Predicting an efficacy of a crop protection agent

Abstract

The disclosure shows methods, computer system and non-transitory computer readable storage medium which relate to the prediction of an efficacy of a crop protection agent.

Description

[0001] Predicting an efficacy of a crop protection agent

[0002] FIELD OF THE DISCLOSURE

[0003] Systems, methods, and computer programs disclosed herein relate to the prediction of an efficacy of a crop protection agent.

[0004] BACKGROUND

[0005] Agriculture, as a critical sector, is continually faced with numerous challenges that threaten crop yield and quality. Among these challenges are pests, diseases, and various harmful environmental conditions. To combat these threats and ensure optimal crop production, crop protection agents are employed. These agents encompass a broad range of substances or methods, including pesticides, fungicides, herbicides, and biological control agents, designed to protect crops from harmful organisms and / or conditions.

[0006] The effectiveness of these crop protection agents, referred to as their “efficacy”, is a crucial determinant of their utility in agricultural practices. Efficacy can be measured in several ways, such as the reduction in pest or disease incidence or severity compared to untreated crops, the increase in crop yield or quality due to the protection provided, or the duration of the protection offered.

[0007] Understanding the efficacy of a crop protection agent under a given environmental condition is of paramount importance. It not only guides the selection and use of these agents but also influences the development of crop protection strategies and policies. Moreover, detailed knowledge about the efficacy of a crop protection agent depending in a given environmental scenario can lead to more sustainable and efficient agricultural practices by minimizing unnecessary or ineffective applications, thereby reducing costs and environmental impact.

[0008] There is a need for the ability to predict the efficacy of a crop protection agent in particular at different environmental conditions for various crops around the globe.

[0009] SUMMARY

[0010] This need and further needs are met by the subject matter of the independent claims of the present disclosure. Preferred embodiments are defined in the dependent claims, the description, and the drawings.

[0011] In a first aspect, the present disclosure relates to a computer-implemented method comprising the steps: providing a model, wherein the model is configured to determine an amount of a crop protection agent in one or more parts of a crop based on input data, wherein the input data comprises agent data, application data and crop data, receiving agent data, application data, and crop data wherein agent data specifies the crop protection agent, wherein the application data specifies an application of the crop protection agent at one or more points in time in a target area and wherein the crop data specifies the crop that is cultivated in the target area, determining an amount of the crop protection agent in one or more parts of the crop in the target area at one or more points in time using the model, determining an efficacy of the crop protection agent at the one or more points in time based on the determined amount, outputting the determined efficacy.

[0012] In another aspect, the present disclosure provides a computer system comprising: a processor; and a memory storing an application program comprising a model configured to determine an amount of a crop protection agent in one or more parts of a crop based on input data, wherein the input data comprises agent data, application data and crop data; the application program configured to perform, when executed by the processor, an operation, the operation comprising: receiving agent data, application data, and crop data wherein agent data specifies the crop protection agent, wherein the application data specifies an application of the crop protection agent at one or more points in time in a target area, and wherein the crop data specifies the crop that is cultivated in the target area, determining an amount of the crop protection agent in one or more parts of the crop cultivated in the target area at one or more points in time using the model, determining an efficacy of the crop protection agent at the one or more points in time based on the determined amount, outputting the determined efficacy.

[0013] In another aspect, the present disclosure provides a non-transitory computer readable storage medium having stored thereon software instructions that, when executed by a processor of a computer system, cause the computer system to execute the following steps: receiving agent data, application data, and crop data wherein agent data specifies the crop protection agent, wherein the application data specifies an application of the crop protection agent at one or more points in time in a target area, and wherein the crop data specifies the crop that is cultivated in the target area, determining an amount of the crop protection agent in one or more parts of the crop cultivated in the target area at one or more points in time using a model, wherein the model is configured to determine an amount of a crop protection agent in one or more parts of a crop based on input data, wherein the input data comprises agent data, application data and crop data, determining an efficacy of the crop protection agent at the one or more points in time based on the determined amount, outputting the determined efficacy.

