Context Climate change poses a significant threat to dairy production systems, particularly in tropical regions where rising temperatures and humidity intensify heat stress in cattle, such as Goiás (Brazil). Heat stress compromises animal welfare, productivity, and farm sustainability, necessitating regional-scale impact assessments using high-resolution climate projections. Objective This study aimed to assess the future impacts of climate change on dairy cattle welfare and milk production in Goiás, Brazil, under intermediate (RCP4.5) and high-emissions (RCP8.5) scenarios across short- (2021–2040), medium- (2041–2060), and long-term (2061–2080) horizons. Methods High-resolution (5 km) climate projections from the Eta Regional Climate Model, driven by HadGEM2-ES, were used to extract daily air temperature and relative humidity. Heat stress was quantified using the Temperature–Humidity Index (THI), with severe stress defined as THI > 78. Animal welfare impacts were evaluated by calculating the seasonal number of days with severe THI (ndaysTHI). Milk production losses were estimated using the Milk Production Decline (MPD) index for moderate-producing (25 kg·cow−1·day−1) and high-producing (40 kg·cow−1·day−1) herds. Seasonal analyses were conducted for DJF (summer), MAM (autumn), JJA (winter), and SON (spring), comparing future projections with a historical baseline (1986–2005). Results and conclusions All future scenarios projected significant increases in air temperature (up to +7 °C under RCP8.5 by 2061–2080) and reductions in relative humidity (locally exceeding −25%). Consequently, THI values increased statewide, with anomalies reaching up to +6 units by the late century under RCP8.5. The number of days with severe heat stress (THI > 78) increased progressively throughout the century. Under RCP8.5 (2061–2080), seasonal increases exceeded 70 additional severe-heat days in some regions, indicating near-continuous exposure during spring and summer. The northwestern region consistently emerged as the most vulnerable hotspot. Milk production losses intensified proportionally to productivity level. Under historical conditions, MPD reached up to 4 kg·cow−1·day−1 for moderate-producing cows and 7 kg·cow−1·day−1 for high-producing cows. Under RCP8.5 (2061–2080), projected declines reached up to 10 kg·cow−1·day−1 in high-producing herds, with losses occurring across all seasons. Even under RCP4.5, significant late-century intensification of production losses was observed. Climate change is projected to transform heat stress in Goiás from a predominantly seasonal constraint into a chronic, year-round challenge, particularly under high-emission scenarios. The substantial increase in severe heat stress days and associated milk production declines—especially in high-producing systems—poses serious risks to animal welfare, productivity, and regional sustainability. The northwestern region represents a critical vulnerability hotspot. Significance These findings highlight the urgent need for integrated adaptation strategies, including improved housing and cooling systems, shade management, genetic selection for thermotolerance, and precision livestock technologies to ensure climate-resilient dairy production in tropical regions.
CONTEXT Low-carbon rice farming is widely recognized as a key strategy for agricultural decarbonization, yet adoption remains limited among smallholder farmers despite expanding AI-enabled advisory and increasing awareness of climate risks. Existing adoption-based models provide limited explanation for why improved information and awareness often fail to translate into low-carbon transition. OBJECTIVE This study explains how farmers translate information into transition intention under real-world constraints by conceptualizing low-carbon agriculture as a constrained system transition. It develops a process-based cognitive decision pathway linking risk perception, perceived feasibility, and decision confidence, while positioning AI-enabled advisory as cognitive infrastructure rather than a direct transition intention. METHODS Survey data were collected from 398 smallholder rice farmers in Vietnam's Mekong Delta. Partial least squares structural equation modelling (PLS-SEM) was employed to estimate a framework integrating sequential mediation and moderated mediation, with government support and market access modelled as system-level enabling conditions. RESULTS AND CONCLUSIONS AI-enabled advisory significantly enhances risk perception, perceived feasibility, and decision confidence but has no direct effect on transition intention. Perceived feasibility emerges as the critical implementation bottleneck, while decision confidence functions as the final cognitive mechanism translating favourable feasibility assessments into transition intention. In contrast, risk perception increases awareness but does not significantly improve feasibility, revealing a fundamental tension between recognizing climate risks and judging transition to be practically achievable. Government support and market access further strengthen this decision pathway by reinforcing the translation of cognitive readiness into transition intention. SIGNIFICANCE This study makes three principal contributions. First, it reconceptualizes low-carbon agriculture as a constrained system transition rather than a discrete adoption decision. Second, it advances a process-based cognitive decision pathway that explains how farmers progress from implementation feasibility to confidence to act under uncertainty. Third, it repositions AI-enabled advisory as cognitive infrastructure, demonstrating that its effectiveness depends on enabling institutional and market conditions rather than information provision alone. These findings provide a more integrated theoretical framework for understanding low-carbon agricultural transition and offer practical guidance for designing AI-enabled and system-oriented transition policies.
