Estimating biomass in grasslands through traditional methods and the use of drones in the State of Chihuahua, Mexico

Objective: To evaluate three biomass estimation methods (Unmanned Aerial Vehicle (UAV or drone), ceptometer, and canopy height), comparing them to the quadrant method in an arborescent tufted grassland in the state of Chihuahua.Methodology: The study was conducted in Teseachi, Namiquipa, in october 2020. We located thirty random points. The first biomass estimation method used was UAV. Once the drone flights were completed, the quadrant was placed and the coordinates were determined. We carried out nine readings using a ceptometer and obtained an average. Subsequently, we measured the average canopy height. Finally, all forage within the quadrant was cut at ground level and packed for laboratory analysis. The Agisoft Metashape software was used to process the SfM of the aerial images, using nine sampling points, applying the NGBDI vegetation index, and calculating the averagepixels of a 3 x 3 m moving window. A simple linear regression model was used to analyze the data with the R Project software, version 4.0.3.Results: The simple linear regression model showed an R 2 of 0.62 (p<0.01), 0.55 (p<;0.001), and 0.48 (p<0.001), for UAV, ceptometer, and canopy height, respectively.Study Limitations: There were no limitations for this report. Conclusions: Data obtained with UAVs can generate predictive biomass maps with acceptable accuracy levels. The ceptometer leaf area index is a reliable method to estimate forage yield. However, using the canopy height method is not advisable to estimate forage yield, since its correlation is weak

Can canopy height of mixed pastures in integrated crop-livestock systems be estimated using planetscope imagery?

ABSTRACT. Canopy height (CH) is one of the key parameters used to evaluate forage biomass production and support grazing management decisions in intensively managed fields. In this study, we demonstrate the potential of using textural information derived from PlanetScope (PS) imagery to estimate CH of intensively managed mixed pastures in an Integrated Crop-Livestock Systems (ICLS) in the western region of São Paulo State, Brazil. PS images and field data of CH were acquired during the forage growing season of 2019 (from May to November) to calibrate and validate the CH prediction models using the Random Forest (RF) regression algorithm. We used as predictor variables eight second-order texture measures derived from the green, red, near-infrared spectral bands of PS images using the grey level co-occurrence matrix (GLCM) statistical texture approach. Pasture CH varied from 0.12 to 1.20 m with a coefficient of variation equal to 63.34%. Our best RF model was able to predict the spatiotemporal changes in pasture CH with high accuracy (R2 = 0.88) even with the high variability of the pasture CH through the forage growing season, mainly due to forage composition (different proportions of millet and ruzi grass) and grazing activities.WCCLF 2021. Evento online

Estimation of Dry Matter and N Nutrient Status of Choy Sum by Analyzing Canopy Images and Plant Height Information

The estimation accuracy of plant dry matter by spectra- or remote sensing-based methods tends to decline when canopy coverage approaches closure; this is known as the saturation problem. This study aimed to enhance the estimation accuracy of plant dry matter and subsequently use the critical nitrogen dilution curve (CNDC) to diagnose N in Choy Sum by analyzing the combined information of canopy imaging and plant height. A three-year experiment with different N levels (0, 25, 50, 100, 150, and 200 kg center dot ha(-1)) was conducted on Choy Sum. Variables of canopy coverage (CC) and plant height were used to build the dry matter and N estimation model. The results showed that the yields of N-0 and N-25 were significantly lower than those of high-N treatments (N-50, N-100, N-150, and N-200) for all three years. The variables of CC x Height had a significant linear relationship with dry matter, with R-2 values above 0.87. The good performance of the CC x Height-based model implied that the saturation problem of dry matter prediction was well-addressed. By contrast, the relationship between dry matter and CC was best fitted by an exponential function. CNDC models built based on CC x Height information could satisfactorily differentiate groups of N deficiency and N abundance treatments, implying their feasibility in diagnosing N status. N application rates of 50-100 kgN/ha are recommended as optimal for a good yield of Choy Sum production in the study region

Prediction of aboveground biomass and dry-matter content in brachiaria pastures by combining meteorological data and satellite imagery.

