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Cover crops: the route to sustainable farming?

Given the increasing focus on soil health, erosion, and pollution, as a result of current agricultural practice, cover cropping is now being used across all sectors of crop production to save nitrogen and agrochemical inputs, increase yields and boost soil sustainability. Is cover cropping the route to sustainable farming? Agri-EPI Business Development Manager Duncan Ross dives into the topic for us highlighting the benefits to farmers to embrace a cover crop farm strategy:

Cover cropping means different things to different people, and the reasons for adoption of cover crops into a farming regime are very diverse and often specific to a particular farm. The transition from Common Agricultural Policy (CAP) as a support mechanism for agriculture to one based on environment and soil management (DEFRA’s Agricultural Transition Plan) will no doubt encourage wider uptake of cover crops.

Cover crops are often referred to as over-wintered, fast growing annuals planted between two cash crops. However, in certain circumstances a cover crop could be considered to cover a complete 12-month cycle due to geographical location, or a short-term grass ley.

The benefits can be many, such as:

  • Increasing levels of soil organic matter, as green manure is incorporated into the soil. increasing biological activity and water retention capacity.
  • Capture of vital nutrients that are made available to the subsequent cash crops rather than lost due to leaching.
  • Improve soil structure as vigorous root activity can be used to break up compaction.
  • Reduce pollution of nutrient and pesticides into water courses and erosion of soil.
  • Habitat creation which can be included in agri-environment schemes to generate additional revenue and can improve pest management by encouraging beneficial insects.

Healthier cropping sequences on the farm

Financially, it may be difficult to quantify the benefit, as any potential reduction of inputs or increase in yield of the following crops are offset by the cost of establishment and destruction of the cover crop. Cover crops, though, should be treated as an integral part of the rotation and good establishment is imperative, drill rather than broadcast, small nitrogen and slug pellet applications will result in a higher level of biomass, more nutrients being captured, more root activity, less pollution/erosion.

Which cover crop should I use?

The correct choice of cover crop will vary from farm to farm and will be dependent on many variables such as: what is trying to be achieved? Things to consider would be:

  • Soil type
  • Geographical location – less likely to get good autumn establishment in Northern parts of the UK.
  • Rotation – not using brassicas in a rotation containing OSR
  • Sowing dates – sooner after harvest of previous cash crop as practical to maximise biomass potential
  • Following plant timings – not to compromise future cash crop
  • Previous herbicide usage – residual herbicide could affect cover crop

Farm Business strategy

Seeking expert agronomic advice is key in making the correct decisions on cover crop strategy and type of seed to be included within the mix. For example, if the aim is the long-term management of arable weeds, where there are fewer active ingredients available, and herbicide resistance is to be considered, the weed challenge must be managed across the whole rotation. The cover crop chosen should be established and then destroyed along with the target weeds before it is able to re-seed, and over time the seed bank can be reduced. This method would rely on use of glyphosate as a control method so as not to disturb the soil as deep cultivation would mix the soil profile and reduce the effectiveness of the strategy.

Putting this into practice, some growers are having success with crimper rollers to destroy the cover crop and do away with the use of chemical control and should glyphosate be banned this may be the best option for conventional no-till farmers.

Improving wheat yield predictions with crop image technology

Novel applications developed by researchers at BioSense Institute in Serbia are dedicated to make deep learning technology a widely accepted practice in agriculture, providing small and big farm holders to benefit from precision farming technology.

BioSense, the Serbian Research and Development Institute for Information Technologies in Biosystems, is a multidisciplinary research institute for agriculture of the future. The wheat yield prediction research conducted in Serbia aims to increase the collection of farm management data, help farmers understand more about their farm business by using sensor technology and IoT applications, and reduce farm labour.

Wheat yield experiments

Wheat is one of the most important crop types in food production worldwide. Due to increasing food demand and rising population, it is necessary to boost production and supplies of wheat and other cereals.

In 2019, BioSense Institute, observed wheat in different experimental field stages and did this for three consecutive seasons. Cameras used during the experiment were the FLIR SC620 in season one and two, and a thermal camera in the third season. By taking pictures of the wheat growing in their field (four weeks before harvest), and uploading it through a mobile application, farmers were able to gain information about the wheat yield estimate based on the current state of growth.

The objective of this research is to enable the farmer to use imagery to detect at an earlier stage when estimated yields are below average and timely apply agronomic treatments to improve yield.

Farm efficiency with data management

The automatization of ear density calculation (number of ears per unit ground area, usually 1m2), which is one of the main agronomic yield components in determining grain yield in wheat, can provide fast evaluation of this attribute and potentially save 200 hours of manual work, ease monitoring, and increase crop management practice efficiency. This will save money from potential yield reduction, which can cause big losses in the farmers’ investments.

The currently used process of yield prediction includes manual and tedious work. The farmer takes samples from the area of 1m2 from the field (if the field is larger, then from a few locations within a field), and measures the biomass. The next step is to separate and count the ears of wheat manually. Since the counting of one sample requires up to 1 hour, while the number of samples can easily exceed 200, this can result in more than 200 working hours, or two to three weeks of manual labour that could be avoided.

The collected dataset comprises RGB and thermal images. Thermal images give us information about the difference in temperature between the ears and their background through their colouring and ease ear detection. Images were taken in four dates on two locations in two stages of wheat growth.

Power of deep learning

Since we have witnessed a huge breakthrough of neural networks, especially in image processing, deep learning has greatly outperformed classical models and algorithms. The nature of deep learning is that the addition of more data improves the quality of results, so by uploading images from farmers (crowd sourcing), the initial database will be expanded, so the algorithm will achieve better and more accurate results.

For more information about the methodologies used in this research by BioSense Institute, visit the DRAGON website.

 

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Data-driven precision agriculture by DRAGON

Agri-EPI Centre is a core partner within the data-driven agriculture services and skill acquisition project DRAGON. The aim of the project is to enable communication skill transfer and knowledge exchange between research organisations and end users through big data and effective data analytics.

 

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This article is an extract from an article of Željana Grbović – Junior Researcher, BioSense Institute – published on www.datadragon.eu.