[0014] BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Fig. 1 shows an exemplary and schematic embodiment of the model of the present disclosure.

[0016] Fig. 2 shows an example of the development of biomass over time.

[0017] Fig. 3 shows an example of the result of linking a plant growth model with an uptake model and a translocation and metabolization model.

[0018] Fig. 4 shows another example of the result of linking a plant growth model with an uptake model and a translocation and metabolization model. DETAILED DESCRIPTION

[0019] The invention will be more particularly elucidated below without distinguishing between the aspects of the invention (method, computer system, computer-readable storage medium). On the contrary, the following elucidations are intended to apply analogously to all the aspects of the invention, irrespective of in which context (method, computer system, computer-readable storage medium) they occur.

[0020] If steps are stated in an order in the present description or in the claims, this does not necessarily mean that the invention is restricted to the stated order. On the contrary, it is conceivable that the steps can also be executed in a different order or else in parallel to one another, unless one step builds upon another step, this absolutely requiring that the building step be executed subsequently (this being, however, clear in the individual case). The stated orders are thus preferred embodiments of the present disclosure.

[0021] As used herein, the articles “a” and “an” are intended to include one or more items and may be used interchangeably with “one or more” and “at least one.” As used in the description and the claims, the singular form of “a”, “an”, and “the” include plural referents, unless the context clearly dictates otherwise. Where only one item is intended, the term “one” or similar language is used. Also, as used herein, the terms “has”, “have”, “having”, or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based at least partially on” unless explicitly stated otherwise.

[0022] Some implementations of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings, in which some, but not all implementations of the disclosure are shown. Indeed, various implementations of the disclosure may be embodied in many different forms and should not be construed as limited to the implementations set forth herein; rather, these example implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0023] The terms used in this disclosure have the meaning that these terms have in the prior art, in particular in the prior art cited in this disclosure, unless otherwise indicated.

[0024] The present disclosure describes means for predicting an efficacy of a crop protection agent applied one or more times to a crop in a target area.

[0025] A “crop protection agent” is a component of a crop protection product. The crop protection agent is also referred to as a biologically active agent or active agent for short. This crop protection agent is the component of a crop protection product that provides the intended protective effect. It is the substance that controls, suppresses, repels or eliminates one or more target pests and / or one or more diseases or foster plant health. Examples of crop protection agents are natural or synthetic herbicides, fungicides, and other pesticides (e.g., insecticides, nematicides, molluscicides and the like) and / or biostimulants. In an example, the crop protection agent is a systemic crop protection agent which refers to an active agent that, when applied to a plant, is absorbed and distributed throughout the plant's tissues, providing protection from pests or diseases. These active agents are absorbed by the plant and can be present in various parts of the plant, such as leaves, stems, and roots. Non-limiting examples of systemic crop protection agents that are applied through foliar application are: Glyphosate, Diquat, Paraquat, 2,4-D, Triclopyr, Imazapyr, Glufosinate, Metribuzin, Sulfosulfuron, Bispyribac-sodium, etc. Non-limiting examples of systemic crop protection agents that are applied to the soil and are taken up by the roots of the plants (crop or weeds) are: Imidacloprid, Thiamethoxam, Thiacloprid, Clothianidin, Fipronil, Fludioxonil, Metalaxyl, Propiconazole, Azoxystrobin, Nitenpyram, Flutriafol, Difenoconazole, Cyantraniliprole, Spirotetramat, Sulfoxaflor, Acetamiprid, Dinotefuran, etc. The term “crop protection product” is understood to mean a product that serves to protect crops and / or crop products from harmful organisms and / or prevent the effect thereof. Crop protection products are usually formulations which comprise at least one crop protection agent.

[0026] A “harmful organism” is an organism that occurs during the cultivation of crops and can damage the crop, and / or negatively affect the crop’s harvest. Examples of such harmful organisms are animal pests such as beetles, caterpillars and worms, fungi and pathogens (e.g., bacteria and viruses). Even if viruses are not classified as organisms from a biological point of view, they are still be included under the term harmful organism in this disclosure.

