Correction: What evidence exists on how biodiversity is affected by the adoption of carbon footprint-reducing agricultural practices? A systematic map

BackgroundThe global agriculture sector is expected to contribute towards carbon net zero by adopting interventions to reduce/offset greenhouse gas emissions and increase carbon sequestration/removal. Many of these interventions require change to land management and agriculturally associated habitats, subsequently impacting biodiversity. This relationship is important as the Convention on Biological Diversity has also pledged to reverse nature decline. To understand this relationship, a systematic map was developed to collate evidence relating to the impacts of carbon footprint reducing interventions on agriculturally associated biodiversity. This systematic map collated studies from temperate farming systems including northern Europe, North America and New Zealand.MethodsA protocol was published to define the methodology. Potentially relevant articles were identified by searching three academic databases using a predefined search string. Also, nine organisational websites were searched using key words. All potentially relevant articles were exported into EPPI-Reviewer-Web. Following deduplication, the remaining articles were screened at title and abstract level, partially with the aide of machine learning, before full text screening and extraction of metadata.Review findingsScreening began with 67,617 articles that ended with an evidence base of 820 primary research studies and 82 reviews. The evidence base includes studies from 1978 to April 2024, of which 81% were studies that lasted less than 5 years. Whilst microorganisms (n = 328), arthropods (n = 190), worms (n = 121) and plants (n = 118) were well represented in the evidence base, other groups such as birds (n = 32), gastropods (n = 16), mammals (n = 13), amphibians (n = 1) and reptiles (n = 1) were represented less well. The most studied interventions were to increase soil organic carbon through reduced tillage (n = 227) and cover cropping (n = 136). However, there were less than five studies in total for the following land management objectives: avoiding soil compaction (n = 2), precision farming (n = 2) and renewable energy production. Study authors reported carbon footprint-reducing practices to positively impact biodiversity in 65% of studies, to have mixed effects in 11%, negative in 8% and no effect in 16% of studies. As no critical appraisal was carried out on the included studies, we recommend further study validation and synthesis in order to support these findings.ConclusionsThe evidence base has highlighted evidence clusters and gaps on how farming practices that can reduce the carbon footprint of a farm impacts agriculturally associated biodiversity. There are many areas for further research including studies investigating the long-term relationship of interventions that alter habitats over a long period such as rewetting peat soils and increasing tree cover. Future research should observe abundance and diversity of multiple species to generate a better understanding of an intervention’s impact. The review evidence base largely matched the primary evidence base, however none were conducted with systematic methodologies. This systematic map is intended to direct further primary and secondary research to improve the understanding of how carbon footprint reducing practices impact biodiversity, thus contributing towards meeting the legally binding global environmental targets in concert.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13750-025-00372-7.

The economy of Bangladesh largely depends on agriculture. Agroforestry is one of the modern agricultural practices which ensure maximum utilization of resources and offer sustainable environmental and economic development. The present study was carried out to find out farmers’ perceptions and problems towards the adoption of agroforestry practices in the coastal area of Bangladesh. In total, 400 farmers were selected from five upazila of the Noakhali district following a proportionate random sampling technique. Face-to-face interviews, case studies, and focus group discussions (FGD) were used to obtain the data. Statics such as range, mean, standard deviation, Chi-square, and correlation analysis were applied. Most of the respondents were middle-aged (45%) with a low literacy rate and large family size (55%). The majority of them (50%) had small-sized land, and they had limited access to agricultural training. A greater part of the respondents (61%) had a very low level of knowledge of agroforestry practice. Silvopasture and homestead agroforestry system was practiced by most of the farmers (77%) without proper concept. Only 11% of the respondents know and practice multistrata agroforestry, which is one of the best agroforestry practice. The perception of the respondents towards the adoption of agroforestry was found low in most of the cases. They expressed a low level of acuity towards agroforestry, and they were not so interested towards the adoption of these practices. “Agroforestry adoption may be hampered by a lack of institutions and policies to support it” and “Lack of knowledge and awareness on agroforestry practice” was identified most significant problems with a chi-square value of 41.6 and 39.2 at 5% level of significance. Respondents’ knowledge of agroforestry, exposure to training, and farm size were positively and significantly correlated with their adoption of the practice (r = 0.437∗∗, 0.572∗∗, and 0.64∗∗, respectively). Demonstrations, awareness campaigns, financial incentives, and collaborations between government organizations and NGOs may increase the adoption of agroforestry practices in the coastal region of Bangladesh.

