Balance Tool (EX-ACT) indicate CSA practices preserve forest resources without which the effect on carbon balance as presented by conventional farming would remain a source of GHG emissions. Farm tenure, age of farmers, location of farm, residential status and access to extension services were the main determining factors influencing CSA practices among cocoa farmers. An in-depth understanding of these indicators can help identify ways to strengthen CSA strategies in the cocoa sector and their contributions to climate change mitigation and resilience.
Adoption of appropriate climate smart innovations is the major step towards enhancing food security, building climate resilience capacity as well as reducing or removing GHG emissions from smallholder farms to sustainably maintain agricultural livelihoods. The purpose of this paper is to investigate the rate, intensity, and determinants of multiple adoptions of climate-smart agriculture innovations among smallholder farmers in Ethiopia's Upper Blue Nile Highlands. All Climate Smart Agriculture (CSA) innovations have a synergistic effect on the adoption of other innovations, with the exception of Soil and Water Conservation (SWC). The economic constraint model demonstrated that farm size, number of plots, and access to financial services influence crop residue management, crop rotation, and agroforestry adoption. Hence, a larger number of plots, a larger total farm size, and access to rural savings and credit increase the probability of adoption. The diffusion innovation model, on the other hand, asserted that frequent extension visits, market access, access to information communication, social networks, and strong tenure security have no less of an impact on the adoption of CSA innovations such as improved variety, crop residue management, crop rotation, compost, SWC, and agroforestry. Furthermore, farmers' perceived technology-specific characteristics of CSA innovations increase the likelihood of adoption. Hence, formal education, more awareness about climate change and CSA, and the ability of CSA innovations to reduce the impact of climate change risks such as rising temperatures, increased hailstorms, and increased erratic rainfall have significantly increased the likelihood of adoption. The integrated technology adoption model explains the determinants of adopting multiple CSA innovations simultaneously. Livelihood asset building programs, strong public extension systems via mobile phone, voice messaging, and radio enhance adoption. Policy to identify and scale up a portfolio of farm-level specific CSA innovations is required.
In recent years, the number of wildfires has increased all over the world. Therefore, mapping wildfire susceptibility is crucial for prevention, early detection, and supporting wildfire management decisions. This study aims to generate Machine Learning (ML) based wildfire susceptibility maps for Adana and Mersin provinces, which are located in the Mediterranean Region of Turkey. To generate a wildfire inventory, this study uses active fire pixels derived from MODIS monthly MCD14ML composites. Furthermore, as a sub aim, the performance of seven ML approaches, namely, stand-alone Logistic Regression (LR), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and ensemble algorithms, namely Random Forest (RF), Gradient Boosting (GB), eXtreme Gradient Boosting (XGB), and AdaBoost (AB), was evaluated based on wildfire susceptibility mapping. The capabilities of the corresponding ML methods were assessed using thirteen wildfire conditioning factors, which can be grouped into four main categories: topographical, meteorological, vegetation, and anthropogenic factors. The Information Gain (IG) approach was used to assess their importance scores. A multicollinearity analysis was also performed to assess the relationship between conditioning factors. To compare the predictive performances of ML algorithms, five performance metrics, namely average accuracy, precision, recall, F1 score, and area under the curve, were used. To test the significance of the generated wildfire susceptibility maps and to detect similarities and differences among the output of these ML algorithms, McNemar's test was implemented. In the end, the ML-based models were locally interpreted using the Shapley Additive exPlanations (SHAP) technique. The AUC values of seven methods varied from 0.817 to 0.879, and the accuracy scores ranged between 0.734 and 0.812. The results showed that the RF model provided the best results considering all performance metrics. The accuracy score and AUC values of the RF model were equal to 0.812 and 0.879, respectively. On the other hand, stand-alone algorithms (LDA, SVM, and LR) represented lower performance than tree-based ensemble methods. Both the IG and SHAP analyses showed that elevation, temperature, and slope factors were the most contributing factors. The RF model classifier found that 7.20% of the study area has very high wildfire susceptibility, and the majority of the wildfire samples (68.84%) correspond to the very high susceptible areas in the RF model. The outcomes of this study are likely to provide decision-makers with a better understanding of wildfires in the Eastern Mediterranean Region of Turkey.
Ecology is the basis of existence on this planet therefore it becomes imperative to maintain the ecological balance. Anthropogenic activities, especially in the agriculture sector are a major cause of climate change. Around the world, the largest user of land is a farmer and therefore it becomes decisive to implement practices that are environmentally and ecologically fruitful. In this context, Climate-Smart Practises (CSA) are promoted to accomplish these objectives. However, implementation of these practises precipitates certain socio-economic challenges in the supply and demand which acts as a hurdle to its enactment. This paper examines such barriers faced in the implementation of Climate-Smart Practises in the Agriculture Sector. Eight critical barriers have been identified which are then segregated into cause and effect groups by using DEMATEL (Decision-Making Trial and Evaluation Laboratory) method. The outcome of this study will help policymakers and proponents of Climate-Smart Agriculture interventions. By working to ameliorate the effects of barriers, efficient implementation CSA practises can be ensured.