[0027] The term “crop” is understood to mean a plant which is specifically grown as a useful plant by human intervention. A “crop” refers to any plant that is intentionally grown and cultivated by humans for food, feed, fiber, timber, fragrance, medical, sanitary and / or other economic purposes. These plants are usually specifically selected and managed to produce a yield or harvest, and they can include a wide variety of species such as grains, fruits, vegetables, oilseeds, and fiber crops. Parts of the crop being grown may be suitable for human and / or animal consumption. Ornamental plants and algae also fall under the term “crop”. The term “crop” also includes seeds that are sown to allow a crop to grow.

[0028] The term “crop” also includes cover crops. A “cover crop” is a plant that is planted primarily to manage soil erosion, fertility, quality, water, weeds, pests, diseases, biodiversity, and / or wildlife. Cover crops are usually not grown for direct harvest but are instead used to benefit the soil and / or subsequent crops. They are typically planted during off-season periods and / or in between regular crop plantings. Cover crops can help prevent soil erosion, improve soil health, increase organic matter, suppress weeds, and / or reduce the need for synthetic fertilizers and / or pesticides. Additionally, they can enhance biodiversity, provide habitat for beneficial insects, and / or contribute to overall sustainable agricultural practices. Common cover crops include legumes like clover and vetch, grasses like rye and oats, and various other species depending on the specific agricultural goals and / or local conditions.

[0029] The term “target area” refers to a part of the earth’s surface (including the crops growing there) to which the crop protection agent has been and / or is to be applied.

[0030] A crop protection agent can be applied in several ways depending on the type of crop protection agent and the type of crop. The present disclosure is intended to encompass all these ways, some of which are exemplified below.

[0031] The crop protection agent can be applied using foliar spraying. This is the most common method where the crop protection agent is sprayed directly onto the crop leaves.

[0032] Spraying can be done using a handheld sprayer for small areas, and / or a (tractor-mounted) sprayer.

[0033] The crop protection agent can be applied using soil application. The crop protection agent can be applied to the soil either before or after the crop is planted. This can be done by broadcasting the crop protection agent over the field or by using a targeted application method like banding or in-furrow application.

[0034] The crop protection agent can be applied using crop seed treatment. The crop protection agent can be applied directly to crop seeds before planting to protect them from harmful organisms in the soil.

[0035] The crop protection agent can be applied using aerial application. In some cases, especially in large fields or orchards, crop protection agents are applied from the air using drones or aircraft.

[0036] The prediction of a crop protection agent’s efficacy is made with the help of a model.

[0037] A “model” is a simplified representation or abstraction of a real-world system, process, or phenomenon. Models are designed to simulate, describe, or predict the behaviour and interactions of complex systems based on a set of assumptions, principles, and input data. The model of the present disclosure may include multiple sub-models. A sub-model is a model that describes one or more aspects of a system.

[0038] A sub-model may receive data from one or more other sub-models as input data and perform calculations based on the received data and / or perform calculations based on input data and deliver the results of the calculations to one or more other sub-models, which use these as input data for further calculations.

[0039] The model of the present disclosure may be a mechanistic model or may comprise one or more mechanistic sub-models.

[0040] A mechanistic model aims to represent mechanisms and interactions within a system using mathematical equations, physical laws, and / or biological principles. A mechanistic model is based on explicit knowledge of underlying processes and aims to represent the causal relationships within a system.

[0041] The model of the present disclosure may be a data-driven model or comprise one or more data-driven sub-models.

[0042] A data-driven model (such as a machine-learning model) is a computational algorithm or mathematical framework that learns patterns and relationships from data, enabling it to make predictions, identify trends, or perform tasks without being explicitly programmed to do so. A machine learning model is trained on training data to produce specific output data based on specific input data.

