23
DOI: https://doi.org/10.1016/j.crm.2024.100686
English
eng
Joseph, Jacob Emanuel
Contributor
<p><span style="margin: 0px; padding: 0px; color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif; font-size: 16px;"><span style="margin: 0px; padding: 0px;">Understanding growing period conditions is crucial for effective climate risk management strategies. Seasonal climate forecasts (SCF) are key in predicting these conditions and guiding risk management in <a href="https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/agricultural-science" title="Learn more about agriculture from ScienceDirect's AI-generated Topic Pages" class="topic-link" style="margin: 0px; padding: 0px; background-color: rgba(0, 0, 0, 0); word-break: break-word; text-decoration-line: underline; text-decoration-thickness: 1px; text-decoration-color: rgb(31, 31, 31); color: rgb(31, 31, 31); text-underline-offset: 1px;">agriculture</a><span style="margin: 0px; padding: 0px;">. However, low SCF adoption rates among <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/smallholder" title="Learn more about smallholder from ScienceDirect's AI-generated Topic Pages" class="topic-link" style="margin: 0px; padding: 0px; background-color: rgba(0, 0, 0, 0); word-break: break-word; text-decoration-line: underline; text-decoration-thickness: 1px; text-decoration-color: rgb(31, 31, 31); color: rgb(31, 31, 31); text-underline-offset: 1px;">smallholder</a> farmers are due to factors like uncertainty and lack of understanding. In this study, we evaluated the benefits of SCF in predicting </span></span><a href="https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/growing-season" title="Learn more about growing season from ScienceDirect's AI-generated Topic Pages" class="topic-link" style="margin: 0px; padding: 0px; background-color: rgba(0, 0, 0, 0); word-break: break-word; text-decoration-line: underline; text-decoration-thickness: 1px; text-decoration-color: rgb(31, 31, 31); color: rgb(31, 31, 31); text-underline-offset: 1px;">growing season</a> conditions, and crop performance, and developing climate risk management strategies in Kongwa district, Tanzania. We used sea surface </span><a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/temperature-anomaly" title="Learn more about temperature anomalies from ScienceDirect's AI-generated Topic Pages" class="topic-link" style="margin: 0px; padding: 0px; background-color: rgba(0, 0, 0, 0); word-break: break-word; text-decoration-line: underline; text-decoration-thickness: 1px; text-decoration-color: rgb(31, 31, 31); color: rgb(31, 31, 31); text-underline-offset: 1px; font-family: ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif; font-size: 16px;">temperature anomalies</a><span style="margin: 0px; padding: 0px; color: rgb(31, 31, 31); font-family: ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif; font-size: 16px;"><span style="margin: 0px; padding: 0px;"> (SSTa) from the Indian and Pacific Ocean regions to predict seasonal rainfall onset dates using the k-nearest neighbor model. Contrary to traditional approaches, the study established the use of rainfall onset dates as the criterion for predicting and describing growing period conditions. We then evaluated forecast skills and the profitability of using SCF in <a href="https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/crop-management" title="Learn more about crop management from ScienceDirect's AI-generated Topic Pages" class="topic-link" style="margin: 0px; padding: 0px; background-color: rgba(0, 0, 0, 0); word-break: break-word; text-decoration-line: underline; text-decoration-thickness: 1px; text-decoration-color: rgb(31, 31, 31); color: rgb(31, 31, 31); text-underline-offset: 1px;">crop management</a> with the Agricultural Production System sIMulator (APSIM) coupled with a simple bio-economic model. Our findings show that SSTa significantly influences rainfall variability and accurately predicts rainfall onset dates. Onset dates proved more effective than traditional methods in depicting key growing period characteristics, including rainfall variability and distribution. Including SCF in climate risk management proved beneficial for maize and sorghum production both agronomically and economically. Not using SCF posed a higher risk to crop production, with an 80% probability of yield losses, especially in late-onset seasons. We conclude that while SCF has potential benefits, improvements are needed in its generation and dissemination. Enhancing the network of </span><a href="https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/extension-agents" title="Learn more about extension agents from ScienceDirect's AI-generated Topic Pages" class="topic-link" style="margin: 0px; padding: 0px; background-color: rgba(0, 0, 0, 0); word-break: break-word; text-decoration-line: underline; text-decoration-thickness: 1px; text-decoration-color: rgb(31, 31, 31); color: rgb(31, 31, 31); text-underline-offset: 1px;">extension agents</a> could facilitate better understanding and adoption by smallholder farmers.</span></p>
Elsevier
2025
pdf
Climate Risk Management
How beneficial are seasonal climate forecasts for climate risk management? An appraisal for crop production in Tanzania
mixed material
Elsevier