Climate Smart Agriculture最新文献

{"title":"Leveraging crop yield forecasts using satellite information for early warning in Senegal","authors":"Shweta Panjwani ,&nbsp;Mahesh Jampani ,&nbsp;Mame H.A. Sambou ,&nbsp;Giriraj Amarnath","doi":"10.1016/j.csag.2024.100024","DOIUrl":"10.1016/j.csag.2024.100024","url":null,"abstract":"<div><div>Agricultural losses driven by climate variability and anthropogenic pressures have severely impacted food security in Senegal. There is a crucial need to generate early warning signals for the upcoming season to enhance food security in response to the sudden climate shocks like drought. In this study, we investigated the spatial distribution of maize and groundnut using factor analysis with a principal component approach. We aimed to identify suitable predictors of crop yields for the development of a seasonal yield prediction model. Subsequently, multi-regression analysis was performed to predict crop yield based on various combinations of satellite-derived vegetation and climate (rainfall) datasets as well as agronomic data from Senegal's 40 districts between 2010 and 2021. Studies revealed a strong correlation between seasonal rainfall (May to September) and crop yield: a 10–20 ​% decline in rainfall can lead to crop losses. The accuracy of the yield prediction model, built on the best performing scenarios for each district based on monsoon onset, duration, and planting time, exceeded 0.5 (R-squared) for all districts when combining rainfall and normalized difference vegetation index (NDVI) data. The model prediction accuracy varied between 0.6 and 0.8 for major crop growing areas. The study emphasizes that refining the yield prediction model using machine learning techniques can improve its accuracy and enable its implementation in early warning systems. This enhanced capability could bolster Senegal's resilience to climate change by aiding decision-makers and planners in developing more effective strategies to ensure food security.</div></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"1 2","pages":"Article 100024"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing spatiotemporal variations of soil organic carbon and its vulnerability to climate change: A bottom-up machine learning approach","authors":"Qichen Wang ,&nbsp;Yinuo Shan ,&nbsp;Wenbo Shi ,&nbsp;Fubo Zhao ,&nbsp;Qiang Li ,&nbsp;Pengcheng Sun ,&nbsp;Yiping Wu","doi":"10.1016/j.csag.2024.100025","DOIUrl":"10.1016/j.csag.2024.100025","url":null,"abstract":"<div><div>Soil organic carbon (SOC) is a crucial component of the terrestrial carbon cycle and essential for agricultural productivity. Quantifying its sensitivity to future climate change is vital for sustaining agricultural practices and mitigating greenhouse gas emissions. However, this remains a challenge as long-term SOC data are scarce and substantial uncertainties regarding future climate scenarios. This study presents a bottom-up machine learning framework to assess the spatiotemporal variations of SOC and its vulnerability to climate change in the Jinghe River Basin, a typical loess hilly and gully watershed. Firstly, the long-term (2000–2023) dynamics of SOC was estimated by integrating in-situ measurements with machine learning techniques. Results show that the high SOC values are primarily distributed in the farmland of the mountain-loess transition zone, while the low-value areas are mainly found in the loess region. During the study period, the SOC content exhibited a slight increasing trend with a rate of 0.02 ​g ​kg⁻¹ ​yr⁻¹ (<em>p</em> ​= ​0.449). The vulnerability of farmland surface SOC to future climate change was then evaluated by combining a robust machine learning model with the bottom-up framework. To this end, the study explored a wide range of possible future climates to identify critical climate thresholds and their spatial variation across the basin’s farmlands. Based on this analysis, this research found that the farmland in the northern basin is generally more susceptible to changing climate with even marginal rises in temperature could lead to severe loss in SOC. These results highlight the need for proactive climate adaptation strategies to safeguard SOC in vulnerable agricultural landscapes, ensuring soil health and resilience in the face of climate change.