Aboveground biomass (AGB) data are important for profitable and sustainable pasture management. In this study, we hypothesized that vegetation indexes (VIs) obtained through analysis of moderate spatial resolution satellite data (Landsat-8 and Sentinel-2) and meteorological data can accurately predict the AGB of Brachiaria (syn. Urochloa) pastures in Brazil. We used AGB field data obtained from pastures between 2015 and 2019 in four distinct regions of Brazil to evaluate (i) the relationship between three different VIs?normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2) and optimized soil adjusted vegetation index (OSAVI)?and meteorological data with pasture aboveground fresh biomass (AFB), aboveground dry biomass (ADB) and dry-matter content (DMC); and (ii) the performance of simple linear regression (SLR), multiple linear regression (MLR) and random forest (RF) algorithms for the prediction of pasture AGB based on VIs obtained through satellite imagery combined with meteorological data. The results highlight a strong correlation (r) between VIs and AGB, particularly NDVI (r = 0.52 to 0.84). The MLR and RF algorithms demonstrated high potential to predict AFB (R2 = 0.76 to 0.85) and DMC (R2 = 0.78 to 0.85). We conclude that both MLR and RF algorithms improved the biomass prediction accuracy using satellite imagery combined with meteorological data to determine AFB and DMC, and can be used for Brachiaria (syn. Urochloa) AGB prediction. Additional research on tropical grasses is needed to evaluate different VIs to improve the accuracy of ADB prediction, thereby supporting pasture management in Brazil

Estimating Pasture Biomass and Canopy Height in Brazilian Savanna Using UAV Photogrammetry

The Brazilian territory contains approximately 160 million hectares of pastures, and it is necessary to develop techniques to automate their management and increase their production. This technical note has two objectives: First, to estimate the canopy height using unmanned aerial vehicle (UAV) photogrammetry; second, to propose an equation for the estimation of biomass of Brazilian savanna (Cerrado) pastures based on UAV canopy height. Four experimental units of Panicum maximum cv. BRS Tamani were evaluated. Herbage mass sampling, height measurements, and UAV image collection were simultaneously performed. The UAVs were flown at a height of 50 m, and images were generated with a mean ground sample distance (GSD) of approximately 1.55 cm. The forage canopy height estimated by UAVs was calculated as the difference between the digital surface model (DSM) and the digital terrain model (DTM). The R2 between ruler height and UAV height was 0.80; between biomass (kg ha−1 GB—green biomass) and ruler height, 0.81; and between biomass (kg ha−1 GB) and UAV height, 0.74. UAV photogrammetry proved to be a potential technique to estimate height and biomass in Brazilian Panicum maximum cv. BRS Tamani pastures located in the endangered Brazilian savanna (Cerrado) biome

Aplicación de técnicas geomáticas en el Smart Farming: Monitorización de cultivos, determinación de biomasa y detección de enfermedades

En la presente tesis se pretende implementar las metodologías empleadas actualmente en agricultura de precisión, en el campo de la teledetección mediante sensores aéreos no tripulados (UAV) para ayudar a los procesos de desarrollo rural en zonas que se encuentran en vías de desarrollo. Estas metodologías comprenderán temáticas relacionadas con el análisis de crecimiento en cuanto a generación de biomasa en cultivos de Ryegrass y Kikuyo, análisis del estado de salud de plantaciones de lupino mediante su respuesta espectral y la detección de enfermedades en palma africana. Como resultados, se pudo detectar de forma temprana la afectación de Pudrición de Cogollo (PC) en palma africana, determinar que tratamiento de desinfección de semilla para lupino es el más adecuado en el control del desarrollo de la Antracnosis y evidenciar que el cultivo de Ryegrass Perenne es el que genera mejores rendimientos en la producción de biomasa