[0043] The model of the present disclosure can be or comprise a hybrid model. A “hybrid model” can combine a mechanistic model with a data-driven model. This integration allows for a more comprehensive representation of complex systems, incorporating both domain knowledge and observed data to improve predictive accuracy.

[0044] The model is configured to determine an amount of a crop protection agent in one or more parts of a crop in a target area based on input data.

[0045] The model may describe the uptake of a crop protection agent by a crop.

[0046] Synonymous terms for the term “describe” are, for example, the terms “calculate” and / or “simulate” and / or “model”.

[0047] The model may describe the distribution of the crop protection agent in the soil in which the crop is grown.

[0048] The model may describe the uptake of the crop protection agent via the roots of the crop and / or via the leaves of the crop.

[0049] The model may describe the distribution of the crop protection agent in the crop.

[0050] The model may describe the transport of the crop protection agent via the roots and the stem to the leaves and / or the fruit.

[0051] The model may describe the transport of the crop protection agent via the leaves to other parts of the crop, e.g., the fruits.

[0052] The model may describe the excretion of the crop protection agent via the leaves.

[0053] The model may describe the application process of the crop protection agent / product. The model may describe the distribution of the crop protection agent / product after it has emerged from at least one nozzle in the direction of the crop and / or in the direction of the soil in which the crop is growing.

[0054] The model may describe processes that have an influence on the distribution of the crop protection agent in the crop. For example, the model may describe the growth of the crop and / or the weather and / or the transport of water and solutes and / or the transport of heat in the soil in which the crop is growing.

[0055] The model may describe the degradation (e.g. chemical degradation and / or metabolic degradation) of the crop protection product in or on one or more parts of the crop and / or in the soil.

[0056] In an embodiment of the present disclosure, the model comprises a soil water flow model.

[0057] A “soil water flow model” describes how water moves through the soil. It takes into account factors such as soil texture and structure, soil porosity, and / or the initial moisture content of the soil. It may also consider the impact of gravity and capillary forces, as well as evaporation and / or crop uptake of water. The soil water flow model can help predict how quickly water will infdtrate the soil, how it will be distributed within the soil profde, and how much will be available for crop use or will drain away.

[0058] In an embodiment of the present disclosure, the model comprises a solute transport model.

[0059] A solute transport model describes how dissolved substances (solutes) move with the water flow in the soil. Solutes can include nutrients (like nitrogen and phosphorus), crop protection agents and further ingredients of the crop protection product, and / or pollutants. The solute transport model may consider processes such as advection (movement of solutes with the water flow), dispersion (spreading of solutes due to variations in water flow paths), and reactions (such as adsorption, decay, or transformation of solutes). The solute transport model may help predict the fate and transport of solutes in the soil, which is important for nutrient management, application of crop protection agents, and pollution control.

[0060] The soil water flow model and the solute transport model are often used together because the transport of solutes is often closely linked to the water flow.

[0061] Soil water flow models and the solute transport models are known and described in the state of the art. One example is the SWAP model. SWAP (Soil-Water-Atmosphere-Plant) simulates transport of water, solutes, and heat in the vadose zone in interaction with vegetation development (see, e.g., J.G. Kroes et al.: SJT P version 4: Theory description and user manual, 2017, ISSN 1566-7197).

[0062] In an embodiment of the present disclosure, the model comprises a plant uptake model.

[0063] A plant uptake model describes the uptake of water, nutrients, crop protection agents and / or further substances by the crop. A first step involves modelling the adsorption of the crop protection agent onto the crop surface (for foliar applied crop protection agents) or the absorption of the crop protection product by the crop roots (for soil applied crop protection agents). This process can be influenced by various factors such as the properties of the crop protection agent (e.g., solubility, lipophilicity), the properties of the crop (e.g., leaf surface characteristics, root structure), and environmental conditions (e.g., temperature, moisture). Adsorption and absorption can be modelled using empirical relationships and / or mechanistic models based on diffusion and mass transfer principles.