There is a growing interest in the use of pyrolysis plants for the conversion of solid waste into useful products (e.g., oil, gas, and char) and the analysis of air-polluting emissions associated with such a process is an emerging research field. This study applied a systematic mapping approach to collating, describing, and cataloging available evidence related to the type and level of air pollutants emitted from pyrolysis plants, the factors affecting emissions, and available mitigation strategies that can be adopted to reduce air pollution. The scientific literature indexed in Scopus and Google Scholar, as well as available industry reports, was interrogated to document the evidence. A database comprising 63 studies was synthesized and cataloged from which 25 air pollutants from pyrolysis plants were considered, including volatile organic compounds and persistent organic pollutants. Air pollutant levels varied depending on the scale of the pyrolysis plants, their operating conditions, and the feedstock used. Various technologies, such as wet scrubbers, electrostatic precipitators, and baghouse filters, are available and have been utilized to reduce emissions and comply with the existing EU regulations for waste incineration (2010/75/EU). The systematic mapping identified several knowledge gaps that need to be addressed to inform relevant environmental policymaking, technology development, and the adoption of best practices for the mitigation of emissions from pyrolysis plants.

Understanding the factors associated with adoption of ecological farming practices is a well‐established topic of interest to agricultural economists. As the transition to more sustainable agriculture has become a policy priority for the European Union, broad and balanced reviews of this literature are important. We develop a systematic map of quantitative observational studies which describes the ecological practice(s) adopted, the frequency of inclusion and significance of a range of independent variables, and how the dependent variable is measured. We also conduct a quality assessment. We find that while socio‐demographic variables and farm structural variables are frequently included, they were insignificant more often than they were significant. For behavioural factors we find stronger evidence for the importance of cognitive or attitudinal variables compared to dispositional attitudinal variables. We also find a growing interest in social factors which will be valuable for researchers to explore further and reflect on the policy implications of our findings.

Aims: The adoption of recommended coffee agricultural practices is critical for enhancing the productivity and sustainability of smallholder coffee farmers. This study explores the influences of independent variables on the adoption of these practices. Study Design: A quantitative method was employed, utilizing structured questionnaires administered to the sample size of 198 smallholder farmers. Methodology: Data were analysed using SPSS version 20 with descriptive statistics (frequencies and percentages) to assess adoption levels. The Probit regression model was applied to evaluate the likelihood that a farmer adopts a given practice based on a set of explanatory variables. The regression examined the association between adoption decision and independent variables, including access to loan, annual income, gender, access to training, number of coffee plants, farm size and educational level. Results: The findings revealed that adoption rate varied across different coffee practices, while some practices like shade adoption and weeding are widely adopted, others like Integrated pest management (IPM) and pesticides use need more support to be widely implemented. Income, land size and access to training were found to significantly affect the adoption of many practices. Income and access to training were associated with the adoption of more advanced practices suggesting farmers with greater financial resources and knowledge are more likely to implement these practices. On the other hand, marital status and gender has no significant influence on adoption of most practices. Conclusion: The study concludes that addressing access to credits and education challenges among smallholder farmers can significantly improve the adoption of these practices, thereby increasing productivity and farmers income. Based on these finding practical training program to improve farmers knowledge, financial support to farmers and targeted interventions for younger farmers should be emphasized.

