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.

FAO Penman-Monteith (FO-PM) is considered the standard method for the estimation of reference evapotranspiration (ET₀) but requires various meteorological data, which are often not available. The objective of this work was to evaluate the performance of the FAO-PM method with limited meteorological data and other methods as alternatives to estimate ET₀ in Jaíba-MG. The study used daily meteorological data from 2007 to 2016 of the National Institute of Meteorology’s station. Daily ET₀ values were randomized, and 70% of these were used to determine the calibration parameters of the ET₀ for the equations of each method under study. The remaining data were used to test the calibration against the standard method. Performance evaluation was based on Willmott’s index of agreement, confidence coefficient and root-mean-square error. When one meteorological variable was missing, either solar radiation, relative air humidity or wind speed, or in the simultaneous absence of wind speed and relative air humidity, the FAO-PM method showed the best performances and, therefore, was recommended for Jaíba. The FAO-PM method with two missing variables, one of them being solar radiation, showed intermediate performance. Methods that used only air temperature data are not recommended for the region.

The Penman-Monteith equation is recommended for the estimation of reference evapotranspiration (ETo). However, it requires meteorological data that are commonly unavailable. Thus, this study evaluates artificial neural network (ANN), multivariate adaptive regression splines (MARS), and the original and calibrated Hargreaves-Samani (HS) and Penman-Monteith temperature (PMT) equations for the estimation of daily ETo using temperature. Two scenarios were considered: (i) local, models were calibrated/developed and evaluated using data from individual weather stations; (ii) regional, models were calibrated/developed using pooled data from several stations and evaluated independently in each one. Local models were also evaluated outside the calibration/training station. Data from 9 stations were used. The original PMT outperformed the original HS, but after local or regional calibrations, they performed similarly. The locally calibrated equations and the local machine learning models exhibited higher performances than their regional versions. However, the regional models had higher generalization capacity, with a more stable performance between stations. The machine learning models performed better than the equations evaluated. When comparing the ANN models with the HS equation, mean RMSE reduced from 0.96 to 0.87 and from 0.84 to 0.73, in regional and local scenarios, respectively. ANN and MARS performed similarly, with a slight advantage for ANN.

Polyethylene mulch can cause serious problems in the environment. A promising alternative to polyethylene mulch is biodegradable mulch. Therefore, a two-year experiment was carried out with zucchini (Cucurbita pepo L.) during the cropping seasons of 2017 and 2018 in Southeast Brazil to investigate the effects of four soil mulching treatments (white polyethylene mulch: WP, black polyethylene mulch: BP, biodegradable mulch of brown recycled paper: RP and nonmulched: NM) and two wetted soil percentages (50 and 70% of the soil surface) using a randomized block design with five repetitions. The average water consumption of the zucchini was 157 mm in the WP treatment, 155 mm in the BP treatment, 155 mm in the WP treatment and 215 mm in the NM treatment. The yield was higher in the WP (78.1 ton ha-1) and BP (69.1 ton ha-1) treatments than in the RP (66.6 ton ha-1) or NM (52.4 ton ha-1) treatments. The daily thermal amplitude was lowest in the RP treatment (3.6-6.0 °C) and highest in the BP treatment (7.3-9.0 °C). The lower wetted soil percentage (50%) reduced water consumption (by 5-7%) and improved water-use efficiency (by 11%). Weeds, including Cyperus roduntus, were more efficiently controlled in the RP treatment than in the WP and BP treatments. In general, compared to the nonmulched treatment, mulches were effective in reducing weed infestation (95%), increasing yield (36%) and improving water-use efficiency (94%). RP, as an alternative to polyethylene mulch, can save water and control weeds in zucchini crops. These results represent useful information that extends the mulching options for technicians and farmers.