[0064] Examples of models that simulate pesticide uptake by plants include the Pesticide Root Zone Model (see, e.g., https: / / esdac.jrc.ec.europa.eu / projects / przmsw), and the DynamiCROP model (see, e.g., https : / / dynamicrop . org / model .php) .

[0065] In an embodiment of the present disclosure, the model comprises a plant growth model.

[0066] A “plant growth model” is a mathematical and / or computational representation of biological processes that contribute to the growth and development of a plant (e.g., the crop). Plant growth models can be used to simulate and / or predict plant responses to various environmental conditions and / or management practices. Plant growth models can range from simple empirical models, which may be based on observed relationships between growth and environmental factors, to complex process-based models, which simulate the underlying physiological processes in detail. In context of the present disclosure, the plant growth model may be used to describe the phenology, biomass accumulation, biomass partitioning, and / or stress response of the crop.

[0067] “Phenology” refers to the timing of key stages in the crop’s life cycle, such as germination, leaf emergence, flowering, and / or maturity. The timing of these stages can be influenced by factors such as temperature, day length, water / and or nutrient availability.

[0068] “Biomass accumulation” represents the growth of the crop in terms of its dry weight. It may be modelled as a function of photosynthesis (which is influenced by factors such as light, temperature, and carbon dioxide concentration) and respiration (the metabolic process that uses energy and produces carbon dioxide).

[0069] “Biomass partitioning” refers to how the accumulated biomass is distributed among different crop parts, such as leaves, stems, roots, and / or fruits. The partitioning can change over time and can be influenced by factors such as the crop’s developmental stage and / or environmental conditions.

[0070] “Stress response” represents how the crop’s growth and development are affected by stress factors, such as drought, nutrient deficiency, pests, and / or diseases.

[0071] Plant growth models are known and described in the state of the art. One example is the WOFOST model. The WOFOST (WOrld FOod STudies) model is a widely used, dynamic, process-based simulation model for the growth and yield of annual field crops. Developed by the Centre for World Food Studies in the Netherlands, WOFOST can be used to study the effects of different environmental conditions and management practices on crop growth and productivity (see, e.g.: A.J.W. de Wit et al. '. System description of the WOFOST 7.2 cropping systems model, Wageningen Environmental Research, May 20209. WOFOST simulates the growth and development of a crop based on the daily weather data, soil and crop characteristics, and management practices.

[0072] In an embodiment of the present disclosure, the model comprises a translocation and metabolization model.

[0073] The translocation and metabolization model describe what happens to a crop protection agent once it has been taken up by a crop.

[0074] Once inside the crop, the crop protection agent is distributed to various crop tissues. This can be modelled using compartment models, for example, where the crop is divided into different compartments (e.g., root, stem, leaf, fruit), and the movement of the crop protection agent between compartments may be simulated based on mass transfer principles.

[0075] “Metabolization”, also known as biotransformation, is the process by which a crop modifies a crop protection agent, often to a less toxic form that can be more easily excreted by the crop. The crop protection agent can undergo various biotransformation reactions inside the crop including hydrolysis, oxidation, reduction, and conjugation. These reactions can be modelled using reaction kinetics, where the rate of reaction depends on the concentration of the crop protection agent and the availability of enzymes. The specific reaction pathways and kinetics can often be determined from laboratory experiments.

[0076] The transformed crop protection agent (metabolites) can be excreted from the crop and / or further degraded. This can also be modelled using reaction kinetics and mass transfer principles, for example.

[0077] All these processes occur over time and can be influenced by various factors such as crop growth stage, environmental conditions, and / or crop protection agent properties. The model is therefore preferably dynamic and capable of simulating changes overtime.

[0078] Translocation and metabolization models are known and described in the state of the art (see, e.g., R.E. Hoagland et al.: Pesticide Metabolism in Plants and Microorganisms: An overview, ACS Symposium Series; American Chemical Society: Washington, DC, 2000). In an embodiment of the present disclosure, the model comprises a degradation model that describes how quickly the crop protection agent degrades.