ABSTRACTClimate‐smart agriculture (CSA) is a set of economically and environmentally friendly practices that can address the issues of food security under the new realities of climate change. However, the adoption of these practices among smallholder farmers is still low, especially in developing countries like Ethiopia. Thus, the main objective of this study was to assess the factors determining rural farm households' adoption of CSA practices and their impact on food security in the Sidama region, Ethiopia. Primary data for this study were obtained from a randomly selected sample of 523 households using a pre‐tested, structured questionnaire. The collected data were analyzed using descriptive statistics, multivariate probit, and an endogenous switching regression model. The study found that, in the study area, conservation of agriculture is the most popular CSA practice, which is adopted by 96.2% of the adopters, followed by soil fertility management (82.2%), crop diversification (62.3%), small‐scale irrigation (47.9%), and crop–livestock integration (40.7%). The multivariate probit model revealed that the sex of the household head, age of the household head, educational level of the household head, extension contact, information on climate change, and distance to the market were identified as determinants for the adoption of CSA practices. Furthermore, the endogenous switching regression model analysis revealed that adoption of CSA practices increased rural farm household food security status. Therefore, this paper suggests that smallholder farmers should incorporate CSA practices to improve their food security.

Canada's agricultural business risk management (BRM) programs require significant public expenditure, with unclear consequences related to climate change adaptation and mitigation through the adoption of beneficial management practices (BMPs). This study examines the relationship between Canada's current suite of BRM programs and the adoption of practices that mitigate GHG emissions in crop agriculture. We review the impacts of agricultural insurance on climate adaptation and mitigation, identifying impacts on both the intensive and extensive margins of production. We consider five potential program modifications, including: (1) changes in producer insurance premiums in AgriInsurance for the adoption of practices that would decrease the actuarially fair insurance rate if they were properly incorporated in the calculation; (2) dedicated insurance products related to trials of specific BMPs; (3) adjustments to current programs to allow more whole‐farm considerations and intercropping; (4) cross‐compliance measures on AgriInvest tied to environmental education; and (5) reduced insurance coverage for unfavorable environmental practices. While the effects of these potential modifications remain uncertain, they will drive the data collection process necessary to ensure that Canada's BRM programs play an appropriate role in greenhouse‐gas reducing BMP adoption and climate change adaptation and mitigation.

Water is a resource that cannot be replaced and can only be renewed if it is well managed. It is basic for all forms of life, for every aspects of socio-economic development, and for the maintenance of a healthy ecosystem. Agriculture is the biggest water user globally, accounting for 70 percent of total water withdrawals on average. There are different practices that can aid in managing water used for agriculture. Some of these agricultural water management practices are; rainwater harvesting, irrigation, organic farming, and use of drought resistant crops. Farmer’s decision to either adopt or reject these practices can be affected by several factors, some of which are socio-economic. Some of the socio-economic factors that may influence adoption of these practices include farmer’s educational level, income level and farm size. This study examined the influence of farmer’s income level on adoption of agricultural water management practices (Rainwater harvesting, Irrigation Drought resistant crops). Cross-sectional survey design was adopted, while proportionate and simple random sampling technique was used to obtain the respondents. The accessible population was 6,230 smallholder farmers from the target population of 26,804 smallholder farmers in Rongai sub county Kenya. The study was done in August 2023 to November 2023.The study included 120 smallholder farmers in Rongai Sub County. The study used questionnaire to collect data while binary logistic regression was used to analyze the data. The results indicated that adoption of agricultural water management practices is low, only 36% of smallholder farmers had adopted agricultural water management practices. The P value calculated for the 120 smallholder farmers was P=.033 which is < than .05 and therefore the null hypothesis was rejected concluding that in this study income level had statistically significant influence on adoption of agricultural water management practices among smallholder farmers in Rongai sub-county in Nakuru ,Kenya. The findings may help in emphasizing the necessity of assisting farmers in removing financial obstacles that may hinder adoption of agricultural water management practices.

