Climate-adaptative management strategies for soybean production under ENSO scenarios in Southern Brazil: An in-silico analysis of crop failure risk

IF 6.1 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Agricultural Systems Pub Date : 2024-10-24 DOI:10.1016/j.agsy.2024.104153

Gabriel Hintz , Ana Carcedo , Luiz Felipe Almeida , Geomar Corassa , Tiago Horbe , Luan Pott , Raí Schwalbert , Trevor Hefley , P.V. Vara Prasad , Ignacio Ciampitti

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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.

Abstract Image

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巴西南部厄尔尼诺/南方涛动情景下大豆生产的气候适应性管理策略：对作物歉收风险的模拟分析

背景大豆（Glycine max L.）是全球粮食安全的重要作物，巴西南里奥格兰德州（RS）在其中发挥着重要作用。本研究旨在利用一种微观方法来解决这一问题：i) 描述ENSO事件中的水分胁迫和空间模式及其频率；ii) 探索适应气候的管理策略，如播种日期和成熟度组，以降低作物歉收的风险，最大限度地提高种子产量和利润。方法使用 APSIM Next Generation 对巴西 RS 地区 187 个地点的三个大豆成熟度组（MG、5.0、5.8 和 6.4）和八个播种日期（从 10 月 5 日到 1 月 20 日）进行了 30 年的作物生长模拟测试。结果和结论模拟产量分为四个区域：东北部、北部、中部和南部：将模拟产量划分为四个区域：东北部、北部、中部和西南部。然后定义了四种 WS 季节模式（无胁迫、早期胁迫、晚期胁迫和全季胁迫）。平均而言，WS 使产量减少达 200 万克/公顷-1（相对于最大值减少 50%）。不同地区的 WS 不尽相同，西南部的胁迫更为频繁和严重（在拉尼娜现象期间，全季胁迫高达 50%）。厄尔尼诺/南方涛动现象影响 WS 频率，厄尔尼诺现象导致胁迫减少，而拉尼娜现象导致胁迫增加。MG 5.0 在所有地区都导致了更高的倒伏风险概率。早播日期导致产量变化最大（最高达 500 万克/公顷-1）。我们的研究结果为制定有针对性的方法来提高大豆产量的稳定性提供了有价值的见解，从而提高了农业在未来气候不确定性面前的抗灾能力。

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来源期刊

Agricultural Systems 农林科学-农业综合

CiteScore

13.30

自引率

7.60%

发文量

174

审稿时长

30 days

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.