Threats to soil quality in Denmark - A review of existing knowledge in the context of the EU Soil Thematic Strategy

The EU Commission is preparing a proposal for a Soil Framework Directive with the purpose of protecting the soil resources in Europe. The proposal identifies six major threats to the sustained quality of soils in Europe. This report addresses the threats that are considered most important under the prevailing soil and climatic conditions in Denmark: compaction, soil organic matter decline, and erosion by water and tillage. For each of these threats, the relevance and damage to soil functions as well as the geographic distribution in Denmark are outlined. We suggest a procedure for identifying areas at risk. This exercise involves an explicit identification of: i) the disturbing agent (climate / management) exerting the pressures on soil, and ii) the vulnerability of the soil to those stresses. Risk reduction targets, measures required to reach these targets, and the knowledge gaps and research needs to effectively cope with each threat are discussed. Our evaluation of the threats is based on soil resilience to the imposed stresses. Subsoil compaction is considered a severe threat to Danish soils due to frequent traffic with heavy machinery in modern agriculture and forestry. The soil content of organic matter is critically low for a range of Danish soils, which should be counteracted by appropriate management options. Soil erosion by tillage, and to a lesser degree by water, adversely affects soil quality on much of the farmland because degradation rates are much higher than generation of soil

Modeling the soil heterogeneity in the discrete element model of soil-sweep interaction

In agriculture the analyse of soil compaction in soil-tool interaction has a significant role. The equipments of agricultural farms are getting bigger and more complicated and it has huge importance to optimize the tillage methods. Two of most frequently investigated factors are the tool’s mixing-effect and the draught force on the tool; these results are important for agronomical experts to design tillage tools and cultivation processes. Discrete element method (DEM) is one of the numerical methods to model soil’s behaviour and soil-tool interaction. Aim of this study is to develop a 3D DEM model for clay soil and analyse the behaviour of soil- model regarding to non-homogeneous soil condition of agricultural fields. Simulation results will be compared with field test measurements for cone penetration tests. In this paper effects of particle’s shape and micromechanical properties will be investigated and simulations will be compared using special particles, so-called clumps in model. Clumps are aggregations that are set of spheres. This study investigates the effect of using clumps instead spheres in simulations and it will be attempted to model the thixotropic behaviour of soil with special kind of particles. Non-homogeneous property and varied compaction of field soil will be modelled with more layers, keep to be comparable the simulation results with field tests. Measurements were set for content; study investigates appropriate set of micromechanical parameters to simulate the effect of water

Effect of soil particle-size distribution (PSD) on soil-subsoiler interactions in the discrete element model

Aim of study: This work investigated the significance and mechanism for the effect of particle-size distribution (PSD) under different nominal radii using the discrete element method (DEM) and validated using the laboratory soil-bin results to accurately determine PSD.Area of study: Yangling, ChinaMaterial and methods: The experimental soil was Lou soil. Soil disturbance characteristics (soil rupture distance ratio, height of accumulated soil, soil density change rate) and cutting forces (draft and vertical) under different treatments were predicted and measured respectively.Main results: The ANOVA outputs showed that PSD significantly affected draft and vertical forces (

Sowing time, false seedbed, row distance and mechanical weed control in organic winter wheat.

In organic farming, mechanical weed control in winter wheat is often difficult to carry out in the fall, and may damage the crop, and weed harrowing in the spring is not effective against erect, tap-rooted weeds such as Tripleurospermum inodorum, Papaver rhoeas, Brassica napus and others which have been established in the autumn. Some experiments concerning sowing strategy and intensity of mechanical weed control, which included row distance, were conducted. The results underline the importance of choosing weed control strategy, including preventive measures, according to the weed flora in the field. In the experiment with low weed pressure and without erect weeds, there was very little effect of sowing strategy and row distance. In such a case, the winter wheat might as well be sown early, in order to avoid possible yield loss by later sowing, and at normal row distance to enhance the competitiveness of the crop. In the experiments with high weed pressure and erect weeds, the weed control was better with late sowing and large row distance (high intensity control), even though this was not always reflected in the yield. However, the trade-off for lower input to the soil seed bank in organic systems should be enough to balance off the risk of smaller yield

Field phenotyping and long-term platforms to characterise how crop genotypes interact with soil processes and the environment