This article discusses the potential of BNs to complement the analytical toolkit of agricultural extension. Statistical modelling of the adoption of agricultural practices has tended to use categorical (logit/probit) regression models focusing on a single technology or practice, explained by a number of household and farm characteristics. Here, a Bayesian network (BN) is used to model household-level data on adoption of agrosilvopastoral practices in Tiby, Mali. We discuss the advantages of BNs in modelling more complex data structures, including (i) multiple practices implemented jointly on farms, (ii) correlation between probabilities of implementation of those practices and (iii) correlation between household and farm characteristics. This paper demonstrates the use of BNs for ‘deductive’ reasoning regarding adoption of practices, answering questions regarding the probability of implementation of combinations of practices, conditional on household characteristics. As such, BNs is a complementary modelling approach to logistic regression analysis, which facilitates exploring causal structures in the data before deciding on a reduced form regression model. More uniquely, BNs can be used ‘inductively’ to answer questions regarding the likelihood of certain household characteristics conditional on certain practices being adopted.

Rice cultivation plays a vital role in the Southeast Asian (SEA) economy, but it poses environmental challenges and contributes a significant amount of greenhouse gas emissions. To address these concerns, sustainable agricultural practices (SAPs) for rice production have been introduced to mitigate the environmental impact of rice production while fostering economic and social sustainability. However, the adoption of these practices remains limited, highlighting the need for a critical review of existing literature to gain deeper insights into the factors influencing farmers’ adoption of these practices in SEA countries. This review analyzed 39 manuscripts to assess the current state of SAPs for rice cultivation in SEA. We found that socio-demographic variables and farm management variables were frequently examined in these studies, with varying levels of significance. Economic and institutional variables were moderately studied and tended to have more significant findings. There is a noticeable research gap regarding behavioral factors, emphasizing the need for further investigation in SEA. Furthermore, the findings underscore the importance of conducting additional research to develop effective monetary and non-monetary incentives and explore methodologies to address the gaps in understanding farmers’ trade-offs and preferences among different SAPs. These efforts are crucial for promoting the widespread adoption of SAPs in rice cultivation.

PurposeThe adoption of climate-smart agricultural practices (CSAPs) is important for sustaining Indian agriculture in the face of climate change. Despite considerable effort by both national and international agricultural organizations to promote CSAPs in India, adoption of these practices is low. This study aims to examine the elements that affect the likelihood and intensity of adoption of multiple CSAPs in Bihar, India.Design/methodology/approachThe probability and intensity of adoption of CSAPs are analyzed using multivariate and ordered probit models, respectively.FindingsThe results show significant correlations between multiple CSAPs, indicating that their adoptions are interrelated, providing opportunities to exploit the complementarities. The results confirm that both the probability and intensity of adoption of CSAPs are affected by numerous factors, such as demographic characteristics, farm plot features, access to market, socio-economics, climate risks, access to extension services and training. Farmers who perceive high temperature as the major climate risk factor are more likely to adopt crop diversification and minimum tillage. Farmers are less likely to adopt site-specific nutrient management if faced with short winters; however, they are more likely to adopt minimum tillage in this case. Training on agricultural issues is found to have a positive impact on the likelihood and the intensity of CSAPs adoption.Practical implicationsThe major policy recommendations coming from of our results are to strengthen local institutions (public extension services, etc.) and to provide more training on CSAPs.Originality/valueBy applying multivariate and ordered probit models, this paper provides some insights on the long-standing discussions on whether farmers adopt CSAPs in a piecemeal or in a composite way.

One of the most important steps in the process of conducting a systematic review or map is data extraction and the production of a database of coding, metadata and study data. There are many ways to structure these data, but to date, no guidelines or standards have been produced for the evidence synthesis community to support their production. Furthermore, there is little adoption of easily machine-readable, readily reusable and adaptable databases: these databases would be easier to translate into different formats by review authors, for example for tabulation, visualisation and analysis, and also by readers of the review/map. As a result, it is common for systematic review and map authors to produce bespoke, complex data structures that, although typically provided digitally, require considerable efforts to understand, verify and reuse. Here, we report on an analysis of systematic reviews and maps published by the Collaboration for Environmental Evidence, and discuss major issues that hamper machine readability and data reuse or verification. We highlight different justifications for the alternative data formats found: condensed databases; long databases; and wide databases. We describe these challenges in the context of data science principles that can support curation and publication of machine-readable, Open Data. We then go on to make recommendations to review and map authors on how to plan and structure their data, and we provide a suite of novel R-based functions to support efficient and reliable translation of databases between formats that are useful for presentation (condensed, human readable tables), filtering and visualisation (wide databases), and analysis (long databases). We hope that our recommendations for adoption of standard practices in database formatting, and the tools necessary to rapidly move between formats will provide a step-change in transparency and replicability of Open Data in evidence synthesis.

