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Assessing the impact of climate change is essential for developing water resource management plans, especially in areas facing severe issues regarding ecosystem service degradation. This study assessed the effects of climate change on the hydrological balance using the SWAT (Soil and Water Assessment Tool) hydrological model in the Mixteco River Basin (MRB), Oaxaca, Mexico. Temperature and precipitation were predicted with the projections of global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6); the bias was corrected using CMhyd software, and then the best performing GCM was selected for use in the SWAT model. According to the GCM MPI-ESM1-2-LR, precipitation might decrease by between 83.71 mm and 225.83 mm, while temperature might increase by between 2.57 °C and 4.77 °C, causing a greater atmospheric evaporation demand that might modify the hydrological balance of the MRB. Water yield might decrease by 47.40% and 61.01% under the climate scenarios SP245 and SSP585, respectively. Therefore, adaptation and mitigation measures are needed to offset the adverse impact of climate change in the MRB. [ABSTRACT FROM AUTHOR]

Objective: To characterize the avocado production system in Huatusco, Veracruz. Design/Methodology/Approach: Interviews based on the snowball method were applied to avocado producers in Huatusco, Veracruz. Meanwhile, fruit and water samples were used to perform a multiresidue analysis of pesticides. Results: The monoculture plantations are rainfed. They have a density of 100-150 trees ha-1. The Hass variety predominates in Andosols, with 5-100% slopes. Most of the producers (92%) carry out conventional management, applying pesticides and chemical fertilizers. Herbicides and brushcutters are used to control weeds. No pesticides were detected in the water samples, while Imidacloprid, Thiabendazole, and alpha-Cypermethrin were identified in a fruit sample, with 0.0038-0.0014 mg/kg, 0.0022-0.0009 mg/kg and 0.0703-0.000 mg/kg concentrations, respectively. These concentrations fall within the limits allowed in Mexico. Study Limitations/Implications: A detailed and frequent sampling of avocado fruits should be carried out to determine pesticide residues. It is also necessary to analyze samples of running water near the orchards to prevent pollution. Findings/Conclusions: The empirical knowledge of the producers about the type of soil is more precise than the scientific classification: they identify five classes of soils beyond Andosols. Additionally, there is a marked inequality in land tenure, since large-scale producers (25% of all producers) own 86% of the established area. Although, in a preliminary sampling, pesticide concentrations did not exceed the limits of residues allowed in the avocado pulp, the number of pesticide applications should be reduced. [ABSTRACT FROM AUTHOR]

The information about where crops are distributed is useful for agri-environmental assessments, but is chiefly important for food security and agricultural policy managers. The quickness with which this information becomes available, especially over large areas, is important for decision makers. Methodologies have been proposed for the study of crops. Most of them require field survey for ground truth data and a single crop map is generated for the whole season at the end of the crop cycle and for the next crop cycle a new field survey is necessary. Here, we present models for recognizing maize (Zea mays L.), beans (Phaseolus vulgaris L.), and alfalfa (Medicago sativa L.) before the crop cycle ends without current-year field survey for ground truth data. The models were trained with an exhaustive field survey at plot level in a previous crop cycle. The field surveys begin since days before the emergence of crops to maturity. The algorithms used for classification were support vector machine (SVM) and bagged tree (BT), and the spectral information captured in the visible, red-edge, near infrared, and shortwave infrared regions bands of Sentinel 2 images was used. The models were validated within the next crop cycle each fifteen days before the mid-season. The overall accuracies range from 71.9% (38 days after the begin of cycle) to 87.5% (81 days after the begin cycle) and a kappa coefficient ranging from 0.53 at the beginning to 0.74 at mid-season [ABSTRACT FROM AUTHOR]

