Climate change is causing an increase in global temperatures, with significant impacts on dairy production. This systematic review analyzes the challenges of new climate scenarios, focusing on the resilience and adaptation strategies of dairy systems. The PRISMA methodology guided the review process using the Scopus and Web of Science databases. After applying exclusion criteria, 30 articles published between 2015 and 2025 were selected. The studies included analyses of the effects of heat stress on animal parameters (I), socioeconomic parameters (II), and technological adaptation tools (III) in various geographic regions. Most research over the last decade addresses category (I), with a greater concentration in the Northern Hemisphere. The Temperature Humidity Index (THI) emerged as the main indicator of heat stress, associated with the physiological responses of dairy cattle. Heat stress reduces milk production, feed intake, reproductive performance, and overall animal health, often leading to increased slaughter rates. Adaptation strategies include genetic selection for heat tolerance, improved environmental monitoring, cooling technologies, and optimized nutritional and management practices, applicable to both pasture and feedlot systems. Despite being among the main producers, studies on the topic in South American countries are still scarce in the literature.

Well-managed pastures support watershed recharge by conserving soil, increasing organic matter, and enhancing water and nutrient retention, while also reducing erosion, nutrient leaching and surface run-off. This study used remote sensing to assess vegetation cover (Vc) in degraded pasture areas and examine its relationship with soil loss in the Brazilian savanna, employing vegetation indices and the Universal Soil Loss Equation (USLE). Given its socioeconomic importance, the Ribeirão Serra Negra Watershed in Piracanjuba, Goiás, Brazil was the focus of the research. Soil loss was estimated from freely available geographic data on climate, while soil class and relevance were assessed through a digital elevation model (DEM). The use of images from the Sentinel-2A orbital sensor alongside the Normalized Difference Vegetation Index (NDVI) also made it possible to quantify and classify Vc according to its condition. The findings reveal that pastures in this region exhibit varying levels of degradation, with intermediate degradation classes being most prevalent: ‘moderately degraded’ areas covered 3,919.25 hectares (ha) (56.87% of the total area), while ‘severely degraded’ areas accounted for 1,984.30 ha (28.79%). The most common index values ranged from 0.6 to 0.7, aligning with the higher prevalence of the moderately degraded class. Soil losses were considerable in these intermediate classes, affecting 5,683.19 ha (82.46%); the most frequent loss rate was estimated to lie between 20 and 40 Mg ha-1 year-1, covering 4271.34 ha (61.98%). Higher Vc indices were more frequently observed in areas with lower soil losses, demonstrating a significant inverse correlation (-0.80, p-value ≤ 0.05) between these variables, with a determination coefficient (R²) of 0.64.

Context: Production of sugarcane (Saccharum officinarum) in Brazil holds a significant global position. However, it faces challenges in yield optimization due to unfavorable edaphoclimatic conditions and technological limitations. Aim: This study aimed to correlate the spatio-temporal variability of sugarcane yield with edaphoclimatic conditions in the central region of Brazil. Methods: This study included 11 sugarcane producing municipalities in central region of Brazil. It utilized 47 years of historical data on yield, climate, and soil. To isolate the climatic effects on yield (Yr), technological trends (YrNT) were removed via simple linear regression adjustment, followed by cluster analysis. Key results: Four groups of Yr and YrNT were identified. Group 1 exhibited the highest average yield (77 Mg ha−1), while Group 4 had the lowest (47 Mg ha−1), with a yield gap of approximately 10.2 Mg ha−1. Municipalities with the highest Yr averages were in Clusters 3 and 4 for climate, and Clusters 2 and 3 for soil. Conclusions: The higher occurrence of anomalies lower than 1.0 σ for climate Group 3 of YrNT indicates that unfavorable climatic conditions combined with inadequate production technologies can lead to significant yield losses (26% of years). Evaluating sugarcane yield by considering crop cycles and edaphoclimatic factors before technological trends can provide a more accurate insight into yield variability. Implications: Understanding the relationship between edaphoclimatic factors and sugarcane yield variation can guide targeted interventions, aiding in the development of strategies to mitigate losses in sugarcane farming.

Crop management strategies are essential to achieve maximum crop yields. In this context, crop models can be used to understand yield variability. The aim of this study was to evaluate historical yields based on the sowing date and plant density. Field experiments were conducted during two growing seasons at two locations using a maturity group 7.6 cultivar, with distinct sowing dates and plant densities (10 to 50 plants m⁻²). After calibration, the model was applied to historical weather data (1990–2022) for nine locations across the Cerrado biome. The CSM-CROPGRO-Soybean model simulated soybean development with biases for physiological maturity of +8 and −7 days for calibration and evaluation, respectively. The relative root mean square error for yield was below 14%. Similar trends were observed for the leaf area index (LAI) across plant densities, while biomass partitioning proved to be a limiting component, mainly for stem biomass. The optimal plant population ranged from 20 to 30 plants m⁻², whereas a low density (10 plants m⁻²) resulted in higher yield losses under late sowing. Therefore, the use of crop simulation models is an effective tool to understand and optimize sowing dates and plant densities under historical climatic conditions in the Cerrado.

Water deficit induces a range of morphological and physiological changes in plants, triggering multiple adaptive responses. The aim of this study was to evaluate the morphological, physiological, and postharvest responses of mini watermelon plants subjected to two foliar silicon (Si) doses and three soil water tensions during two cycles. The experimental design was in randomized blocks, in a greenhouse, with a 2 × 3 × 2 factorial scheme, with four replications. Soil water tension affected plant growth, and silicon reduced plant length by 17.5%. Fruit traits were affected by the soil water tension, with silicon influencing fruit length, weight, and rind thickness. Transpiration declined from 4.06 to 1.21 mmol m⁻² s⁻¹ in cycle 1 and from 2.79 to 1.28 mmol m⁻² s⁻¹ in cycle 2 as soil water potential decreased from −30 to −65 kPa. Biochemical parameters respond to soil water tension, with total soluble carbohydrates increasing by 75% in cycle 1 and 378% in cycle 2 as soil water tension decreased from −30 to −65 kPa in the absence of silicon application. Silicon application promoted higher total soluble carbohydrate accumulation under severe water deficit conditions. In conclusion, soil water tension was the main determinant of crop performance, while silicon showed limited effects as a drought-mitigation strategy.