Teff farming in Ethiopia is commonly seen as being dominated by men, with women playing supporting roles on some aspects of the growing process. This study is rooted in existing literature on drivers of Best Practices (BP) adoption and decision-making theory and is unique in that it focuses primarily on understanding how gender-specific factors influence decision-making on the adoption of BPs. To this end, the study assessed the intra-household gender dynamics at play within farming households in Amhara, Ethiopia, and their influence on deciding whether or not to adopt agricultural best practices for teff farming. These gender dynamics include the division of labor between women and men, intra-household decision-making processes, social and cultural norms and access factors (such as access to information, training, credit and control over income). Using data from a three-round quantitative survey with one woman and one man in 555 households, as well as focus group discussions and in-depth interviews, this study is uniquely placed to assess the impact of these gender-specific and intra-household factors on the adoption of best practices. The findings show that households where women are more involved in teff farming, have less input into decision-making, less control over income, and more access to information and adopt on average more best practices. However, there is significant heterogeneity when looking at individual best practices, with women’s decision-making power or access to resources particularly important for specific practices such as sowing in rows. This study implies that designing more gender-sensitive agricultural programs and extension services in Ethiopia – specifically on practices relevant to women and men – can increase best practice adoption, with the ultimate aim of increasing productivity and income for teff farming households, and empowering women. Since male and female farmers are involved in different practices, access to resources and decision-making power have different impacts depending on the gender of the respondent and the practice analyzed, and there is no “one size fits all” solution to improve teff farming productivity. Key words: intra-household dynamics, decision-making, gender roles, best practice adoption, teff

Background and Context Teaching Software Engineering (SE) involves helping students understand key concepts and problems while designing software. Traditional methods often render students passive, reducing engagement and learning. Active learning strategies, such as Example-Based Learning, have shown promise in improving Software Engineering Education. Objective This study aims to investigate and classify the existing empirical evidence about using Example-Based Learning in Software Engineering Education. Method We carried out a systematic mapping to collect existing studies and evidence that describe how instructors have been employing Example-Based Learning to teach SE concepts. Findings From 30 studies, we mapped benefits, challenges, SE contents, example types, and artifacts related to Example-Based Learning . We also proposed guidelines to support its adoption in educational practice. Implications Example-Based Learning supports learning by fostering interaction, concept interpretation and application, motivation, and confidence. However, its adoption poses challenges, including increased instructor effort, limited support, and time-consuming diagram creation

In the wake of the resource constraints for external farm inputs faced by farmers in developing countries, sustainable agriculture practices that rely on renewable local or farm resources present desirable options for enhancing agriculture productivity. In this study, plot‐level data from the semi‐arid region of Ethiopia, Tigray are used to investigate the factors influencing farmers' decisions to adopt agriculture practices, with a particular focus on conservation tillage, compost and chemical fertilizer. A trivariate probit model is used to analyze the determinants of adoption of these practices. In addition, stochastic dominance analysis is used to compare the productivity impacts of compost with that of chemical fertilizer based on a six‐year cross‐sectional farm‐level dataset. Our results indicate heterogeneity with regard to the factors that influence adoption decisions of the three practices and the importance of both plot and household characteristics on influencing adoption decisions. In particular, we found that household endowments and access to information, among other factors, impact the choice of sustainable farming practices significantly. Furthermore, the use of stochastic dominance analysis supported the contention that sustainable farming practices enhance productivity. They even proved to be superior to the use of chemical fertilizers — justifying the need to investigate factors that influence adoption of these practices and to use this knowledge to formulate policies that encourage adoption.