</div></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"1 2","pages":"Article 100025"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Maize–peanut intercropping and N fertilization changed the potential nitrification rate by regulating the ratio of AOB to AOA in soils","authors":"Yongyong Zhang ,&nbsp;Fengyan Zhao ,&nbsp;Zhanxiang Sun ,&nbsp;Wei Bai ,&nbsp;Chen Feng ,&nbsp;Anita C. Risch ,&nbsp;Liangshan Feng ,&nbsp;Beat Frey","doi":"10.1016/j.csag.2024.100023","DOIUrl":"10.1016/j.csag.2024.100023","url":null,"abstract":"<div><div>Maize–peanut intercropping could potentially mitigate nitrogen (N) loss from the soil, a process primarily governed by the net nitrification rate. However, the impact of maize–peanut intercropping on the potential nitrification rate (PNR) and its relationships with key players, such as ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB), are not well understood. Herein, we conducted a field experiment involving two management systems and two crops, namely, maize (MP_m) and peanut (MP_p) intercropping, maize monoculture (MM), and peanut monoculture (PM), under three N fertilization rates (no N fertilization, 150 ​kg ​N ​ha⁻¹, and 300 ​kg ​N ​ha⁻¹). Under intercropping (MP_m and MP_p), the abundance of AOA <em>amoA</em> gene increased by 64.8 ​% and 60.3 ​% and the abundance of AOB <em>amoA</em> gene increased by 63.2 ​% and 68.2 ​% compared to the MM and PM monoculture systems, respectively. Furthermore, the abundances of AOA and AOB decreased in MP_p and MM, while AOB increased in MP_m and PM across the N fertilization gradient. The PNR increased corresponding to the N fertilization rates, with intercropping enhancing the PNR in peanut-planted soil but reducing the PNR in maize-planted soil compared to monocropping. Notably, no significant positive relationship between the abundances of AOA or AOB and the PNR. Random forest analysis indicated that the AOB/AOA ratio was an important predictor of the PNR. N fertilization and intercropping regulated the AOB/AOA ratio mainly through controlling the ammonia content and the soil C/N, respectively. These findings highlight the substantial impacts of N fertilization and intercropping on PNR, with the AOB/AOA ratio emerging as a valuable predictive indicator for the PNR.</div></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"1 2","pages":"Article 100023"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling the top-down control of soil viruses over microbial communities and soil organic carbon cycling: A review","authors":"Shuo Wang ,&nbsp;Dong Zhu ,&nbsp;Tida Ge ,&nbsp;Yongfeng Wang ,&nbsp;Ying Zhang ,&nbsp;Chao Liang ,&nbsp;Hanpeng Liao ,&nbsp;Xiaolong Liang","doi":"10.1016/j.csag.2024.100022","DOIUrl":"10.1016/j.csag.2024.100022","url":null,"abstract":"<div><div>Soil viruses play a pivotal yet underexplored role in microbial community succession and soil organic matter (SOM) turnover. This review synthesizes current knowledge on the mechanisms by which soil viruses influence SOM dynamics. It highlights how viral lysis accelerates microbial turnover and restructures microbial communities and how these processes rewire nutrient cycling and substantially fuel microbial metabolism. Furthermore, we also discussed the critical roles of virus-carried auxiliary metabolic genes (AMGs) in microbial processes, the degradation of complex organic materials and nutrient cycling. In together, this review emphasizes the significance of virus-microbe interactions in regulating SOM formation, transformation, and stabilization, and underscores the need and urgency for further research to achieve a comprehensive understanding of how soil viruses contribute to carbon cycling and ecosystem sustainability. Understanding virus-microbe-environment interactions is crucial for developing strategies to enhance soil carbon storage, mitigate climate change, and promote sustainable soil management practices.</div></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"1 2","pages":"Article 100022"},"PeriodicalIF":0.