Climate Smart Agriculture最新文献

{"title":"Factors affecting decision-making to strengthen climate resilience of smallholder farms in the Centre region of Cameroon","authors":"Pierre Marie Chimi ,&nbsp;William Armand Mala ,&nbsp;Jean Louis Fobane ,&nbsp;Karimou Ngamsou Abdel ,&nbsp;Baruch Batamack Nkoué ,&nbsp;Lethicia Flavine Feunang Nganmeni ,&nbsp;Eusebe Ydelphonse Nyonce Pokam ,&nbsp;Sophie Patience Endalle Minfele ,&nbsp;John Hermann Matick ,&nbsp;Franc Marley Tchandjie ,&nbsp;François Manga Essouma ,&nbsp;Joseph Martin Bell","doi":"10.1016/j.csag.2024.100004","DOIUrl":"10.1016/j.csag.2024.100004","url":null,"abstract":"<div><p>This study examined the resilience to climate change of smallholder family farms in the Centre Region of Cameroon. Data were collected using a mixed-methods strategy and analyzed using descriptive, multivariate, and inferential statistics. Family farms exhibited a mean climate resilience index of 0.46 (medium), with the Ntui, Mbangassina, Batchenga, and Obala regions scoring 0.42, 0.44, 0.47, and 0.51, respectively. Family farmers had a high transformation capacity (59.07 ​%), a low adaptation capacity (32.10 ​%), and a very low absorption capacity (8.82 ​%). Logistic regression revealed significant causal relationships (<em>p</em> ​&lt; ​0.05) between the capacity of the farms to adapt to climate fluctuations and change and annual income, access to agricultural inputs, access to agricultural machinery, and membership in a farmers organization. These are the primary factors that could significantly increase climate resilience in Cameroonian family farms. Consequently, policymakers in these regions and beyond should consider these as indicators when developing policies to strengthen the climate resilience of local agricultural systems. In doing so, they should also consider community monitoring and indigenous knowledge, which can help bridge the gap between local adverse impacts and the necessary adaptations to climate change.</p></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"1 1","pages":"Article 100004"},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950409024000042/pdfft?md5=30e1d6a9348ce693c369713b8bf718f9&pid=1-s2.0-S2950409024000042-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141023372","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":"Climate-smart agriculture: Insights and challenges","authors":"Yilai Lou ,&nbsp;Liangshan Feng ,&nbsp;Wen Xing,&nbsp;Ning Hu,&nbsp;Elke Noellemeyer,&nbsp;Edith Le Cadre,&nbsp;Kazunori Minamikawa,&nbsp;Pardon Muchaonyerwa,&nbsp;Mohamed A.E. AbdelRahman,&nbsp;Érika Flávia Machado Pinheiro,&nbsp;Wim de Vries,&nbsp;Jian Liu,&nbsp;Scott X. Chang,&nbsp;Jizhong Zhou,&nbsp;Zhanxiang Sun,&nbsp;Weiping Hao,&nbsp;Xurong Mei","doi":"10.1016/j.csag.2024.100003","DOIUrl":"10.1016/j.csag.2024.100003","url":null,"abstract":"<div><p>Agriculture, broadly defined to include crop and livestock production, forestry, aquaculture and fishery, represents a key source or sink of greenhouse gas emissions. It is also a vulnerable sector under climate change. The term climate-smart agriculture has been widely used since its inception in 2010, but no clear and unified understanding of its scientific meaning exists. Here, we systematically analyzed the relationship between agriculture and climate change and interpreted the scientific definition of climate-smart agriculture. We believe that climate-smart agriculture represents a modern production approach to coordinatively promote food security, climate mitigation benefits and agricultural adaptation to climate change towards the Sustainable Development Goals. In addition, due to the worsening global climate change situation, we expounded on the urgency and major challenges in promoting climate-smart agriculture.</p></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"1 1","pages":"Article 100003"},"PeriodicalIF":0.0,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950409024000030/pdfft?md5=5c5abd295882d7dd6990e43dd33886ce&pid=1-s2.0-S2950409024000030-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141027560","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":"Coupling of microbial-explicit model and machine learning improves the prediction and turnover process simulation of soil organic carbon","authors":"Xuebin Xu ,&nbsp;Xianting Wang ,&nbsp;Ping Zhou ,&nbsp;Zhenke Zhu ,&nbsp;Liang Wei ,&nbsp;Shuang Wang ,&nbsp;Periyasamy Rathinapriya ,&nbsp;Qicheng Bei ,&nbsp;Jinfei Feng ,&nbsp;Fuping Fang ,&nbsp;Jianping Chen ,&nbsp;Tida Ge","doi":"10.1016/j.csag.2024.100001","DOIUrl":"10.1016/j.csag.2024.100001","url":null,"abstract":"<div><p>Modeling soil organic carbon (SOC) is helpful