Unsustainable agronomic practices and environmental change necessitate a revolution in agricultural production to ensure food security. A new generation of crops that yield more with fewer inputs and are adapted to more variable environments is needed. However, major changes in breeding programmes may be required to achieve this goal. By using the genetic variation in crop yield in specific target environments that vary in soil type, soil management, nutrient inputs and environmental stresses, robust traits suited to specific conditions can be identified. It is here that long-term experimental platforms and field phenotyping have an important role to play. In this review, we will provide information about some of the field-based platforms available and the cutting edge phenotyping systems at our disposal. We will also identify gaps in our field phenotyping resources that should be filled. We will go on to review the challenges in producing crop ideotypes for the dominant management systems for which we need sustainable solutions, and we discuss the potential impact of three-way interactions between genetics, environment and management. Finally, we will discuss the role that modelling can play in allowing us to fast-track some of these processes to allow us to make rapid gains in agricultural sustainability

Mehitamata õhusõiduki rakendamine põllukultuuride saagikuse ja maa harimisviiside tuvastamisel

A Thesis for applying for the degree of Doctor of Philosophy in Environmental Protection.Väitekiri filosoofiadoktori kraadi taotlemiseks keskkonnakaitse erialal.This thesis aims to examine how machine learning (ML) technologies have aided significant advancements in image analysis in the area of precision agriculture. These multimodal computing technologies extend the use of machine learning to a broader spectrum of data collecting and selection for the advancement of agricultural practices (Nawar et al., 2017) These techniques will assist complicated cropping systems with more informed decisions with less human intervention, and provide a scalable framework for incorporating expert knowledge of the PA system. (Chlingaryan et al., 2018). Complexity, on the other hand, can be seen as a disadvantage in crop trials, as machine learning models require training/testing databases, limited areas with insignificant sampling sizes, time and space-specificity, and environmental factor interventions, all of which complicate parameter selection and make using a single empirical model for an entire region impractical. During the early stages of writing this thesis, we used a relatively traditional machine learning method to address the regression problem of crop yield and biomass prediction [(i.e., random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] to predicted dry matter (DM) yields of red clover. It obtained favourable results, however, the choosing of hyperparameters, the lengthy algorithms selection process, data cleaning, and redundant collinearity issues significantly limited the way of the machine learning application. We will further discuss the recent trend of automated machine learning (AutoML) that has been driving further significant technological innovation in the application of artificial intelligence from its automated algorithm selection and hyperparameter optimization of the deployable pipeline model for unravelling substance problems. However, a present knowledge gap exists in the integration of machine learning (ML) technology with unmanned aerial systems (UAS) and hyperspectral-based imaging data categorization and regression applications. In this thesis, we explored a state-of-the-art (SOTA) and entirely open-source AutoML framework, Auto-sklearn, which was built on one of the most frequently used machine learning systems, Scikit-learn. It was integrated with two unique AutoML visualization tools to examine the recognition and acceptance of multispectral vegetation indices (VI) data collected from UAS and hyperspectral narrow-band VIs across a varied spectrum of agricultural management practices (AMP). These procedures incorporate soil tillage method (STM), cultivation method (CM), and manure application (MA), and are classified as four-crop combination