Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability for the farmer’s economy. In this study, we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using vegetation indices (VIs). A total of 10 VIs (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. Highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA showed clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimating the performance, showing greater precision at 51 DAS. The use of unmanned aerial vehicles (UAVs) to monitor crops allows us to optimize resources and helps in making timely decisions in agriculture in Peru.

In Peru, common bean varieties adapt very well to arid zones, and it is essential to strengthen their evaluations accurately during their phenological stage by using remote sensors and UAV. However, this technology has not been widely adopted in the Peruvian agricultural system, causing a lack of information and precision data on this crop. Here, we predicted the yield of four beans cultivars by using multispectral images, vegetation indices (VIs) and multiple linear correlations (with 11 VIs) in 13 different periods of their phenological development. The multispectral images were analyzed with two methods: (1) a mask of only the crop canopy with supervised classification constructed with QGIS software; and (2) the grids corresponding to each plot (n = 48) without classification. The prediction models can be estimated with higher accuracy when bean plants reached maximum canopy cover (vegetative and reproductive stages), obtaining higher R² for the c2000 cultivar (0.942) with the CIG, PCB, DVI, EVI and TVI indices with method 2. Similarly, with five VIs, the camanejo cultivar showed the highest R² for both methods 1 and 2 (0.89 and 0.837) in the reproductive stage. The models better predicted the yield in the phenological stages V3–V4 and R6–R8 for all bean cultivars. This work demonstrated the utility of UAV tools and the use of multispectral images to predict yield before harvest under the Peruvian arid ecosystem.

Dry forests are home to large amounts of biodiversity, are providers of ecosystem services, and control the advance of deserts. However, globally, these ecosystems are being threatened by various factors such as climate change, deforestation, and land use and land cover (LULC). The objective of this study was to identify the dynamics of LULC changes and the factors associated with the transformations of the dry forest in the Tumbes region (Peru) using Google Earth Engine (GEE). For this, the annual collection of Sentinel 2 (S2) satellite images of 2017 and 2021 was analyzed. Six types of LULC were identified, namely urban area (AU), agricultural land (AL), land without or with little vegetation (LW), water body (WB), dense dry forest (DDF), and open dry forest (ODF). Subsequently, we applied the Random Forest (RF) method for the classification. LULC maps reported accuracies greater than 89%. In turn, the rates of DDF and ODF between 2017 and 2021 remained unchanged at around 82%. Likewise, the largest net change occurred in the areas of WB, AL, and UA, at 51, 22, and 21%, respectively. Meanwhile, forest cover reported a loss of 4% (165.09 km²) of the total area in the analyzed period (2017–2021). The application of GEE allowed for an evaluation of the changes in forest cover and land use in the dry forest, and from this, it provided important information for the sustainable management of this ecosystem.

It is important to carry out the morphological characterization of coffee parent plants and the physicochemical properties of the associated soils in the Amazon region, Peru, in order to achieve germplasm conservation. One hundred coffee mother plants were identified and located in five provinces of the region and evaluated according to morphological descriptors such as stipula shape, young leaf color, leaf shape, leaf apex shape, young shoot color, leaf color, fruit color, fruit shape, mature leaf color, and rust incidence percentage. In the plots where the parent plants were located, soil sampling was carried out to determine the physical and chemical properties. The varieties with the greatest presence in the five provinces were Típica and caturra roja, with the greatest number of specimens reported for the province of Bagua. The predominant stipule shape was triangular (91%), lanceolate leaf shape (60%) and red fruit color (90%). Bongará reported the lowest incidence of yellow rust, as well as the Mundo Novo Rojo variety. Soil pH ranged from acidic to neutral values, low electrical conductivity, high organic matter content, low phosphorus content, high potassium levels and medium cation exchange capacity. The predominant textural class was sandy loam. The physical and chemical characterization of the soils under study show favorable ranges to encourage the best development of coffee cultivation.

Grassland ecosystems cover about 25% of the Earth’s surface, providing essential ecosystem services that benefit nature, people, and food security. This study evaluated agronomic and nutritional parameters of ryegrass (Lolium multiflorum Lam.) based on fertilization levels and cutting frequency in the Amazonas department. The INIA 910—Kumymarca variety was used with nitrogen fertilization rates (0, 60, 120, 180 kg/ha) and cutting intervals of 30 and 45 days for agronomic traits and 30, 45, and 60 days for nutritional traits. A randomized complete block design with eight treatments and three replications was applied. Repeated measures analysis and Tukey’s mean comparison tests (p < 0.005) were performed, along with Pearson correlation and response surface analysis using the central composite design in R. The results showed that applying 180 kg/ha of nitrogen with a 45-day cutting interval provided the highest dry matter yield (460 kg/m²) and superior agronomic traits, including plant height (96.73 cm), number of tillers, and stem diameter. Non-fertilized treatments had the highest crude protein content (17.45%) and digestibility, while higher nitrogen doses increased crude fiber and acid detergent fiber, reducing digestibility. Significant correlations were observed between fresh and dry weight with plant height (p = 0.000; r = 0.84), fiber contents (p = 0.000; r = 1), and ash and protein content (p = 0.000; r = 0.85). The optimal management practice was cutting every 45 days with 180 kg/ha of nitrogen (T8), maximizing forage yield and quality. Proper fertilization and cutting management can improve ryegrass production, benefiting livestock feeding and rural economies.

White clover (Trifolium repens L.) is known for its ability to fix nitrogen biologically, its high nutritional value, and its adaptability to livestock systems. However, excessive fertilization with synthetic nitrogen alters its symbiosis with Rhizobium and reduces the protein content of the forage. The objective of this study was to evaluate the interaction between nitrogen fertilization (0 and 60 kg N ha⁻¹), cutting time, and post-cutting evaluation on the morphology, yield, and nutritional composition of white clover. A completely randomized block experimental design with three factors, distributed in three blocks, was used. Within each block, three replicates of each treatment were assigned (six interactions), giving a total of 54 experimental units. The data were analyzed using a three-way analysis of variance and Tukey’s multiple comparison test. Exponential models and generalized additive models (GAMs) were applied to the morphology and yield data to identify the best fit. The treatment with 60 kg N ha⁻¹ and cutting at 30 days showed significant increases in plant height (47.42%), fresh weight (59.61%), dry weight (98.41%), and leaf width (27.55%) compared to the control. It also produced the highest protein content (28.44%) compared to the other treatments with fertilization, without negatively affecting digestibility. The GAMs best fit most morphological and yield parameters (except leaf height and width). All fertilized treatments had higher fresh and dry weight yields. In conclusion, applying 60 kg N ha⁻¹ after cutting at 30 days, followed by harvesting between 54 and 60 days, improved both the quality and yield of white clover, which favored sustainable pasture management and reduced excessive nitrogen use.