for understanding its distribution and turnover processes, which can guide the implementation of effective measures for carbon (C) sequestration and enhance land productivity. Process-based simulation with high interpretability and extrapolation, and machine learning modeling with high flexibility are two common methods for investigating SOC distribution and turnover. To take advantage of both methods, we developed a hybrid model by coupling of a two-carbon pool microbial model and machine learning for SOC modeling. Here, we assessed the SOC model's predictive, mapping, and interpretability capabilities for the SOC turnover process on Ningbo region. The results indicate that the microbial model with density-dependence (β ​= ​2) and microbial biomass carbon simulation performed better in modeling the parameters of the microbial-based C cycle, such as microbial carbon use efficiency (CUE), microbial mortality rate, and assimilation rate. By integrating this optimal microbial model and random forest (RF) model, the hybrid model improved the prediction accuracy of SOC, with an increased R² from 0.74 to 0.84, residual prediction deviation increased from 1.97 to 2.50, and reduced the root-mean-square error from 4.65 to 3.67 ​g ​kg⁻¹ compared to the conventional RF model. As a result, the predicted SOC distribution exhibited high spatial variation and provided abundant details. Microbial CUE and potential C input, represented by net primary productivity, emerged as the primary factors driving SOC distribution in Ningbo region. Projections of SOC under the CMIP6 SSP2-4.5 scenario revealed that regional C loss in high SOC areas was mainly caused by decreased microbial CUE and C input, induced by climate change. Our findings highlight the potential of combining the microbial-explicit model and machine learning to improve SOC prediction accuracy and understand SOC feedback in a changing climate.</p></div>","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"1 1","pages":"Article 100001"},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950409024000017/pdfft?md5=51776cd89ac145dabbf44e66f0e6d8b5&pid=1-s2.0-S2950409024000017-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140793462","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":"Dynamic Fluidic Sprinkler and Intelligent Sprinkler Irrigation Technologies","authors":"Xingye Zhu, Alexander Fordjour, Junping Liu, Shouqi Yuan","doi":"10.1007/978-981-19-8319-1","DOIUrl":"https://doi.org/10.1007/978-981-19-8319-1","url":null,"abstract":"","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74549486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fish Farms Effluents for Irrigation and Fertilizer: Field and Modeling Studies","authors":"A. Zohry, S. Ouda","doi":"10.1007/978-3-030-93111-7_3","DOIUrl":"https://doi.org/10.1007/978-3-030-93111-7_3","url":null,"abstract":"","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80250760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate-Smart Agriculture: Reducing Food Insecurity","authors":"S. Ouda, A. Zohry","doi":"10.1007/978-3-030-93111-7","DOIUrl":"https://doi.org/10.1007/978-3-030-93111-7","url":null,"abstract":"","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82052830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate Extremes and Crops","authors":"S. Ouda, A. Zohry","doi":"10.1007/978-3-030-93111-7_5","DOIUrl":"https://doi.org/10.1007/978-3-030-93111-7_5","url":null,"abstract":"","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"119 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77951463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil-Smart Practices: Integrated Soil Fertility Management","authors":"A. Zohry, S. Ouda","doi":"10.1007/978-3-030-93111-7_2","DOIUrl":"https://doi.org/10.1007/978-3-030-93111-7_2","url":null,"abstract":"","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85723791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration Between Crop-Smart, Water-Smart and Soil-Smart Practices","authors":"A. Zohry, S. Ouda","doi":"10.1007/978-3-030-93111-7_4","DOIUrl":"https://doi.org/10.1007/978-3-030-93111-7_4","url":null,"abstract":"","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"264 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91445131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanical Harvest of Fresh Market Apples","authors":"","doi":"10.1007/978-981-16-5316-2","DOIUrl":"https://doi.org/10.1007/978-981-16-5316-2","url":null,"abstract":"","PeriodicalId":100262,"journal":{"name":"Climate Smart Agriculture","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86368976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}