Monitoring of large agricultural lands is often hampered by data collection logistics at field level. To solve such a problem, remote sensing techniques have been used to estimate vegetation indices, which can subsidize crop management decision-making. Therefore, this study aimed to select vegetation indices to detect variability in irrigated corn crops. Data were collected in São Desidério, Bahia State (Brazil), using an OLI sensor (Operational Land Imager) embedded to a Landsat-8 satellite platform. Five corn growing plots under central pivot irrigation were assessed. The following vegetation indices were tested: NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), SAVI (Soil Adjusted Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SR (Simple Ratio), NDWI (Normalized Difference Water Index), and MSI (Moisture Stress Index). Among the tested indices, SR was more sensitive to high corn biomass, while GNDVI, NDVI, EVI, and SAVI were more sensitive to low values. Overall, all indices were found to be concordant with each other, with high correlations among them. Despite this, the use of a set of these indices is advisable since some respond better to certain peculiarities than others.

The correct irrigation frequency relies on the plant population density to increase yield and efficiency in water use. A field experiment was carried out in 2019 to study the effect of three irrigation frequencies, one (F1), four (F2), and eight (F3) days, and four planting densities, 20 (D1), 24 (D2), 28 (D3), and 30 (D4) plants per m², on the variables: soil moisture, actual water consumption, growth, grain yield, and water productivity of beans (Phaseolus vulgaris L.). The soil water balance was calculated based on the soil moisture, which was measured by tensiometers. The irrigation was done by a drip tape. The variation in soil moisture was greatly influenced by irrigation frequencies and planting densities in F3. Plant growth was not affected by treatments. Irrigation frequencies and planting densities have deeply affected the biometric and chlorophyll synthesis traits of beans. The highest yields were found in the treatments F1D3 (2968 kg ha⁻¹), F1D4 (2997 kg ha⁻¹), and F3D3 (2946 kg ha⁻¹). For water productivity, treatments with F1 irrigation frequency and the higher planting densities were more efficient in water use. Overall, the results are useful for technicians and farmers who work with irrigation management on beans.

To expand financial knowledge and promote the socioeconomic sustainability of agricultural production systems, this work applied financial instruments to small-scale irrigated crops, making use of agricultural projects from the Bom Sucesso/Santa Cruz Settlement, located in the Tocantins-Araguaia basin, Brazil. The economic viability indicators of two irrigated okra production systems (furrow and drip) were calculated using the following indicators: Net Present Value, Modified Internal Rate of Return, Cost–Benefit Index, Discounted Payback, and Average Cost of Production. And a risk assessment through sensitivity analysis, a tornado chart, and Monte Carlo simulation was performed in a simplified financial model. The economic viability results showed that both irrigated systems were economically viable, although the okra drip system had lower indicators and reduced financial resilience, with a 58% probability of success, while the furrow system’s was 89%. The crops’ financial return was mainly influenced by okra’s sale price and productivity, followed by harvesting and planting costs. The financial tools applied in this study demonstrated some key factors to assess socioeconomic sustainability, such as the necessity to increase producers’ organizational capacity and to achieve technical improvements, which could result in market power, competitive sale prices, lower input costs, and greater productivity. The applied instruments increased financial management and have the potential to the build economic and social strength in the area of small-scale irrigated crops, and they can be replicated using simplified tools for producers, companies, and policy makers.

Brazil is the world’s largest soybean exporter. Over half of the cultivated area is in the Brazilian Savanna (Cerrado) region, where soybean is typically grown under rainfed conditions. However, soybeans have been cultivated in irrigated systems, further increasing water demand in the region, which already faces water problems. Therefore, it is crucial to generate technical information to support the management of water resources in irrigated soybean crops. This study aimed to estimate the water deficit sensitivity index of soybean crops. Two field experiments were carried out in randomized blocks with four treatments and four replicates from May to Aug 2019 and 2021. Three treatments with irrigation suspension were applied in three different phenological stages: vegetative, reproductive, and seed filling, and a fully irrigated control plot to meet the total plant demand. The results showed 40 % and 34 % reductions in soybean seed yield when water deficit was applied in stages R1-R5 and R5-R7, respectively, compared to the control treatment. There was a linear correlation with r² values equal to 0.98, 0.97, and 0.99 for the relationships between seed yield and applied irrigation, seed yield and actual evapotranspiration (ET_a), and ET_a and irrigation, respectively. The yield response factors were equal to 0.12, 0.36, and 0.57 for the stages V2-R1, R1-R5, and R5-R7, respectively. The yield response factor for the soybean cycle was equal to 1.16.

The Penman-Monteith method (PM-FAO) is recommended as standard for calculation of reference evapotranspiration (ETo). However, its use requires a series of meteorological variables that is not normally available, restricting its application in many locations. A solution to the problem of unavailability of meteorological data was presented in FAO Bulletin 56, which contains methodologies for estimating wind speed, solar radiation and relative humidity. The objective of the present study was to evaluate the performance of the PM-FAO methodologies for missing data and Hargreaves-Samani as alternatives to the PM-FAO standard method at different time scales and seasons for the municipalities of Linhares and São Mateus, located in the northern region of the state of Espírito Santo. The comparison was performed using linear regression parameters (β₀ and β₁), coefficient of determination, standard error of estimation (SEE) and coefficient of performance. The best alternative to the standard PM-FAO standard method for estimating ETo in the studied area was the Penman-Monteith method with missing wind speed data, since the R² for this method always remained above 0.94 and the confidence coefficient was classified as great, for all seasons and scales. The Hargreaves-Samani method did not present satisfactory performance, with R² below 0.7, regardless of the time scale and time of the year, and it yielded the greatest SEE (1.0 mm d^-1) at spring on a two-day scale. Thus, its use in the northern region of the Espírito Santo state is not recommended.

Remote sensing as a tool to determine biophysical parameters of irrigated seed corn crop