Climate variability in the recent decades has intensified in the SSA region, which makes it imperative to explore adequate adaptation and mitigation strategies to offset its current and future adverse impacts. Farmers' perception of climate variability can significantly influence their coping, mitigation, and adaptation potential. This study assessed farmers' perceptions of indicators and consequences of climate variability and explored factors influencing their perception of climate variability and adoption of climate coping strategies. A cross-sectional survey design was used to sample 300 farmers in the Central Highlands of Kenya. Binary logistic regression models were used to determine factors that influenced the perception of climate variability, adaptation, and mitigation strategies based on three predictor sets, including socioeconomic, institutional, and environmental dimensions. Three climate adaptation and mitigation strategy groups adopted by farmers, including crop adjustment, nutrient management, and soil and water management practices, were subjected to binary logistic regression. The core determinants of farmers' perception of climate variability included (tropical livestock unit) TLU (p = 0.008), access to agricultural training (p = 0.022), change in agricultural production (p = 0.005), change in forest cover (p = 0.014), soil fertility status (p = 0.039), and perceptions of soil erosion (p = 0.001). Most farmers reported changes in all climatic indicators during the decade preceding the survey, including increasing temperature (80%), reduced precipitation (78%), and declining season lengths (76%). There were significant relationships between climate variability perceptions and coping strategies, with the soil and water management set showing stronger links with climate perceptions compared to crop adjustment and nutrient management strategies. Critical mitigation and adaptation strategies to cope with climate variability implemented by farmers included the use of fertilizer and manure in combination (71%), terracing (66%), and crop rotation (60%). Farmers' perceptions significantly determined the adoption of climate-smart agriculture technologies, and environmental determinants strongly influenced climate variability coping strategies. Therefore, while formulating climate sustainability-related policies, farmers' perceptions should be considered.
Fire is among the most dangerous and devastating natural hazards in forest ecosystems around the world. The development of computational ensemble models for improving the predictive accuracy of forest fire susceptibilities could save time and cost in firefighting efforts. Here, we combined a locally weighted learning (LWL) algorithm with the Cascade Generalization (CG), Bagging, Decorate, and Dagging ensemble learning techniques for the prediction of forest fire susceptibility in the Pu Mat NationalPark, Nghe An Province, Vietnam. A geospatial database that contained records from56 historical fires and nine explanatory variables was employed to train the standaloneLWL model and its derived ensemble models. The models were validated for their goodness-of-fit and predictive capability using the area under the receiver operating characteristic curve (AUC) and several other statistical performance criteria. The CG-LWL and Bagging-LWL models with AUC = 0.993 showed the highest training performance, whereas the Dagging-LWL ensemble model with AUC = 0.983 performed better than Decorate-LWL (AUC = 0.976), CG-LWL and Bagging-LWL (AUC = 0.972),and LWL (AUC = 0.965) for predicting the spatial pattern of fire susceptibilities across the study area. Our study promotes the application of ensemble models in forest fire prediction and enhances the researchers' understanding of the processes of model building. Although these four ensemble models were originally developed for the estimation of forest fire susceptibility, the models are sufficiently general to be used for predicting other types of natural hazards, such as landslides, floods, and dust storms, by considering local geo-environmental factor.
Turkey has a high forest fire potential along the Aegean and Mediterranean coasts, related to climate and extremely sensitive forests. In Turkey over 10,000-ha forest area has been destroyed every year and inevitable damage has been revealed. Forest fires not only destroy forest areas, but also cause damage to ecosystems, habitats and especially human lives. Because Muğla province has 90% of total pine honey production in the world and a high potential forest fire occurrence rate, sustainability of ecosystems, productivity and economic income require determining forest fire susceptibility zones. Generating forest fire susceptibility zones is a complex study which requires considering environmental, forestry, topographic, economic, and meteorological parameters within a decision support platform. At this point, Geographical Information System (GIS) aided Multi-Criteria Decision Analysis (MCDA) techniques can provide sufficient and effective solutions for fire susceptibility mapping due to the comparable and scalable structure of the criteria that are used to determine the susceptibility map when deciding. In this study, the weight of each criterion is calculated via the Analytical Hierarchy Process (AHP). Then, TOPSIS and VIKOR methods were used to generate forest fire susceptibility maps in Muğla province. The results indicated that 1659.44 ha (13%) and 3952.14 ha (31%) of the study area were assigned as highly prone to forest fire according to the TOPSIS and VIKOR calculations respectively, and an 81% correlation coefficient was calculated between methods. The reliabilities of the maps were verified with 1454 forest fire locations. Considering the respective 89.54%, 86.94% and 88.99% accuracy rate of VIKOR, TOPSIS and AHP susceptibility maps, all the methods could be used in forest fire susceptibility map generation which has comprehensive decision making process.
