{"title":"Climate-adaptative management strategies for soybean production under ENSO scenarios in Southern Brazil: An in-silico analysis of crop failure risk","authors":"","doi":"10.1016/j.agsy.2024.104153","DOIUrl":null,"url":null,"abstract":"<div><h3>CONTEXT</h3><div>Soybeans (<em>Glycine</em> max L.) are a crucial crop for global food security, and the state of Rio Grande do Sul (RS), Brazil, plays a significant role. However, climate instability, particularly water stress (WS), is a major concern in this region, causing large interannual yield variability.</div></div><div><h3>OBJECTIVE</h3><div>This study aims to address this issue using an in-silico approach to: i) characterize WS and spatial patterns and their frequency within ENSO events; and ii) explore climate-adaptative management strategies such as planting dates and maturity groups to mitigate the risk of crop failure and maximize seed yield and profits.</div></div><div><h3>METHODS</h3><div>Crop growth simulations were performed testing three soybean maturity groups (MG, 5.0, 5.8, and 6.4) and eight planting dates (from October 5th to January 20th) over 30 years at 187 locations in RS, Brazil, using APSIM Next Generation. Failure risk was calculated as the percentage of simulations that yielded less than the economic break-even soybean yield in a given scenario.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>The simulated yields were clustered into four regions: Northeast, North, Central, and Southwest. Four WS seasonal patterns were then defined (no stress, early stress, late stress, and whole season stress). On average, WS reduced yields up to 2 Mg ha-1 (∼50 % relative to the maximum). WS varied among regions, with the SW experiencing more frequent and severe stress (up to 50 % of whole season stress during La Nina). ENSO events influenced WS frequency, with El Niño events associated with reduced stress and La Niña events to increased stress. The MG 5.0 resulted in a higher probability of failure risk in all regions. Early planting dates resulted in the highest yield variability (up to 5 Mg ha-1). Climate-adaptative management strategies, such as optimizing planting dates and maturity groups, resulted in a 15 % reduction in crop failure.</div></div><div><h3>SIGNIFICANCE</h3><div>Our findings provide valuable insights for developing targeted approaches to enhance soybean yield stability, thereby increasing the resilience of agriculture in the face of future climate uncertainties.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":null,"pages":null},"PeriodicalIF":6.1000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Agricultural Systems","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0308521X24003032","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
CONTEXT
Soybeans (Glycine max L.) are a crucial crop for global food security, and the state of Rio Grande do Sul (RS), Brazil, plays a significant role. However, climate instability, particularly water stress (WS), is a major concern in this region, causing large interannual yield variability.
OBJECTIVE
This study aims to address this issue using an in-silico approach to: i) characterize WS and spatial patterns and their frequency within ENSO events; and ii) explore climate-adaptative management strategies such as planting dates and maturity groups to mitigate the risk of crop failure and maximize seed yield and profits.
METHODS
Crop growth simulations were performed testing three soybean maturity groups (MG, 5.0, 5.8, and 6.4) and eight planting dates (from October 5th to January 20th) over 30 years at 187 locations in RS, Brazil, using APSIM Next Generation. Failure risk was calculated as the percentage of simulations that yielded less than the economic break-even soybean yield in a given scenario.
RESULTS AND CONCLUSIONS
The simulated yields were clustered into four regions: Northeast, North, Central, and Southwest. Four WS seasonal patterns were then defined (no stress, early stress, late stress, and whole season stress). On average, WS reduced yields up to 2 Mg ha-1 (∼50 % relative to the maximum). WS varied among regions, with the SW experiencing more frequent and severe stress (up to 50 % of whole season stress during La Nina). ENSO events influenced WS frequency, with El Niño events associated with reduced stress and La Niña events to increased stress. The MG 5.0 resulted in a higher probability of failure risk in all regions. Early planting dates resulted in the highest yield variability (up to 5 Mg ha-1). Climate-adaptative management strategies, such as optimizing planting dates and maturity groups, resulted in a 15 % reduction in crop failure.
SIGNIFICANCE
Our findings provide valuable insights for developing targeted approaches to enhance soybean yield stability, thereby increasing the resilience of agriculture in the face of future climate uncertainties.
期刊介绍:
Agricultural Systems is an international journal that deals with interactions - among the components of agricultural systems, among hierarchical levels of agricultural systems, between agricultural and other land use systems, and between agricultural systems and their natural, social and economic environments.
The scope includes the development and application of systems analysis methodologies in the following areas:
Systems approaches in the sustainable intensification of agriculture; pathways for sustainable intensification; crop-livestock integration; farm-level resource allocation; quantification of benefits and trade-offs at farm to landscape levels; integrative, participatory and dynamic modelling approaches for qualitative and quantitative assessments of agricultural systems and decision making;
The interactions between agricultural and non-agricultural landscapes; the multiple services of agricultural systems; food security and the environment;
Global change and adaptation science; transformational adaptations as driven by changes in climate, policy, values and attitudes influencing the design of farming systems;
Development and application of farming systems design tools and methods for impact, scenario and case study analysis; managing the complexities of dynamic agricultural systems; innovation systems and multi stakeholder arrangements that support or promote change and (or) inform policy decisions.