Improving the physical properties and water management of heavy meadow soils through water management and the use of soil improvers

Abstract

The aim is to develop a method that links different approaches to support decision-making in the agricultural sector - from monitoring to planning and decision-making-, that contributes to sustainable water and agricultural management. Patent claims: 1. Improving the physical properties and water management of heavy meadow soils through water management and the use of soil improvers Procedural steps: 1) Based on satellite data from previous years, assessment of inland water damage in the selected area 2) Digital Surface Model, meaning the creation of a surface model using photogrammetry 3) Based on the completed model, the preparation of a system designed to improve water management

Description

[0001] Patent description

[0002] 1) Title of invention:

[0003] Improving the physical properties and water management of heavy meadow soils through water management and the use of soil improvers

[0004] 2) Name of technical area:

[0005] Natural science - agricultural science

[0006] 3) Presentation of the state of the teefemhsgy of which the applicant hee^se familiar with, together with a description of solutions close to the invention and, if possible, sources which facilitate the understanding of the invention and the assessment of its patentability:

[0007] Inland water causes serious problems for arable crop production in a significant part of Hungary, affecting 45% of the country, mainly the Great Plain, due to its extent and natural conditions conducive to the formation of water abundance. About a quarter of our country is a low-lying flat area from which water does not flow naturally. The regions of the country most threatened by inland water: landscapes of the Upper Tisza region (Bereg, Tisza-Szamoskdz, Retkoz, Bodrogkbz, Taktakoz), Hortobagy - Berettyo extension, Jaszsag and parts of Nagykunsag and the region of Lower Tisza.

[0008] The area at risk reaches 4.4 million ha, 41% of which is intensively farmed agricultural land. The average inland water floods over many years affects 85,000 hectares nationwide, but in extreme cases it can exceed hundreds of thousands of hectares. In the last 40 years, there have only been a few years when no major inland water protection has taken place.

[0009] In agricultural areas, inland water is from high rainfall, seeping waters from higher watercourses than terrain and from waters coming from higher areas due to adverse fall conditions. The poor water conductivity of the soil, high groundwater level, insufficient terrain fall, fields with different soil and water management, the use of heavy power machines, the lack of underground cultivation, the lack of a drainage network, and unexpected, large amounts of rainfall play a decisive role in the formation of inland water.

[0010] Protection primarily appears in the proper use of land. With the right choice of cultivation methods, we can do a lot to prevent the formation and damage of inland water. If we cannot intervene with land use or the damage is small, we must use methods of land improvement, which can be: physical or mechanical, chemical and biological. In the case of inland water problems, mechanical methods are the most relevant. These include: deep rotation, subsoil loosening, drainage (soil piping) and drainage.

[0011] Studies have shown that drainage is one of the dominant agricultural water management practices that can increase and improve crop yields in soils with shallow water levels or poor drainage (V alayamkunnath et al. 2020). However, the implementation of such projects requires proper planning and takes into account different sources of uncertainties and risks. Studies have shown that investments in agricultural projects are less successful, mainly due to the high risks and uncertainty associated with implementation (Yet et al. 2020). Financing a drainage project is a one-time investment with long-term benefits. However, if the investment benefits are lower than the costs of the project, which are often financed by loans, agricultural productivity and farm profitability can be significantly reduced. Many climatic, environmental and financial factors can have a significant impact on return of investment, and these factors are often highly uncertain and interrelated (Pollino et al. 2007; Barton et al. 2008). For example, between 1981 and 2010, Hungarian agriculture experienced high climate variability, resulting in a 33-67% decrease in crop yields due to drought (Varallyay 2015). In addition, unforeseen socioeconomic factors such as inflation rates and production costs that change over time can significantly affect the results of agricultural projects (Freebaim 1981). Recent research on data-driven decisionmaking in agricultural and environmental sciences shows that data is scarce to predict project financial outcomes. Consequently, this limits the use of data-driven valuation approaches in the agricultural sector (Yet et al. 2020; Yet et al. 2016). Bayesian statistics [Puga et al. 2015; Vogelgesang and Scharkow 2017; van de Schoot et al. 2021] show promising results in other sectors, particularly risk assessment and decision-making (Flaxman et al. 2020; Brauner et al. 2021). In the agricultural sector, where environmental, social and economic factors are likely to occur coupled with a lack of data, Bayesian statistics can serve as a tool to support decisionmaking and project implementation (Govender et al. 2022; Comet et al. 2016). Bayesian statistical models allow experts to model causation based on preliminary and ex post estimates (Rasmussen et al. 2013; Constantinou et al. 2016). This is particularly important in agricultural systems, where researchers and decision-makers often do not have sufficient data to calculate the preliminary and conditional probabilities needed to make decisions (Tari 1996).

[0012] 4) Indication of the task to be solved with the invention:

[0013] In agriculture, inland water poses a serious threat to soil properties, crop yields and farm profitability. Today, water surplus and scarcity, appearing almost simultaneously as a result of climate change, cause significant problems in arable crop production in Hungary. Excess water affects the efficiency of the entire crop production system and the cultivation technology, especially in terms of carrying out tillage, absorbing applied nutrients and optimal feasibility of harvesting. Today, the application of best practices in soil management cannot be achieved without precision farming and data-driven decision-making.

[0014] Proximal soil sensing and remote sensing offer the possibility of collecting high- precision terrain models and terrain features, agronomic practices and vegetation indices to develop data-driven decision support. In case of inland waterlogging, it is important to drain excess water from the field before applying any agronomic and soil management practices.