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An exploration of the latest developments, obstacles, and potential future pathways for climate-smart agriculture","authors":"Asif Raihan ,&nbsp;Mohammad Ridwan ,&nbsp;Md Shoaibur Rahman","doi":"10.1016/j.csag.2024.100020","DOIUrl":"10.1016/j.csag.2024.100020","url":null,"abstract":"<div><div>Global climate change presents major challenges to agricultural production, the most significant this century being mitigating greenhouse gas (GHG) emissions and achieving food security. Climate-smart agriculture (CSA) is a conceptual framework that offers potential solutions to these multifaceted problems. Sustainable agriculture can be achieved by implementing strategies aimed at enhancing adaptation, reducing GHG emissions, and safeguarding national food security. However, there has been limited critical examination of the advances made in CSA within emerging and developed nations. This study provides a timely, informative, and comprehensive review of the academic literature, collating recent advances, challenges, and potential future directions of CSA. It identifies and analyzes a range of pertinent issues and obstacles, and offers policy recommendations to foster cooperation and drive forward the objectives of CSA. Future development of CSA is expected to focus on leveraging advanced internet technologies to enhance agricultural data security, optimize cropping patterns, and improve management techniques. This will encompass the integration of precision farming and genetic enhancement technologies to boost crop yields in the face of changing climatic conditions.</div></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"1 2","pages":"Article 100020"},"PeriodicalIF":0.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Site-based climate-smart tree species selection for forestation under climate change","authors":"Wenhuan Xu ,&nbsp;Anil Shrestha ,&nbsp;Guangyu Wang ,&nbsp;Tongli Wang","doi":"10.1016/j.csag.2024.100019","DOIUrl":"10.1016/j.csag.2024.100019","url":null,"abstract":"<div><div>Global climate change threatens ecosystem functions and resilience, prompting large-scale planting initiatives to mitigate its impacts. To ensure new plantations are adaptive to future climates, it is crucial to consider climate mismatches resulting from climate change when selecting tree species. However, current research is all species-based, which is not effective for species selection across species at specific plantation sites. Our research developed a novel site-based approach that can identify optimal tree species for specific planting sites under projected future climates. We evaluated the feasibility and effectiveness of this method across 10 representative sites in diverse climatic zones in China based on climate niche projections for 100 key tree species. Our findings demonstrated the necessity and effectiveness of this approach, which can select a suit of suitable tree species tailored for any potential planting site across China under different climate change scenarios. For instance, at Tibet Dongjiu Forest farm, <em>Aibes densa</em> and <em>Quercus pannosa</em> currently showed high suitability scores above 0.8 (on a scale of 0–1). However, by the 2080s, <em>Aibes densa</em>'s suitability was projected to drop to 0.25, while <em>Quercus pannosa</em> was expected to maintain its suitability. Conversely, <em>Quercus aquifolioides</em> currently had a low suitability of 0.08, but it was projected to increase to 0.74 by the 2080s. These findings demonstrate the importance of using this approach to avoid selecting the wrong species or overlooking potentially suitable species. In addition, our simulation analysis suggests that a dataset of 40–50 species is necessary to ensure that most planting sites can identify 2–3 suitable species. This advancement significantly enhances the precision and effectiveness of tree species selection strategies for local practitioners, offering vital insights for forestry, conservation, and ecological restoration projects. These results highlight the tremendous potential and practical applicability of our site-based approach in enhancing forestry adaptation and ecological functions in response to global climate change.</div></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"1 2","pages":"Article 100019"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sugar competition is important for sheath blight resistance in rice towards climate adaptation","authors":"Shuo Yang ,&nbsp;Chunyan Huang ,&nbsp;Dandan Li ,&nbsp;Naoki Yamamoto ,&nbsp;Xiaofeng Zhu ,&nbsp;Yuanhu Xuan","doi":"10.1016/j.csag.2024.100018","DOIUrl":"10.1016/j.csag.2024.100018","url":null,"abstract":"<div><p>Sheath blight (ShB) caused by <em>Rhizoctonia solani</em> Kühn is one of the most serious diseases in rice and is highly susceptible to climate and environmental influences, high humidity climate conditions combined with higher temperatures often lead to more severe occurrences of ShB. The heterotrophic <em>R. solani</em> and rice might compete