fields (i.e., red clover-grass mixture, spring wheat, pea-oat mixture, and spring barley). Additionally, they have not been thoroughly evaluated and lack characteristics that are accessible in agriculture remote sensing applications. This thesis further explores the existing gaps in the knowledge base for several critical crop categories and cultivation management methods referring to biomass and yield analysis, as well as to gain a better understanding of the potential for remotely sensed solutions to field-based and multifunctional platforms to meet precision agriculture demands. To overcome these knowledge gaps, this research introduces a rapid, non-destructive, and low-cost framework for field-based biomass and grain yield modelling, as well as the identification of agricultural management practices. The results may aid agronomists and farmers in establishing more accurate agricultural methods and in monitoring environmental conditions more effectively.Doktoritöö eesmärk oli uurida, kuidas masinõppe (MÕ) tehnoloogiad võimaldavad edusamme täppispõllumajanduse valdkonna pildianalüüsis. Multimodaalsed arvutustehnoloogiad laiendavad masinõppe kasutamist põllumajanduses andmete kogumisel ja valimisel (Nawar et al., 2017). Selline täpsemal informatsioonil põhinev tehnoloogia võimaldab keerukate viljelussüsteemide puhul teha otsuseid inimese vähema sekkumisega, ja loob skaleeritava raamistiku täppispõllumajanduse jaoks (Chlingaryan et al., 2018). Põllukultuuride katsete korral on komplekssete masinõppemudelite kasutamine keerukas, sest alad on piiratud ning valimi suurus ei ole piisav; vaja on testandmebaase, kindlaid aja- ja ruumitingimusi ning keskkonnategureid. See komplitseerib parameetrite valikut ning muudab ebapraktiliseks ühe empiirilise mudeli kasutamise terves piirkonnas. Siinse uurimuse algetapis rakendati suhteliselt traditsioonilist masinõppemeetodit, et lahendada saagikuse ja biomassi prognoosimise regressiooniprobleem (otsustusmetsa regression, tugivektori regressioon ja tehisnärvivõrk) punase ristiku prognoositava kuivaine saagikuse suhtes. Saadi sobivaid tulemusi, kuid hüperparameetrite valimine, pikk algoritmide valimisprotsess, andmete puhastamine ja kollineaarsusprobleemid takistasid masinõpet oluliselt. Automatiseeritud masinõppe (AMÕ) uusimate suundumustena rakendatakse tehisintellekti, et lahendada põhiprobleemid automatiseeritud algoritmi valiku ja rakendatava pipeline-mudeli hüperparameetrite optimeerimise abil. Seni napib teadmisi MÕ tehnoloogia integreerimiseks mehitamata õhusõidukite ning hüperspektripõhiste pildiandmete kategoriseerimise ja regressioonirakendustega. Väitekirjas uuriti nüüdisaegset ja avatud lähtekoodiga AMÕ tehnoloogiat Auto-sklearn, mis on ühe enimkasutatava masinõppesüsteemi Scikit-learn edasiarendus. Süsteemiga liideti kaks unikaalset AMÕ visualiseerimisrakendust, et uurida mehitamata õhusõidukiga kogutud andmete multispektraalsete taimkatteindeksite ja hüperspektraalsete kitsaribaandmete taimkatteindeksite tuvastamist ja rakendamist põllumajanduses. Neid võtteid kasutatakse mullaharimisel, kultiveerimisel ja sõnnikuga väetamisel nelja kultuuriga põldudel (punase ristiku rohusegu, suvinisu, herne-kaera segu, suvioder). Neid ei ole põhjalikult hinnatud, samuti ei hõlma need omadusi, mida kasutatatakse põllumajanduses kaugseire rakendustes. Uurimus käsitleb biomassi ja saagikuse seni uurimata analüüsivõimalusi oluliste põllukultuuride ja viljelusmeetodite näitel. Hinnatakse ka kaugseirelahenduste potentsiaali põllupõhiste ja multifunktsionaalsete platvormide kasutamisel täppispõllumajanduses. Uurimus tutvustab kiiret, keskkonna suhtes kahjutut ja mõõduka hinnaga tehnoloogiat põllupõhise biomassi ja teraviljasaagi modelleerimiseks, et leida sobiv viljelusviis. Töö tulemused võimaldavad põllumajandustootjatel ja agronoomidel tõhusamalt valida põllundustehnoloogiaid ning arvestada täpsemalt keskkonnatingimustega.Publication of this thesis is supported by the Estonian University of Life Scieces and by the Doctoral School of Earth Sciences and Ecology created under the auspices of the European Social Fund

Improved assessment of nitrogen and phosphorus fate and transport for intensively managed irrigated stream-aquifer systems