[0079] “Degradation” refers to the process by which chemical compounds, such as active ingredients in pesticides, herbicides, fungicides and / or other products break down and transform over time when exposed to environmental factors. Degradation can occur through various mechanisms, including chemical, physical, and biological processes, leading to the reduction of the compound’s concentration and activity.

[0080] The degradation model can, for example, describe the degradation of the crop protection agent as a function of the conditions prevailing in the target area (weather (e.g. air temperature, humidity, solar radiation), soil conditions (e.g., soil temperature, soil moisture), and as a function of the composition of the product (e.g., formulation type, composition), whereby the conditions prevailing in the target area and the composition of the product may be used as input data in the model.

[0081] Degradation models are known and described in the state of the art (see, e.g., S. Beulke et al. -. Evaluation of methods to derive pesticide degradation parameters for regulatory modelling, Biol Fertil Soils 33, 2002, 558-564; M. J. Loos: Modeling of pesticide biodegradation in soil, Master Thesis, ETH Zurich, https: / / doi.org / 10.3929 / ethz-a-006121570).

[0082] The model of the present disclosure may comprise a distribution model. Such a “distribution model” may describe the distribution of the crop protection agent during and / or after the application of the crop protection agent in the target area.

[0083] For example, the distribution model can use application information (e.g., information on the type of application and / or on the nozzles used and / or on the spray pressure and / or on other / additional application parameters) and / or weather information (e.g. wind direction and / or wind speed and / or precipitation) and / or destination information (e.g., soil characteristics, topology, topography) as input data and use this information (and / or other / further information) to calculate how the crop protection product comprising a crop protection agent spreads in the target area.

[0084] Distribution models are described in the state of the art (see, e.g., F. van den Berg et al;. PEARL model for pesticide behaviour and emissions in soil-plant systems, WOt-technical report 61, 2016, ISSN 2352-2739; H. Dou et al . Computational model of pesticide deposition distribution on canopies for air-assisted spraying, Front. Plant Sci., 2023, Volume 14).

[0085] The model of the present disclosure may comprise an environmental fate model. An “environmental fate model” simulates the behaviour of crop protection agent in the environment, including processes such as degradation, leaching, volatilization and run-off. These models are designed to assess the fate and transport of active ingredients and their breakdown products in various environmental compartments, such as soil, water, air, and vegetation (see, e.g., A. Di Guardo et al.: Environmental fate and exposure models: advances and challenges in 21st century chemical risk assessment, Environ. Sci.: Processes Impacts, 2018, 20, 58-71; N. Suciu et al.: Environmental Fate Models, in: Global Risk-Based Management of Chemical Additives II: Risk-Based Assessment and Management Strategies, Chapter: Environmental fate models, Springer, pp. 47-71).

[0086] The model can be or include a residue and / or metabolism model. Such a model may predict the formation and behaviour of crop protection agent residues in crops, soil, and / or the environment. Such a model may provide information on residue levels, degradation rates, and / or the potential for carryover into subsequent crops (see, e.g., M. Ebeling, K. Hammel: Evaluating plant residue decline data with KinGUII and TREC: results from case studies involving also non-SFO kinetic models, Environ Sci Eur 32, 2020, 116).

[0087] The models listed here are only examples. There may be further / other models.

[0088] Fig. 1 shows an exemplary and schematic embodiment of the model of the present disclosure. A crop C is represented by four compartments CR, CS, CL, and CF. The root compartment CR stands for the roots of the crop, the stem compartment CS for the stem of the crop, the leaf compartment CL for the leaves of the crop, and the fruit compartment CF for the fruits of the crop. The term “fruit” is to be interpreted broadly and also includes, for example, the grains in cereals.

[0089] The temporal growth of the crop and the growth stages of the crop can be modelled with the help of a plant growth model.

[0090] Plant C is in contact with soil S via the roots. A soil water flow model can describe how water moves through the soil S. A solute transport model can describe how dissolved substances (solutes) move with the water flow in the soil S.

[0091] Water and nutrients can be absorbed via the roots. An uptake model can describe the uptake of water and nutrients via the roots.

