Predicting pasture biomass using a statistical model and machine learning algorithm implemented with remotely sensed imagery

Abstract Accurate daily estimates of pasture biomass can improve the profitability of pasture-based dairy system by optimising input of feed supplements and pasture utilisation. However, obtaining accurate pasture mass estimates is a laborious and time-consuming task. The aim of this study was to test the performance of an integrated method combining remote sensing imagery acquired with a multispectral camera mounted on an unmanned aerial vehicle (UAV), statistical models (generalised additive model, GAM) and machine learning algorithms (random forest, RF) implemented with publicly available data to predict future pasture biomass loads. This study showed that using observations of pasture growth along with environmental and pasture management variables enabled both models, GAM and RF to predict the pre-grazing pasture biomass production at field scale with an average error below 20%. If predictive variables (i.e. post-grazing pasture biomass) were excluded, model performance was reduced, generating errors up to 40%. The post-grazing biomass information at high spatial resolution (

[1]  Kefei Chen,et al.  A simple and parsimonious generalised additive model for predicting wheat yield in a decision support tool , 2019, Agricultural Systems.

[2]  Simon N. Wood,et al.  Shape constrained additive models , 2015, Stat. Comput..

[3]  D. Lobell,et al.  On the use of statistical models to predict crop yield responses to climate change , 2010 .

[4]  C. Scheer,et al.  Effect of irrigation scheduling on nitrous oxide emissions in intensively managed pastures , 2019, Agriculture, Ecosystems & Environment.

[5]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

[6]  C. Willmott.  ON THE VALIDATION OF MODELS , 1981 .

[7]  Zoltán Barcza,et al.  Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices , 2018, Agricultural and Forest Meteorology.

[8]  Bruno Basso,et al.  Assessing and Modeling Pasture Growth under Different Nitrogen Fertilizer and Defoliation Rates in Argentina and the United States , 2019, Agronomy Journal.

[9]  Pierre C. Beukes,et al.  Regular estimates of herbage mass can improve profitability of pasture-based dairy systems , 2019, Animal Production Science.

[10]  S. Vincenzi,et al.  Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy , 2011 .

[11]  W. J. Fulkerson,et al.  Benefits of accurately allocating feed on a daily basis to dairy cows grazing pasture , 2005 .

[12]  Jadunandan Dash,et al.  Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq. , 2018, The Science of the total environment.

[13]  Johannes Friedl,et al.  Dissimilatory nitrate reduction to ammonium (DNRA), not denitrification dominates nitrate reduction in subtropical pasture soils upon rewetting , 2018, Soil Biology and Biochemistry.

[14]  Cameron E. F. Clark,et al.  Gaps and Variability in Pasture Utilisation in Australian Pasture-Based Dairy Systems , 2013 .

[15]  F. Castaldi,et al.  Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize , 2017, Precision Agriculture.

[16]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[17]  Bruno Basso,et al.  Estimation of spatial and temporal variability of pasture growth and digestibility in grazing rotations coupling unmanned aerial vehicle (UAV) with crop simulation models , 2019, PloS one.

[18]  Telmo Adão,et al.  Classification of an Agrosilvopastoral System Using RGB Imagery from an Unmanned Aerial Vehicle , 2019, EPIA.

[19]  James W. Jones,et al.  Development, uncertainty and sensitivity analysis of the simple SALUS crop model in DSSAT , 2013 .

[20]  William N. Venables,et al.  Modern Applied Statistics with S , 2010 .

[21]  Cameron E. F. Clark,et al.  Original paper: Use of a pasture growth model to estimate herbage mass at a paddock scale and assist management on dairy farms , 2010 .

[22]  Jonathan P. Resop,et al.  Random Forests for Global and Regional Crop Yield Predictions , 2016, PloS one.

[23]  B. Basso,et al.  Seasonal crop yield forecast: Methods, applications, and accuracies , 2019, Advances in Agronomy.

[24]  Frank Wechsung,et al.  Statistical regression models for assessing climate impacts on crop yields: A validation study for winter wheat and silage maize in Germany , 2016 .

[25]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[26]  David B. Lobell,et al.  Comparing and combining process-based crop models and statistical models with some implications for climate change , 2017 .

[27]  W. Fulkerson,et al.  Estimating mass of temperate and tropical pastures in the subtropics , 1993 .

[28]  Jongwoo Song,et al.  Bias corrections for Random Forest in regression using residual rotation , 2015 .

[29]  D. Chapman,et al.  Regrowth dynamics and grazing decision rules: further analysis for dairy production systems based on perennial ryegrass (Lolium perenne L.) pastures , 2012 .

[30]  Bruno Basso,et al.  Can Organic Amendments Support Sustainable Vegetable Production , 2017 .

[31]  R. Tibshirani,et al.  Generalized Additive Models , 1991 .

[32]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[33]  Johannes Friedl,et al.  Field-scale management and environmental drivers of N2O emissions from pasture-based dairy systems , 2020, Nutrient Cycling in Agroecosystems.

[34]  T. Quaife,et al.  Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model , 2018, Remote Sensing of Environment.

[35]  Dj Donaghy,et al.  Plant-soluble carbohydrate reserves and senescence - key criteria for developing an effective grazing management system for ryegrass-based pastures: a review , 2001 .

[36]  Bruno Basso,et al.  Simulating crop growth and biogeochemical fluxes in response to land management using the SALUS model , 2015 .

[37]  Andreas Burkart,et al.  Deploying four optical UAV-based sensors over grassland: challenges and limitations , 2015 .