for sugar at the border of interaction; however, the underlying mechanism remains unclear. In this study, we demonstrated that the expression level of <em>Sugar will eventually be exported transporters</em> (<em>SWEETs</em>) induction was higher in ShB susceptible varieties than in ShB resistant varieties by <em>R. solani</em> inoculation. Inoculation of <em>R. solani</em> revealed that most <em>sweet</em> mutants were less susceptible to ShB than the wild-type. Also, <em>sugar transporters</em> (<em>STPs</em>) gene expression was sensitive to <em>R. solani</em> infection. STPs were localized at the plasma membrane and transported hexose in yeast. Knockdown of <em>STP4</em> increased the susceptibility of rice to ShB. Interestingly, sequence analysis identified two monosaccharide transporter genes (hereafter named <em>RsMST</em>). RsMSTs transported 2-deoxyglucose, a toxic glucose analog in yeast, suggesting their role as glucose transporter. Spray-induced gene silencing of <em>RsMST1</em> or <em>RsMST2</em> dramatically suppressed their expression level and reduced virulence of <em>R. solani</em>. These data suggested that <em>R. solani</em> might induce SWEETs to efflux sugar from the cytosol to apoplast, and STP and RsMSTs compete for sugar at the apoplast for host defense and pathogen virulence. This study provided important insights for ShB-resistant breeding in rice.</p></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"1 2","pages":"Article 100018"},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950409024000182/pdfft?md5=8fa88b8d03e15fba29b9e67517a33825&pid=1-s2.0-S2950409024000182-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142162652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impacts of film mulching and ridging on N2O emissions, relevant functional genes, and microbial communities in rain-fed potato fields","authors":"Liwei Wang ,&nbsp;Jingjie Miao ,&nbsp;Yubo Meng ,&nbsp;Naijia Wang ,&nbsp;Kai Zhang ,&nbsp;Kangjun Guo ,&nbsp;Yue Zhang ,&nbsp;Jun Zhang ,&nbsp;Chen Feng ,&nbsp;Rajesh Kumar Soothar ,&nbsp;Jiaxin Zhang ,&nbsp;Xining Gao ,&nbsp;Cheng Wang","doi":"10.1016/j.csag.2024.100010","DOIUrl":"10.1016/j.csag.2024.100010","url":null,"abstract":"<div><p>Rain-fed potato (<em>Solanum tuberosum</em>) fields in drylands significantly contribute to nitrous oxide (N₂O) emissions, making them an important focus of agricultural greenhouse gas research. Film mulching and ridging are key agricultural methods in potato cultivation. Investigating the impact of these methods on N₂O emissions, nitrifying/denitrifying functional genes, and microbial communities can provide a theoretical basis for soil emission reduction and more sustainable dryland agriculture. We examine the effects of flat tillage with mulching, ridge tillage with mulching, flat tillage without mulching, and ridge tillage without mulching, on potato fields under natural rainfall conditions in Wuchuan County, China. N₂O emission fluxes were monitored using a static (dark) chamber and gas chromatography. Real-time quantitative PCR (q-PCR) was used to quantify abundances of nitrifying and denitrifying bacteria related to N₂O emissions at various potato-growth stages. Illumina high-throughput sequencing was used to investigate microbial community structure by targeting 16S rRNA genes; related soil elements (soil temperatures and moisture) are analyzed. Mulching and ridging indirectly influence N₂O emissions, nitrifying/denitrifying functional gene copy numbers, and microbial community structure by altering soil temperature and moisture. Cumulative N₂O emissions and emission intensity were both consistently higher in ridge tillage with mulching during the potato-growing period. Ammonia-oxidizing archaea are the main microorganisms that control N₂O emissions, with nitrification-coupled denitrification also being an important mechanism contributing to high N₂O emissions during soil dry–wet cycles. Increased soil temperature and moisture elevated N₂O emissions and functional gene copy numbers. The combination of mulching and ridging effectively uses the characteristics of both practices, making <em>Nitrospira</em> the dominant genus, and significantly increases N₂O emissions.