Purpose
This paper aims to examine impediments to the adoption of sustainable water-efficient technological innovation in agriculture. Farming is the largest water consumer and food production expansion in response to global population growth, combined with increasing droughts from climate change, threatens water and food insecurity for many countries. Yet, climate smart agriculture (CSA) innovation adoption has been slow, and in this regard, governments and the agricultural sector are not fulfilling their social responsibility and sustainability obligations.
Design/methodology/approach
Barriers to water-efficient drip irrigation (DI) adoption in Australia were investigated via 46 depth interviews with agricultural stakeholders and a survey of 148 farmers.
Findings
While DI water efficiency is recognised, this is not the key determinant of farmers’ irrigation method selection. Complex interrelationships between internal and external barriers impede DI adoption are identified. These include costs, satisfaction with alternative irrigation methods, farmer characteristics that determine the suitability of the innovation and the extent it is incremental or radical, plus various multidimensional risks. Government support of alternative, less water-efficient irrigation methods is also a critical barrier.
Originality/value
A conceptual framework for understanding barriers to sustainability oriented innovation adoption is presented. Its insights should be applicable to researchers and practitioners concerned with understanding and improving the adoption of socially responsible and sustainable innovation in a wide range of contexts. Recommendations for overcoming such adoption barriers are discussed in relation to the research focus of water-efficient agriculture and encouraging uptake of DI.
Recent fires in Iran’s Zagros forests have inflicted heavy, extensive losses to the environment, forests, villages, and forest inhabitants, resulting in a huge financial loss to the country. With the increasing risk of fire and the resulting losses, it has become ever more necessary to design and develop efficient fire control and prediction procedures. The present study utilizes the Dong model to develop a map of areas vulnerable to fire in the Zarivar lake forests as a representative sample of Zagros forests. The model uses as its inputs some of the most significant factors (such as vegetation, physiographic features, and the human component) that affect the fire occurrence and spread. Having assigned weights to each factor based on the model, all maps were overlapped in the ArcMap and then the region was divided into five zones. The results showed that 74% of the region was located in three classes: highly vulnerable, vulnerable, and medially vulnerable. To validate the proposed zoning map it was compared with a map based on real data obtained from previous fires. The results showed that 81% of fire incidents were located in highly vulnerable, vulnerable and medially vulnerable zones. Furthermore, the findings indicated a medium to a high degree of fire vulnerability in Zarivar Lake forests.
Climate-smart agriculture (CSA) is a suggested pathway to the improvement of food security in a changing climate. The Department of Agricultural Extension under the Bangladesh Ministry of Agriculture has been promoting CSA with farmers through climate field schools since 2010. This study investigated the impact of adoption of CSA practices on the household food security of coastal farmers in southern Bangladesh. Factors determining household food security were also explored. Data were collected from 118 randomly selected farmers of Kalapara sub-district in Patuakhali, Bangladesh. We identified 17 CSA practices that were adopted by the farmers in the study area. Those practices were saline-tolerant crop varieties, flood-tolerant crop varieties, drought-resistant crop varieties, early maturing rice, vegetables in a floating bed, ‘sorjan’ method of farming, pond-side vegetable cultivation, the cultivation of watermelon, sunflower or plum, relay cropping, urea deep placement, organic fertilizer, mulching, use of pheromone trap, rain water harvesting and seed storage in plastic bags or glass bottles. The farmers adopted on average seven out of these CSA practices. Among the sampled households, 32% were assessed as food secure, 51% were mildly to moderately food insecure and 17% were severely food insecure. Adoption of CSA practices was positively associated with household food security in terms of per capita annual food expenditure (β = 1.48 Euro, p = 0.015). Households with a better educational level, farming as a major occupation, a larger pond size, greater number of cattle, higher household income, smaller family size and less difficulty with access to markets were likely to be more food secure. Increasing the adoption of CSA was important to enhance food security but not a sufficient condition since other characteristics of the farmers (personal education, pond size, cattle ownership and market difficulty) had large effects on food security. Nevertheless, increased adoption of saline-tolerant and flood-tolerant crop varieties, pond-side vegetable cultivation and rainwater harvesting for irrigation could further improve the food security of coastal farmers in southern Bangladesh.
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