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Ensuring global food security and adapting to the challenges posed by climate change, particularly in rainfed agriculture, are paramount concerns. This research investigates the impacts of climate change on the yield of the potato crop variety Imilla Negra (Solanum tuberosum spp.) under the extreme climatic conditions of the Peruvian Altiplano. From the experimentation in six crop plots under a rainfed agricultural system, periodic crop growth parameter measurements were obtained from 2017 to 2018. The results showed a good performance of the AquaCrop model in the calibration and validation, successfully simulating crop growth and yield parameters. Climate projections showed precipitation decreases and temperature and evapotranspiration increases for the representative concentration pathway (RCP), RCP 4.5, and RCP 8.5 scenarios in 2023–2050. A comparison of crop yields between the base period (2006–2021) and the period 2023–2037 showed no significant changes, whereas a more considerable decrease was observed for the period 2038–2050. It is concluded that climate change generates moderate impacts on potato crop yields under the rainfed agricultural system in the Peruvian Altiplano due to the average reduction in precipitation. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is a crucial component of the planet and is essential for agriculture development. Our region is known for its livestock and agricultural activities. Hence, understanding the spatial distribution of SOC is crucial for sustainable land management of soils in the mountain ecosystems in the Andes. The methodology consisted of obtaining 53 soil samples from depths of 0 to 105 cm, which were analyzed to obtain SOC (Kg/m2) and organic matter (OM) (%). Ordinary kriging, a geostatistical method, was used to determine SOC. Pearson's statistical method was applied to determine the association between SOC and precipitation, temperature, altitude, and organic matter and gave acceptable correlations of 0.38, −0.32, 0.40, and 0.59. These results were used to apply weighting criteria for climatological and environmental variables. The weighted overlay tool was used for modeling and mapping the spatial variability of SOC. The estimated spatial distribution of SOC in the micro-watershed reveals an increasing trend from south to north, specifically within the 0–20 cm depth profile. The study confirmed through the spatial analysis that regions with intensive agriculture have low reserves (<3 Kg/m2) of SOC, and areas without agricultural activity but with grazing have average resources of 3 Kg/m2 to 5 Kg/m2 of SOC. Finally, in the upper micro-watersheds where there is no agricultural activity, the reserves are high (5 Kg/m2 to 6.8 Kg/m2). Accordingly, we can promote sustainable and responsible land use practices that support long-term productivity, environmental protection, and societal well-being by prioritizing efficient land utilization, soil conservation, biodiversity conservation, land restoration, and informed land use planning in the high Andean ecosystem of Puno–Peru. [ABSTRACT FROM AUTHOR]

The objective of the research is to develop a global land use / land cover map (LULC) of the Apurímac Region, from ESA Sentinel-2 images with a resolution of 10 m. to predict 10 soil type classes throughout the year in order to generate a representative snapshot of 2020. The methodology used in the analysis is the machine learning model, for the classification it was based on Artificial Intelligence (AI). For the processing, 6 bands of Sentinel-2 surface reflectance data were used: visible blue, green, red, near-infrared and two short-wave infrared bands, to create the final map, the model is run on multiple dates of images throughout the year on the Google Earth Engine (GEE) platform. The results of the study determine the total area is 2 111 415.29 ha, where the water represents 9 392.84 ha. (0.44%), on the other hand, snow/ice occupies 227.89 ha, representing 0.01%, while cultivated land occupies an area of 34 408.09 ha, (1.63%), bushes/shrubs occupy most of 1 740 486.69 ha, which represents 82.435% of the total area. [ABSTRACT FROM AUTHOR]