Economics of autonomous machines for regenerative agriculture

In: Precision agriculture '23
Authors:
A.K.M.A. Al-Amin 1Harper Adams University, Shropshire, Newport, TF10 8NB, United Kingdom; abdullahalamin@live.harper.ac.uk; abdullah.alamin@bau.edu.bd
2Bangladesh Agricultural University, Mymensingh 2202, Bangladesh

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E. Dickin 1Harper Adams University, Shropshire, Newport, TF10 8NB, United Kingdom; abdullahalamin@live.harper.ac.uk; abdullah.alamin@bau.edu.bd

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J.M. Monaghan 1Harper Adams University, Shropshire, Newport, TF10 8NB, United Kingdom; abdullahalamin@live.harper.ac.uk; abdullah.alamin@bau.edu.bd

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K. Franklin 1Harper Adams University, Shropshire, Newport, TF10 8NB, United Kingdom; abdullahalamin@live.harper.ac.uk; abdullah.alamin@bau.edu.bd

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J. Lowenberg-DeBoer 1Harper Adams University, Shropshire, Newport, TF10 8NB, United Kingdom; abdullahalamin@live.harper.ac.uk; abdullah.alamin@bau.edu.bd

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Type:
Chapter
Pages:
747–755
DOI:
https://doi.org/10.3920/978-90-8686-947-3_94

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Biodiversity benefits of mixing cropping are well known, but the practice is rare because it increases labour and management requirements. Regenerative strip cropping is the simplest mixed cropping and hypothesized to be economically competitive with autonomous machines. To test this hypothesis, the Hands Free Hectare-Linear Programming (HFH-LP) model was used to analyse a wheatbarley- flower mix-spring bean rotation on a modelled 500 ha arable farm in England. The results show that autonomous strip cropping was more profitable than with conventional machines and modest increases in yield or reductions in variable costs made it economically competitive than more intensive rotations.

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Cover Precision agriculture '23
E-Book ISBN:
9789086869473
Publisher:
Wageningen Academic
Print Publication Date:
27 Jul 2023
Preliminary Material
Grape counting in RGB videos – comparing two instance segmentation models
Changing how agronomic trials are conducted: modulated on farm response surface experiments (MORSE)
Quantifying real-time opening disk load to assess compaction and potential for planter control
Water status estimation using thermal imagery at different scales in the vineyard
Smart irrigation system for precision water management: effect on yield and fruit quality of yellow fleshed kiwifruit in northern Italy
Comparison between 60° and 30° hollow cone nozzles for targeted UAV-spray applications in vineyards
Using a vegetation index to define homogeneous zones for variable rate irrigation in vineyard
Evaluation of the PROMET model in on-farm research at the ‘Digital Experimental Field BeSt-SH’
Data requirements for forecasting tree crop yield – a macadamia case study
Precision monitoring of vine water stress using UAVs and opensource processing chains
Detection of Fusarium oxysporum by hyperspectral imaging in strawberry plants
Testing crop protection performances in a vineyard subjected to real-time volume adjustment through an adapted conventional sprayer
Limitations of grain yield monitor data to evaluate treatment differences within on-farm experimentation
Effects of canopy density-based airblast fan airflow adjustment on vines spray deposit
Farmer-led on-farm experimentation enhanced with digital agronomy
Evaluation of portable tools for fast field assessment of winter wheat grain quality
Redesigning spatial on-farm precision experiments for innovative vineyard crop protection
Evaluation of the soil quality of Chilean orchards using a gamma radiation sensor
Follow the leader: a path generator and controller for precision tree scanning with a robotic manipulator
Monitoring chickpea physiological traits by Sentinel-2 imagery
Investigation of spraying applications using a UAS in viticulture
A new leafiness-LiDAR index to estimate light interception in intensive olive orchards
Long-term evaluation of the Grassmaster II probe used to estimate productivity of dryland pastures
Technological approach to evaluating the effect of livestock trampling on soil compaction
Tracking wheat senescence based on UAV multispectral imaging
Variable-rate fertiliser application to manage spatial variability in a hilly vineyard of Prosecco PDO
Synthetic data generation for validating site-specific crop yield response modelling using WOFOST and gaussian geostatistical simulations
Implementation of variable rate of inputs in winter crops under rainfed conditions
Strawberry flower and fruit detection based on an autonomous imaging robot and deep learning
Variable rate nitrogen in potato cropping systems
Using an oat cover crop as a reflector of the spatial variation of soil nutrient availability
On-farm evaluation of variable rate irrigation for winter wheat in semi-arid western USA
Assessing the ability of ECa and drone data to capture spatial patterns in soil moisture for more precise turfgrass irrigation
How can precision agriculture prescription maps contribute to the 50% pesticide reduction goal of the farm-to-fork strategy in viticulture
An optical trapezoid model for actual evapotranspiration and winter wheat yield estimation
A novel approach to map-sensor-based site-specific nitrogen fertilisation in winter wheat
Second-generation ultrasonic sensor in precision spraying: testing and actuation range refinement
Target-N: Sentinel-2 based nitrogen optimisation in Swedish cereal production
Testing irrigation management based on an unoccupied aerial vehicle and an artificial neural network
Classifying potato yield in North Spain from Sentinel-2 data based on Random Forest and multitemporal auxiliary data
A low cost sensor to improve surface irrigation management
Grapevine water status in a variably irrigated vineyard with NIR hyperspectral imaging from a UAV
Potential of the dark green color index for dynamic monitoring of N requirements in wheat crop
Combining crop growth modeling, active sensing and machine learning to improve in-season nitrogen management of maize
Weeds detection in winter wheat field using improved-YOLOv4 with attention module from UAV imagery
Towards a digital twin for optimal field management
Generalization of deep learning models applied to semantic segmentation of in-field natural images in vineyards
Detecting and locating mushroom clusters by a Mask R-CNN model in farm environment
Recognition of weeds in cereals using AI architecture

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