2019 Fall.Includes bibliographical references.Nitrogen (N) and Phosphorus (P) are essential elements for animal nutrition and plant growth. However, over the previous decades, excessive loading of fertilizers in agricultural activities has led to elevated concentrations of N and P contaminations in surface waters and groundwater worldwide and associated eutrophication. Therefore, precisely understanding and representation of water movement and fate and transport of N and P within a complex dynamic groundwater-surface water system affected by agricultural practices is of essential importance for sustaining ecological health of the stream-aquifer environment while maintaining high agricultural productivity. Modeling tools often are used to assess N and P contamination and evaluate the impact of management practices. Such models include land surface-based watershed models such SWAT, and aquifer-based models that simulate spatially-distributed groundwater flow. However, SWAT simulates groundwater flow in a simplistic fashion and therefore is not suited for watersheds with complex groundwater flow patterns and groundwater-surface interactions, whereas groundwater models do not simulate land surface processes. This dissertation establishes the modeling capacity for assessing the movement, transformation, and storage of nitrate (NO₃) and soluble P in intensively managed irrigated stream-aquifer systems. This is accomplished by (1) developing a method to apply the SWAT model to such a system, and includes: designating each cultivated field as an individual hydrologic response unit (HRU), crop rotations to simulate the impact of changing crop types for each cultivated field, including N and P mass in irrigation water, and seepage from earthen irrigation canals into the aquifer; (2) simulating land surface hydrology, groundwater flow, and groundwater-surface water interactions in the system using the coupled flow model SWAT-MODFLOW, with the enhanced capability of linkage between SWAT groundwater irrigation HRUs and MODFLOW pumping cells, and the use of MODFLOW's EVT package to simulate groundwater evapotranspiration; and (3) linking RT3D, a widely used groundwater reactive solute transport model, to SWAT-MODFLOW to credibly represent of NO₃-N and soluble P fate and transport processes in irrigated agroecosystems to evaluate best management practices for nutrient contamination. This last phase will also address the uncertainty in system output (in-stream nutrient loads and concentrations, groundwater nutrient concentrations model predictions). Each modeling phase is applied to a 734 km² study region in the Lower Arkansas River Valley (LARV), an alluvial valley in Colorado, USA, which has been intensively irrigated for over 130 years and is threatened by shallow water tables and nutrient contamination. Multiple best management practices (BMPs) are investigated to analyze the effectiveness in reducing NO₃-N and soluble P contamination in the LARV. These strategies are related to irrigation management, nutrient management, water conveyance efficiency, and tillage operations. The most effective individual BMP in most areas is to decrease fertilizer by 30%, resulting in average NO₃-N and soluble P concentrations within the region could be reduced by 14% and 9%, respectively. This individual BMP could lower the average NO₃-N concentrations by 19% and soluble P concentrations by 2%. Combinations of using 30% irrigation reduction, 30% fertilization reduction, 60% canal seepage, and conservation tillage are predicted to have the greatest overall impact that can not only provide a decrease of groundwater concentration in NO₃-N up to 41% and soluble P concentration up to 8%, but also reduce the median of the in-stream NO₃-N and soluble P to meet the Colorado interim standard. As nutrient conditions within the Lower Arkansas River Valley are typical of those in many other intensively irrigated regions, the results of this dissertation and the developed modeling tools can be applied to other watersheds worldwide

An interdisciplinary approach towards improved understanding of soil deformation during compaction

International audienceSoil compaction not only reduces available pore volume in which fluids are stored, but it alters the arrangement of soil constituents and pore geometry, thereby adversely impacting fluid transport and a range of soil ecological functions. Quantitative understanding of stress transmission and deformation processes in arable soils remains limited. Yet such knowledge is essential for better predictions of effects of soil management practices such as agricultural field traffic on soil functioning. Concepts and theory used in agricultural soil mechanics (soil compaction and soil tillage) are often adopted from conventional soil mechanics (e.g. foundation engineering). However, in contrast with standard geotechnical applications, undesired stresses applied by agricultural tyres/tracks are highly dynamic and last for very short times. Moreover, arable soils are typically unsaturated and contain important secondary structures (e.g. aggregates), factors important for affecting their soil mechanical behaviour. Mechanical processes in porous media are not only of concern in soil mechanics, but also in other fields including geophysics and granular material science. Despite similarity of basic mechanical processes, theoretical frameworks often differ and reflect disciplinary focus. We review concepts from different but complementary fields concerned with porous media mechanics and highlight opportunities for synergistic advances in understanding deformation and compaction of arable soils. We highlight the important role of technological advances in non-destructive measurement methods at pore (X-ray tomography) and soil profile (seismic) scales that not only offer new insights into soil architecture and enable visualization of soil deformation, but are becoming instrumental in the development and validation of new soil compaction models. The integration of concepts underlying dynamic processes that modify soil pore spaces and bulk properties will improve the understanding of how soil management affect vital soil mechanical, hydraulic and ecological functions supporting plant growth

Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation

The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years