[0015] For the most suitable solution, high-resolution sensors from VERRIS technology and multispectral images from Sentinel-2 mission were used to learn about soil properties, vegetation and weather indicators to characterize the terrain characteristics of water-affected fields. This dataset provides a solution for monitoring and managing water management in agricultural systems. However, the implementation of agricultural projects, such as drainage or other crop production technologies, involves high uncertainty and risk and, if not properly planned, can have a significant negative impact on farm profitability. As such, cost-benefit analyses can help allocate resources more efficiently. Specifically, we combined a wide range of field and remote sensing data, unsupervised machine learning, and Bayesian probabilistic models to (1) identify inland water-prone sites on an agricultural scale, and (2) test whether installing drainage systems would bring positive benefits to the farmer. Using the K-means clustering algorithm of water and vegetation indices from Sentinel-2 multispectral images, we were able to detect places prone to inland water in the studied area. Furthermore, by combining the Bayesian statistical model with the A / B test, we show that installing a drainage system can increase farm profitability by 1.7 times per year compared to existing farming. The co-effects related to yield, area under cultivation and time (years) were 0.5, 1.5 and 1.9 respectively.

[0016] Overall, the results underline the importance of data-driven decision-making in agricultural project design and resource management in the wake of smart agriculture for food security and climate change adaptation.

[0017] 5.) Details of one or more ways in which the invention is implemented to support the scope of protection:

[0018] In the course of our research, we first assessed the extent of inland water damage on the selected fields based on satellite data from previous years. ND VI values were used for the survey. When using satellite imagery or aerial photography, different plant indices are often used to estimate the spatial pattern and expected yield of plant biomass. Many types of vegetation indices have been developed, one of which is the RVI (Ratio Vegetation Index), which is equal to the ratio of radiation reflected in the infrared and red spectra. An improved version of this is the Normalised Difference Vegetation Index (ND VI), which is one of the most commonly used plant indexes. ND VI is a dimensionless metric used to define vegetation activity in a given area.

[0019] It is calculated by dividing the difference and sum of radiation intensities reflected in the near-infrared (NIR) and visible red (RED) spectra of light. NDVI is related to the specific chlorophyll content of the vegetation cover covering the area. NDVI is well suited for determining the amount of green biomass or leaf surface area, and can be used to detect undesirable patterns within a field, such as inland water or the damage it causes.

[0020] We used images from the European Space Agency's Sentinel 2 satellite, before harvest. For different cultures and vintages, appropriate information could be obtained at different times. The ndvi value of the damaged area also depends on the time of shooting.

[0021] For the location of our research, we chose a field located next to Cigand settlement, which is 92 hectares in size. In the past 5 years, agricultural damage caused by inland water has always been above 20% on the experimental field. However, in 2020, damage was greater than ever. In total, 43% of the area was affected by inland water, which caused more than 30% crop loss.

[0022] In the second step, we created a Digital Surface Model (DSM) using photogrammetry. Photogrammetry is the discipline of remote sensing, which determines the dimensions of real objects in the images based on measurements and calculations taken of objects and terrain. Photogrammetry is a procedure based on the quantitative evaluation of remotely sensed images. For the recording, we used an RTK precision monitoring drone, we took the shots with a normal RGB camera from a height of 120 meters. The raw images were then stitched together using DJI Terra geospatial software, which produced the digital surface model (DSM). There is a strong correlation between surface irregularities and damage caused by inland water.

[0023] In the third step, with the help of the completed surface model, the natural slopes and the maximum depth and slope of the drain pipe drainage can be determined. After creating the relief model, it was possible to see where the deepest areas were, how deep the trenches to be drained were. After that, the design of drainage pipes for the area could begin. During the design, we used GIS software, in which we prepared the tunnel plan consisting of the spine and suction systems.

[0024] 7.) An explanation of how the invention can be used in industry, if it is not apparent from the nature or description of the invention:

[0025] As described in the previous points, applicability in agriculture is substantiated.

[0026] 8) Description of the advantages of the invention compared to the state of the art Agricultural researchers and resource managers experience advances in agricultural technologies, often related to precision farming. However, each device is used in an isolated manner without a clear connection. For example, in agriculture, especially water management, research often focuses on monitoring and has limited research on management and decisionmaking.

[0027] We have tried to link different approaches to support decision-making in the agricultural sector, from monitoring to planning to decision-making. Another important aspect is the careful handling of compacted soil layers with traditional methods - for example, only when moisture is adequate, in order to smear it and prevent further compaction. In addition, well-thought-out loosening and dusting activities, such as cultivating or medium or deep loosening, all help to infiltrate rainfall locally, helping to avoid the formation of inland water in some cases.

[0028] We found that the combination of remotely perceived data, Bayesian modeling and A / B testing represents a promising area for future research into agricultural resource management. By combining these tools, we can gain an in-depth understanding of the impact of waterlogging on agricultural production and productivity, as well as on cost-benefit ratios. As more and more freely available data, data collection and analysis tools emerge, these methods have great potential to make a significant contribution to sustainable water and agricultural management.

[0029] Patent claims:

[0030] Improving the physical properties and water management of heavy meadow soils through water management and the use of soil improvers

Claims

Patent claim(s):

1. Improving the physical properties and water management of heavy meadow soils through water management and the use of soil improvers