Context Rainfed agriculture in semi-arid regions is constrained not only by rainfall scarcity but also by inefficient conversion of rainfall into plant-available water. Large yield gaps persist because of poor rainfall partitioning, limited soil-moisture storage, and weak integration of land, water, and cropping-system management. Objective This study aimed to develop and validate an integrated Land Resource Inventory (LRI)–hydrology framework for improving water availability and guiding sustainable cropping-system intensification in semi-arid rainfed systems. Methods The framework was developed using long-term experimental evidence from ICRISAT's heritage watersheds in Alfisols and Vertisols, integrating soil–water characterization, rainfall–runoff relationships, landform management, and water budgeting. It was subsequently applied in Bundelkhand, central India, across a ~ 5000-ha geographical landscape (~3000 ha agricultural land), using LRI-based planning, hydrological analysis, and targeted natural resource management interventions. Results and conclusions Heritage watershed results demonstrated strong contrasts in soil–water dynamics, with Vertisols (150–200 mm plant-available water) providing greater moisture-buffering capacity than Alfisols (80–120 mm). Raised-bed landform interventions reduced runoff by ~20–40% and improved rainfall retention and soil-moisture availability. At landscape scale, targeted interventions created ~731,000 m3 of additional water-storage capacity and substantially improved shallow groundwater availability. The landscape-equivalent supplemental irrigation requirement increased from ~35 to ~78 mm as cropping systems intensified; the incremental demand (~43 mm) was comparable to the estimated additional annual runoff-harvesting opportunity (~40–45 mm), complemented by in-situ moisture conservation. Improved water availability enabled a transition from moisture-limited systems, including blackgram-based systems and seasonal fallows, towards more intensive and diversified systems involving groundnut, wheat, and chickpea. Agricultural production more than doubled, while net returns increased from USD 427 to 1107 ha−1 year−1 (approximately USD 854 to 2214 household−1 year−1). The results demonstrate that integrating soil, hydrology, water availability, and crop-water demand can support sustainable intensification through improved management of available landscape resources. Significance The LRI–hydrology framework provides a scalable and replicable approach for translating soil and hydrological understanding into landscape-level decisions on water harvesting, cropping-system design, and resource management. It offers a pathway for improving water productivity, strengthening climate resilience, and supporting land degradation neutrality across semi-arid rainfed agro-ecologies in Asia and Africa.
CONTEXT Many jurisdictions have policies that require simultaneous reductions in greenhouse gas (GHG) emissions and nitrogen (N) and phosphorus (P) losses to water. In New Zealand's pasture-based dairy sector, farm planning tools are used to target actions to mitigate emissions and nutrient losses. However, the regional effectiveness and co-benefits from these actions are highly variable. OBJECTIVE We aimed to determine if widely promoted on farm actions, implemented independently, could reduce emissions and nutrient losses with minimal trade-offs, and if effectiveness would vary by region. METHODS Using data from over 750 dairy farms (2013–2024) and > 6300 data records in New Zealand, we used gradient boosted regression trees to model changes in emissions and losses in response to six mitigation actions: three examined reduced fertiliser usage options, two improved irrigation and effluent practices, and one avoided winter forage cropping. RESULTS AND CONCLUSIONS Modelling indicated reductions in N and P losses to water (0–21% and 1–10%, respectively) and GHG emissions (1–11%) with no statistically significant trade-offs between water and air outcomes. Avoiding winter forage cropping substantially reduced N and P losses in regions where the practice is prevalent. The greatest overall benefits were observed in Canterbury and Southland. Results demonstrate that widely promoted good farming practices can deliver simultaneous improvements in water quality and climate performance. SIGNIFICANCE This study provides national-scale evidence to support ongoing good farming practice implementation and give farmers confidence that multiple environmental outcomes can be pursued with minimal unintended consequences, while highlighting that the extent and feasibility of uptake will depend on individual farm systems and local conditions.
