Remote Sensing of Forage Droughts in the Dry Chaco (Argentina)

IF 2.4 3区环境科学与生态学 Q2 ECOLOGY

Rangeland Ecology & Management Pub Date : 2025-08-30 DOI:10.1016/j.rama.2025.07.014

Lisandro Blanco , Walter Agüero , Alicia Sancho , Marcos Texeira

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引用次数: 0

Abstract

Background

Drought is widely recognized as one of the most complex natural hazards due to its gradual onset and long-lasting impacts. With climate change, droughts are becoming increasingly intense, frequent, and prolonged, particularly in arid and semiarid rangelands, posing a serious threat to the sustainability of livestock systems. The objective of this study was to develop, calibrate, and validate a methodology for detecting forage droughts and monitoring their spatial and temporal patterns using satellite-derived anomalies in the Normalized Difference Vegetation Index (NDVI), hereafter NDVI anomalie (NDVIA).

Methods

Forage droughts are defined as temporary reductions in forage productivity in rangelands, grasslands, and pastures caused by rainfall falling below the long-term average. These reductions are so severe that even with adaptive forage management, the forage biomass accumulated during the growing season is insufficient to sustain livestock during the vegetative rest period (forage drought model). The study focuses on the dry Chaco region of Argentina. NDVIA values were correlated with forage biomass data collected in the field at 20 sites over a 10-yr period. Using a logistic regression model, the NDVIA threshold indicating the presence or absence of forage drought was determined.

Results

Results revealed a significant relationship (P < 0.05) between NDVIA and forage drought presence/absence. During the study period (2001–2023), forage droughts in the region typically recurred every 5 ± 2 growing seasons. A cluster analysis identified two subzones with significant differences (P < 0.05) in the temporal dynamics of forage drought occurrence.

Implications

The combined application of the forage drought model and this NDVI-based monitoring system could serve as a “guidebook” for implementing forage management strategies at the farm scale (e.g., adaptive forage management) and shaping public policies at the regional scale (e.g., satellite index-based insurance). This methodological approach, first-of-its-kind in region, could be adapted to other arid and semiarid ecosystems globally, enhancing the early warning and management of forage droughts.

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阿根廷查科干旱区牧草干旱遥感研究

干旱是公认的最复杂的自然灾害之一，因其发生缓慢且影响持久。随着气候变化，干旱变得越来越严重、频繁和持续时间越来越长，特别是在干旱和半干旱的牧场，对畜牧业系统的可持续性构成严重威胁。本研究的目的是开发、校准和验证一种方法，该方法可以利用归一化植被指数（NDVI）（以下简称NDVI异常（NDVIA））的卫星衍生异常来检测牧草干旱并监测其时空模式。方法草料干旱被定义为由于降雨量低于长期平均水平而导致的牧场、草原和牧场草料生产力的暂时下降。这些减少是如此严重，以至于即使采用适应性饲料管理，在生长季节积累的饲料生物量也不足以在营养休息期间维持牲畜（饲料干旱模型）。这项研究的重点是阿根廷干旱的查科地区。NDVIA值与在20个地点收集的10年牧草生物量数据相关。利用logistic回归模型，确定了牧草干旱存在与否的NDVIA阈值。结果NDVIA与牧草干旱存在与否呈显著相关（P <； 0.05）。研究期间（2001-2023年），该地区草料干旱每5个 ± 2个生长季发生一次。聚类分析发现两个亚区在牧草干旱发生的时间动态上存在显著差异（P <； 0.05）。牧草干旱模型与基于ndvi的监测系统的结合应用可以作为在农场规模上实施牧草管理策略（如适应性牧草管理）和在区域规模上制定公共政策（如基于卫星指数的保险）的“指南”。该方法在区域内首创，可适用于全球其他干旱和半干旱生态系统，加强牧草干旱的预警和管理。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Rangeland Ecology & Management 农林科学-环境科学

CiteScore

4.60

自引率

13.00%

发文量

审稿时长

12-24 weeks

期刊介绍： Rangeland Ecology & Management publishes all topics-including ecology, management, socioeconomic and policy-pertaining to global rangelands. The journal''s mission is to inform academics, ecosystem managers and policy makers of science-based information to promote sound rangeland stewardship. Author submissions are published in five manuscript categories: original research papers, high-profile forum topics, concept syntheses, as well as research and technical notes. Rangelands represent approximately 50% of the Earth''s land area and provision multiple ecosystem services for large human populations. This expansive and diverse land area functions as coupled human-ecological systems. Knowledge of both social and biophysical system components and their interactions represent the foundation for informed rangeland stewardship. Rangeland Ecology & Management uniquely integrates information from multiple system components to address current and pending challenges confronting global rangelands.