Awareness of evapotranspiration (ET) and crop coefficient (K_c) is necessary for irrigation management in coffee crops. ET and K_c spatial variabilities are disregarded in traditional methods. Methods based on radiometric measurements have potential to obtain these spatialized variables. The K_c curve and spatial variability of actual evapotranspiration (ET_a) were determined using images from Landsat 8 satellite. We used images of young and adult coffee plantations from OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) sensors over a two-year period. Evapotranspiration was estimated using the Surface Energy Balance Algorithm for Land (SEBAL). Moreover, the reference evapotranspiration (ET_o) was estimated through the Penman-Monteith method. We obtained the values for the evapotranspiration fraction (ET_f), analogous to K_c, according to ET and ET_o values. The study was conducted in Buritis, Minas Gerais State, Brazil, in areas cropped with Coffea arabica irrigated by central pivots. A comparative analysis was made using different statistical indices. Average ET_a was 2.17 mm d⁻¹ for young coffee plantations, , and the K_c mean value was 0.6. For adult coffee plantations, average ET_a was 3.95 mm d⁻¹, , and the K mean value was 0.85. The ET and K data obtained based on the SEBAL algorithm displayed similar values to studies that used traditional methods. This model has huge potential to estimate ET of different stages of coffee plantation for the region studied.

Coffee is a global commodity, with Brazil being a major producer, particularly in the Minas Gerais state. This study applied machine learning to predict the Arabica coffee yield in the region, analyzing two groups of cultivars (G1 and G2) using data from 1993 to 2020. The Factor Analysis of Mixed Data (FAMD) was employed to explore the relationships between climatic factors, management practices, and the coffee yield. Four machine learning models, such as Multiple Linear Regression (MLR), Random Forest (RF), XGBoost (XGB), and Support Vector Machines (SVM) were calibrated and evaluated for yield prediction. The FAMD revealed complex interactions among variables, requiring four principal components to explain approximately 64.6% of the total variance. Management practices, such as the planting density and pruning, had a stronger influence on G1 cultivars, while G2 cultivars were more sensitive to climatic conditions, particularly the air temperature. Among the machine learning models, RF and XGB performed best in the yield estimation, whereas MLR and SVM were less effective, particularly for values above 60 bags ha⁻¹ (1 bag = 60 kg). These findings underscore the variability in the yield across cultivars and demonstrate the potential of machine learning to guide tailored management strategies for different coffee cultivars.

The leaf area index (LAI) is relevant in studies of phenomena at different scales, such as for the leaf to canopy scale and the calculation of the extinction coefficient of photosynthetically active radiation (kPAR), providing input for the parameterization of physiological basis models. The objective of this work was to verify the variation of the LAI and the coffee kPAR subjected to different drip irrigation levels (130, 100, 70, and 40%) and to compare the data obtained from radiation bar linear sensors (SunScan) in the plants that received full irrigation with the values found by other LAI estimation methodologies. The study was conducted in Piracicaba, São Paulo State, Brazil, using the species Coffea arabica cv. Red Catuaí IAC 144; a drip irrigation system was adopted, with the irrigation controlled by tensiometry. The mean LAI values were higher in the L130 (irrigation level of 130%) and L100 (irrigation level of 100%) treatments than those with deficit irrigation depths. The mean kPAR values were lower for the L130 and L100 treatments than the values found in the deficit irrigation depth treatments. When comparing SunScan to other methodologies, the mean error (ME) and absolute mean error (AME) were high.

Coffee genetic improvement programs have been evolving very quickly, with frequent launches of new cultivars. The adoption of these new genetic materials by rural producers requires knowledge of agronomic performance in different production systems. Thus, this research aimed to evaluate the agronomic performance of irrigated and rainfed Arabica coffee (Coffea arabica L.) cultivars in the Cerrado Mineiro region. Evaluations were conducted in experimental fields across 22 farms of Arabica coffee producers, and 11 used an irrigated production system and 11 used a rainfed system. Twelve cultivars were evaluated as follows: Catuaí Vermelho IAC 144, Bourbon Amarelo IAC J10, Topázio MG 1190, MGS Epamig 1194, Catiguá MG2, MGS Catiguá 3, MGS Ametista, Pau Brasil MG1, MGS Paraíso 2, MGS Aranãs, Sarchimor MG 8840, and IAC 125 RN. Based on grain yield, processing yield, seed density, grain size, and cup quality, agronomic performance evaluations were conducted annually for the 2019, 2020, 2021, and 2022 harvests. The results showed that the grain yield was higher in the irrigated system compared to the rainfed system. In irrigated fields, the average increases in grain yield were 38%. Irrigation improved the performance of the cultivars in terms of processing yield, although it reduced cup quality. MGS Paraíso 2 cultivar showed the best productive performance, with an average of over four harvests of 52 and 42 bags ha⁻¹ (1 bag = 60 kg) in irrigated and rainfed systems, respectively. The cultivars least responsive to irrigation were IAC 125 RN, MGS Catiguá 3, MGS Ametista, and MGS Paraíso 2, with grain yield increases of 24%, 26%, 27%, and 28%, respectively. The most responsive cultivars were MGS EPAMIG 1194, Sarchimor MG 8840, and Pau Brasil MG1, with grain yield increases of 33%, 35%, and 39%, respectively. The agronomic performance results of coffee cultivars in irrigated and rainfed production systems will allow rural producers to adopt cultivars that are more suitable for the Cerrado Mineiro region.

The tomato (Solanum lycopersicum) is the second most produced vegetable globally, playing a significant role in national and international economies. This crop is highly sensitive to water deficit and thermal stress, which directly affect yield and fruit quality. Foliar application of calcium carbonate (CaCO₃) may be a possible strategy to minimize the effects of these abiotic stresses. This research aimed to determine: (a) the effects of different water replacement levels (WRLs) and photoprotector strategies (Ps) applied to the canopy on production and harvest quality of tomato fruit, (b) thermal responses—Crop Water Stress Index (CWSI) and soil temperature and (c) crop water productivity (WP_c). The research was conducted at the University of São Paulo (USP/ESALQ), Piracicaba, State of São Paulo, Brazil. The experimental design adopted was randomized blocks, with four blocks and nine treatments, totaling 36 plots. The treatments were arranged in a 3 × 3 factorial scheme, with three WRLs (70, 100 and 130% of the required irrigation depth) and three photoprotector strategies (without photoprotector, with photoprotector and with photoprotector + adjuvant). Biometric and thermal responses, productivity, harvest quality and WP_c were determined. The highest plant height and stalk diameter values were found in the treatment with photoprotector and adjuvant, with an average of 0.98 m and 0.0130 m, respectively. For the variables soil temperature, CWSI and tomato productivity, no significant differences were observed. The general average productivity obtained was 77.9 Mg ha⁻¹. The highest WP_c values were found in the WRL 70 treatments, with an average of 23.6 kg m⁻³. No significant differences were observed for pulp firmness. The highest average value of soluble solids was observed in the treatments with photoprotector (4.8 °Brix) and the highest average value of titratable acidity was observed in the WRL 130 treatments (0.36%). Therefore, deficit irrigation resulted in water savings without compromising tomato productivity and the application of photoprotector and adjuvant increased tomato quality.