Crop water parameters, including actual evapotranspiration, transpiration, soil evaporation, crop coefficients, evaporative fractions, aerodynamic resistances, surface resistances and percolation fluxes were estimated in a commercial mango orchard during two growing seasons in Northeast Brazil. The actual evapotranspiration (E_a) was obtained by the eddy covariance (EC) technique, while for the reference evapotranspiration (E₀); the FAO Penman–Monteith equation was applied. The energy balance closure showed a gap of 12%. For water productivity analysis the E_a was then computed with the Bowen ratio determined from the eddy covariance fluxes. The mean accumulated E_a for the two seasons was 1419 mm year⁻¹, which corresponded to a daily average rate of 3.7 mm day⁻¹. The mean values of the crop coefficients based on evapotranspiration (K_c) and based on transpiration (K_cb) were 0.91 and 0.73, respectively. The single layer K_c was fitted with a degree days function. Twenty percent of evapotranspiration originated from direct soil evaporation. The evaporative fraction was 0.83 on average. The average relative water supply was 1.1, revealing that, in general, irrigation water supply was in good harmony with the crop water requirements. The resulting evapotranspiration deficit was 73–95 mm per season only. The mean aerodynamic resistance (r_a) was 37 s m⁻¹ and the bulk surface resistance (r_s) was 135 s m⁻¹. The mean unit yield was 45 tonne ha⁻¹ being equivalent to a crop water productivity of 3.2 kg m⁻³ when based on E_a with an economic counterpart of US$ 3.27 m⁻³. The drawback of this highly productive use of water resources is an unavoidable percolation flux of approximately 300 mm per growing season that is detrimental to the downstream environment and water users.

Many remote sensing‐based evapotranspiration (RSBET) algorithms have been proposed in the past decades and evaluated using flux tower data, mainly over North America and Europe. Model evaluation across South America has been done locally or using only a single algorithm at a time. Here, we provide the first evaluation of multiple RSBET models, at a daily scale, across a wide variety of biomes, climate zones, and land uses in South America. We used meteorological data from 25 flux towers to force four RSBET models: Priestley–Taylor Jet Propulsion Laboratory (PT‐JPL), Global Land Evaporation Amsterdam Model (GLEAM), Penman–Monteith Mu model (PM‐MOD), and Penman–Monteith Nagler model (PM‐VI). was predicted satisfactorily by all four models, with correlations consistently higher () for GLEAM and PT‐JPL, and PM‐MOD and PM‐VI presenting overall better responses in terms of percent bias (%). As for PM‐VI, this outcome is expected, given that the model requires calibration with local data. Model skill seems to be unrelated to land‐use but instead presented some dependency on biome and climate, with the models producing the best results for wet to moderately wet environments. Our findings show the suitability of individual models for a number of combinations of land cover types, biomes, and climates. At the same time, no model outperformed the others for all conditions, which emphasizes the need for adapting individual algorithms to take into account intrinsic characteristics of climates and ecosystems in South America.

The Caatinga is the largest nucleus of Seasonally Dry Tropical Forests (SDTF) in the Neotropics. The leafing patterns of SDTF vegetation are adapted to the current environmental and climate variability, but the impacts of climate change tend to alter plants’ phenology. Thus, it is necessary to characterise phenological parameters and evaluate the relationship between vegetation and environmental drivers. From this information, it is possible to identify the dominant forces in the environment that trigger the phenological dynamics of the Caatinga. In this way, remote sensing represents an essential tool to investigate the phenology of vegetation, particularly as it has a long series of vegetation monitoring and allows relationships with different environmental drivers. This study has two objectives: (i) estimate phenological parameters using an Enhanced Vegetation Index (EVI) time-series over 20 years, and (ii) characterise the relationship between phenologic dynamics and environmental drivers. TIMESAT software was used to determine four phenological parameters: Start Of Season (SOS), End Of Season (EOS), Length Of Season (LOS), and Amplitude (AMPL). Boxplots, Pearson’s, and partial correlation coefficients defined relationships between phenologic dynamics and environmental drivers. The non-parametric test of Fligner–Killeen was used to test the interannual variability in SOS and EOS. Our results show that the seasonality of vegetation growth in the Caatinga was different in the three experimental sites. The SOS was the parameter that presented the greatest variability in the days of the year (DOY), reaching a variation of 117 days. The sites with the highest SOS variability are the same ones that showed the lowest EOS variation. In addition, the values of LOS and AMPL are directly linked to the annual distribution of rainfall, and the longer the rainy season, the greater their values are. The variability of the natural cycles of the environmental drivers that regulate the ecosystem’s phenology and the influence on the Caatinga’s natural dynamics indicated a greater sensitivity of the phenologic dynamics to water availability, with precipitation being the limiting factor of the phenologic dynamics. Highlights: The EVI time series was efficient in estimating phenological parameters. The high variability of the start of season (SOS) occurred in sites with low variability of end of the season (EOS) and vice versa. The precipitation and water deficit presented a higher correlation coefficient with phenological dynamics. Length of Season (LOS) and amplitude (AMPL) are directly linked to the annual distribution of rainfall.

Investigating the timing of key phenological events across environments with variable seasonality is crucial to understand the drivers of ecosystem dynamics. Leaf production in the tropics is mainly constrained by water and light availability. Identifying the factors regulating leaf phenology patterns allows efficiently forecasting of climate change impacts. We conducted a novel phenological monitoring study across four Neotropical vegetation sites using leaf phenology time series obtained from digital repeated photographs (phenocameras). Seasonality differed among sites, from very seasonally dry climate in the caatinga dry scrubland with an eight-month long dry season to the less restrictive Cerrado vegetation with a six-month dry season. To unravel the main drivers of leaf phenology and understand how they influence seasonal dynamics (represented by the green color channel (Gcc) vegetation index), we applied Generalized Additive Mixed Models (GAMMs) to estimate the growing seasons, using water deficit and day length as covariates. Our results indicated that plant-water relationships are more important in the caatinga, while light (measured as day-length) was more relevant in explaining leafing patterns in Cerrado communities. Leafing behaviors and predictor-response relationships (distinct smooth functions) were more variable at the less seasonal Cerrado sites, suggesting that different life-forms (grasses, herbs, shrubs, and trees) are capable of overcoming drought through specific phenological strategies and associated functional traits, such as deep root systems in trees.

The objective of this work was to evaluate the productive performance, biological efficiency, and the competitive ability of the forage cactus-sorghum intercropping under different irrigation depths with saline water in the Brazilian Semiarid Region. The experiment was carried out in Serra Talhada, in the state of Pernambuco, between 2014 and 2015, in a randomized complete block design, with four replicates, and a 5x3 factorial arrangement in split plots, with one year of forage cactus cycle and two years of sorghum cycles. The plots were composed by four irrigation water depths based on the fractions of 25, 50, 75, and 100% of the reference evapotranspiration, besides dry conditions; and the subplots consisted of three cropping systems (single crop of forage cactus, single crop of sorghum, and intercropping of forage cactus-sorghum). The increase of water irrigation depths increased the yields of sorghum in single crop and of the forage cactus-sorghum intercropping. The intercropping system promoted production stability with a higher system productivity index (SPI) for forage cactus-sorghum intercropping (6,279.02 kg ha^-1 DM) than for forage cactus in single crop (4,626.98 kg ha^-1 DM). The relative density coefficient (12.33) was indicative of the great compatibility of the intercropping system. The forage cactus-sorghum association shows biological advantage and competitiveness capacity, with a higher productive index than their monocultures.