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The excessive use of N in agriculture has created various environmental and economic problems. Remote sensing and unmanned aerial vehicles (UAV) are feasible solutions to infer the status of a crop and enable a better management during the growing season. The objective of this study was to correlate experimental N content and wheat (Triticum aestivum L.) crop aboveground biomass data with vegetation indices estimated using UAV images. In this study, the N nutrition index and N dilution curve were used as indicators of the state of plant N; input variables to estimate these indicators were the N content and aboveground biomass. Four flight campaigns were conducted at different phenological stages of a wheat crop and seven N doses were evaluated. A linear relationship of blue normalized difference vegetation index (BNDVI) and green normalized difference vegetation index (GNDVI) with aboveground biomass and N content was identified. BNDVI and biomass demonstrated high R² during boots swollen and end of anthesis growth stages (0.62 and 0.68, respectively), while GNDVI showed the highest R² during the ear half emerged and beginning of anthesis growth stages (0.84 and 0.79, respectively). For N content estimation, GNDVI showed a higher correlation than BNDVI, and the adjustment curve showed an R² up to 0.81 only for the last flight (end of anthesis), BNDVI showed an R² of 0.78. Remote sensing and vegetation indices estimated from UAV images can be reliably used to estimate N content and wheat biomass, contributing to knowing the crop N status. [ABSTRACT FROM AUTHOR]

Copyright of Ingeniería Agrícola y Biosistemas is the property of Universidad Autonoma Chapingo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditional estimations of evapotranspiration (ET ) are based on the crop coefficient (Kc). This can be disadvantageous when precise estimations of crop water uptake are required in the Irrigation Districts. Using satellite images, it is possible to estimate vegetation indexes (VI ), such as the Normalized Difference Vegetation Index (NDVI ) and the Soil Adjusted Vegetation Index (SAVI ). These indexes enable monitoring crop development and estimating precisely spatial and temporal Kc variability. The objective of this study was to validate the use of VI to estimate Kc and ET of wheat (Triticum aestivum) in the Río Yaqui Irrigation District, Sonora, Mexico. Validation was carried out with eight images from the sensors TM and ETM+ of the LANDSAT 5 and 7 satellites and measurements of turbulent flux with the Eddy Covariance (EC ) technique for the year 2008. ET estimated from VI and measurement with EC showed a high degree of correspondence. For the eight images, the square root of the Root Mean Square Error (RMSE) was 0.69 mm d-1, the Mean Absolute Error (MAE) 0.62 mm d-1, the index of agreement (d) 0.91 for NDVI and RMSE of 0.64 mm d-1, MAE of 0.57 and an index of agreement of 0.92 for SAVI in daily estimations. It is thus concluded that VI allow to estimate spatial and temporal variability of the Kc and ET with precision in extensive agricultural regions. [ABSTRACT FROM AUTHOR]

Corn yields vary spatially and temporally in the plots as a result of weather, altitude, variety, plant density, available water, nutrients, and planting date; these are the main factors that influence crop yield. In this study, different multispectral and red-green-blue (RGB) vegetation indices were analyzed, as well as the digitally estimated canopy cover and plant density, in order to estimate corn grain yield using a neural network model. The relative importance of the predictor variables was also analyzed. An experiment was established with five levels of nitrogen fertilization (140, 200, 260, 320, and 380 kg/ha) and four replicates, in a completely randomized block design, resulting in 20 experimental polygons. Crop information was captured using two sensors (Parrot Sequoia_4.9, and DJI FC6310_8.8) mounted on an unmanned aerial vehicle (UAV) for two flight dates at 47 and 79 days after sowing (DAS). The correlation coefficient between the plant density, obtained through the digital count of corn plants, and the corn grain yield was 0.94; this variable was the one with the highest relative importance in the yield estimation according to Garson's algorithm. The canopy cover, digitally estimated, showed a correlation coefficient of 0.77 with respect to the corn grain yield, while the relative importance of this variable in the yield estimation was 0.080 and 0.093 for 47 and 79 DAS, respectively. The wide dynamic range vegetation index (WDRVI), plant density, and canopy cover showed the highest correlation coefficient and the smallest errors (R = 0.99, mean absolute error (MAE) = 0.028 t ha−1, root mean square error (RMSE) = 0.125 t ha−1) in the corn grain yield estimation at 47 DAS, with the WDRVI index and the density being the variables with the highest relative importance for this crop development date. For the 79 DAS flight, the combination of the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), WDRVI, excess green (EXG), triangular greenness index (TGI), and visible atmospherically resistant index (VARI), as well as plant density and canopy cover, generated the highest correlation coefficient and the smallest errors (R = 0.97, MAE = 0.249 t ha−1, RMSE = 0.425 t ha−1) in the corn grain yield estimation, where the density and the NDVI were the variables with the highest relative importance, with values of 0.295 and 0.184, respectively. However, the WDRVI, plant density, and canopy cover estimated the corn grain yield with acceptable precision (R = 0.96, MAE = 0.209 t ha−1, RMSE = 0.449 t ha−1). The generated neural network models provided a high correlation coefficient between the estimated and the observed corn grain yield, and also showed acceptable errors in the yield estimation. The spectral information registered through remote sensors mounted on unmanned aerial vehicles and its processing in vegetation indices, canopy cover, and plant density allowed the characterization and estimation of corn grain yield. Such information is very useful for decision-making and agricultural activities planning. [ABSTRACT FROM AUTHOR]