</p></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"1 1","pages":"Article 100010"},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950409024000108/pdfft?md5=186578f057fa786ea8bf7e8a742566e6&pid=1-s2.0-S2950409024000108-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141637058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Near surface camera informed agricultural land monitoring for climate smart agriculture","authors":"Le Yu ,&nbsp;Zhenrong Du ,&nbsp;Xiyu Li ,&nbsp;Qiang Zhao ,&nbsp;Hui Wu ,&nbsp;Duoji weise ,&nbsp;Xinqun Yuan ,&nbsp;Yuanzheng Yang ,&nbsp;Wenhua Cai ,&nbsp;Weimin Song ,&nbsp;Pei Wang ,&nbsp;Zhicong Zhao ,&nbsp;Ying Long ,&nbsp;Yongguang Zhang ,&nbsp;Jinbang Peng ,&nbsp;Xiaoping Xin ,&nbsp;Fei Xu ,&nbsp;Miaogen Shen ,&nbsp;Hui Wang ,&nbsp;Yuanmei Jiao ,&nbsp;Yong Luo","doi":"10.1016/j.csag.2024.100008","DOIUrl":"https://doi.org/10.1016/j.csag.2024.100008","url":null,"abstract":"<div><p>Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing is limited by revisit cycles and weather conditions, leading to gaps in agricultural monitoring. To address these limitations, we designed and deployed a Near Surface Camera (NSCam) Network across China, and explored its application in agricultural land monitoring and achieving climate-smart agriculture (CSA). By analyzing the image data captured by the NSCam Network, we can accurately assess long-term or abrupt agricultural land changes. According to the preliminary monitoring results, integrating NSCam data with remote sensing imagery greatly enhances the temporal details and accuracy of agricultural monitoring, aiding agricultural managers in making informed decisions. The impacts of abnormal weather conditions and human activities on agricultural land, which are not captured by remote sensing imagery, can be complemented by incorporating our NSCam Network. The successful implementation of this method underscores its potential for broader application in CSA, promoting resilient and sustainable agricultural practices.</p></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"1 1","pages":"Article 100008"},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S295040902400008X/pdfft?md5=6469db54577239abf9e1cab9b8ea62db&pid=1-s2.0-S295040902400008X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141606240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supporting smallholder livestock farmers’ adaptive capacity to climate change in Kenya: What role does entrepreneurial orientation and uptake of CSA play?","authors":"Evaline Chepng'etich ,&nbsp;Josiah Mwangi Ateka ,&nbsp;Robert Mbeche ,&nbsp;Forah Obebo","doi":"10.1016/j.csag.2024.100007","DOIUrl":"10.1016/j.csag.2024.100007","url":null,"abstract":"<div><p>Improving smallholder farmers' adaptive capacity to climate change has become a major concern of governments and development agencies. Adaptive capacity determines the inherent ability of a system to cope with vulnerability to climate change. This paper used cross sectional survey data of 737 livestock producing households to assess determinants of adaptive capacity among Arid and Semi-Arid (ASAL) communities in Kenya. Specifically, we focused on the role of entrepreneurship orientation (risk taking, proactiveness and innovativeness) and uptake of climate smart agricultural (CSA) practices in improving adaptive capacity – a dimension which has received limited research attention. Adaptive capacity was measured using a set of indicators representing the five capitals in the Sustainable Livelihood Framework (SLF). The determinants of adaptive capacity were analyzed using fractional and censored regression models. The results revealed mixed influence of entrepreneurship orientation on adaptive capacity. While risk taking and proactiveness were positively associated with a higher adaptive capacity, innovativeness did not have any influence. Similarly, uptake of livestock CSA practices was associated with a higher level of adaptive capacity. Other factors that positively influenced adaptive capacity were age, gender, education level, diversity of income, access to extension services, credit, and collective action. The findings suggest that a strategy to promote entrepreneurial orientation, uptake of CSA, accumulation of human and financial capital would enhance livestock producers’ adaptive capacity.</p></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"1 1","pages":"Article 100007"},"PeriodicalIF":0.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950409024000078/pdfft?md5=2f5748e5f1a8cac7d282495dbcbb4e11&pid=1-s2.0-S2950409024000078-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141699944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}