The number of plants, or planting density, is a key factor in corn crop yield. The objective of the present research work was to count corn plants using images obtained by sensors mounted on an unmanned aerial vehicle (UAV). An experiment was set up with five levels of nitrogen fertilization (140, 200, 260, 320 and 380 kg/ha) and four replicates, resulting in 20 experimental plots. The images were taken at 23, 44 and 65 days after sowing (DAS) at a flight altitude of 30 m, using two drones equipped with RGB sensors of 12, 16 and 20 megapixels (Canon PowerShot S100_5.2, Sequoia_4.9, DJI FC6310_8.8). Counting was done through normalized cross-correlation (NCC) for four, eight and twelve plant samples or templates in the a* channel of the CIELAB color space because it represented the green color that allowed plant segmentation. A mean precision of 99% was obtained for a pixel size of 0.49 cm, with a mean error of 2.2% and a determination coefficient of 0.90 at 44 DAS. Precision values above 91% were obtained at 23 and 44 DAS, with a mean error between plants counted digitally and visually of ±5.4%. Increasing the number of samples or templates in the correlation estimation improved the counting precision. Good precision was achieved in the first growth stages of the crop when the plants do not overlap and there are no weeds. Using sensors and unmanned aerial vehicles, it is possible to determine the emergence of seedlings in the field and more precisely evaluate planting density, having more accurate information for better management of corn fields. [ABSTRACT FROM AUTHOR]

Some techniques, such as the Katerji and Perrier approach, estimate the bulk canopy resistance (rc) as a function of meteorological variables and then calculate the hourly evapotranspiration using the Penman–Monteith equation, so that traditional crop coefficients are not needed. As far as we know, there are no published studies regarding using this method for a maize crop. The objective of this study was to calibrate and validate the canopy resistance for an irrigated continuous maize crop in the Midwestern United States (US). In addition, we determined the effect of derivation year, bowen ratio, and the extent of canopy. In this study we derive empirical coefficients necessary to estimate rc for maize, five years (2001–2005) were considered. A split-sample approach was taken, in which each year's data was taken as a potential calibration data set and validation was accomplished while using the other four years of data. We grouped the data by green leaf area index (GLAI) and the Bowen ratio (β) by parsing the data into a 3 × 3 grouping: LAI (≥2, ≥3, and ≥4) and |β| (≤0.1, ≤0.2, and ≤0.3). The best fit data indicated reasonably good results for all nine groupings, so that the calibration coefficients derived for the conditions LAI ≥ 2 and |β| ≤ 0.3 were taken in light of the longer span associated with LAI ≥ 2 and the larger number of hours. For the calibrations in this subgroup, the results indicate that the annual empirical coefficients for rc are nearly the same and equally effective, regardless of the year used for calibration. Our validation included all the daytime hours regardless of β. Thus, it was concluded that the calibration at our site was independent of the derivation year. Knowledge of the Bowen ratio was useful in calibration, but accurate ET estimates (validation) can be obtained without knowledge of the Bowen ratio. Validation resulted in hourly ET estimates for irrigated maize that explained 83% to 86% of the variation in measured ET with an accuracy of ± 0.2 mm. [ABSTRACT FROM AUTHOR]