[0092] The weather W has an influence on the amount of water that gets into the soil S. The weather W has an influence on the growth of crop C. A weather model can predict solar radiation, temperatures and / or precipitation over time.

[0093] Application data describe the application A of a crop protection agent. The crop protection agent can wet the leaves and / or fruits of crop C and / or get into the soil S. The application of the crop protection agent can be modelled with an application model.

[0094] Crop protection agent that gets onto the leaves and / or fruit of crop C can be washed off by rain and get into the soil S.

[0095] The distribution of the crop protection agent after application can be described with the help of a distribution model.

[0096] The crop protection agent can be taken up by crop C via the leaves of crop C, via the fruits of crop C and / or via the roots of crop C. The uptake of the crop protection agent by crop C can be modelled with the help of a plant uptake model.

[0097] Crop protection agent that gets into crop C can spread throughout the crop. Crop protection agent that enters crop C can be metabolized. Crop protection agent and / or metabolites of the crop protection agent can be excreted by crop C. The translocation, metabolization and excretion of the crop protection agent can be modelled with the help of a translocation and metabolization model.

[0098] The degradation of the crop protection agent can be described with the help of a degradation model.

[0099] The input data on the basis of which the model determines the amount of a crop protection agent in one or more parts of a crop, includes agent data, crop data and application data. The input data may include additional data (see below).

[0100] “Agent data” specifies the crop protection agent for which a prediction is to be made using the model.

[0101] Agent data may specify the active ingredient itself and / or the crop protection product containing the active ingredient.

[0102] The crop protection agent may be specified by a name (e.g., IUPAC name, trivial name, brand name) and / or a unique identifier (e.g., CAS number).

[0103] Agent data may include a chemical formula of the crop protection agent, e.g. in the form of a molecular formula and / or a structural formula.

[0104] The structural formula can, for example, be given as SMILES, InChi or WLN representation. The simplified molecular-input line-entry system (SMILES) is a specification in the form of a line notation for describing the structure of chemical species using short ASCII strings. The IUPAC International Chemical Identifier (InChi) is a textual identifier for chemical substances. Wiswesser line notation (WLN) was one of the first line notations capable of precisely describing complex molecules. The structural formula can, for example, be given as molecular graph. A molecular graph is a representation of the structural formula of a chemical compound in terms of graph theory. A molecular graph can be a labelled graph whose vertices correspond to the atoms of the compound and edges correspond to chemical bonds. Its vertices can be labelled with the kinds of the corresponding atoms and edges can be labelled with the types of bonds. Molecular graphs can be represented by matrices, such as adjacence matrices, for example.

[0105] Agent data may include a (molecular) fingerprint of the crop protection agent, such as Morgan fingerprint, also known as extended-connectivity fingerprint ECFP4, MinHashed fingerprint MHFP6, MAP4, RDKit AP, Molecular ACCess Systems keys fingerprint (MACCS), and PubChem Fingerprints (PubChemFP), AtomPairs2DFingerprint (APFP), GraphOnlyFingerprint (GraphFP), Natural Compound Molecular Fingerprint (NC-MFP) and / or others.

[0106] The crop protection agent may be specified by physical and / or chemical properties. Thus, agent data may comprise one or more physical and / or chemical properties of the crop protection agent.

[0107] Such physical and / or chemical properties can be, for example, molecular weight, water solubility, partition coefficient (e.g., for octanol / water: Kow), melting point (e.g., at standard conditions), proportion of polar / apolar functional groups, pKa / pKb values, pH of the crop protection agent in an aqueous solution, DT50value, soil organic carbon / water partitioning coefficient Koc, transpiration stream concentration factor TSCF and / or other / fiirther physical and / or chemical properties.

[0108] The DT50 value is a quantitative measure of the rate of degradation of crop protection agents in the environment. DT stands for dissipation time or disappearance time, the numerical value 50 stands for a reduction of the originally present quantity by 50 %. In the case of first-order degradation, a DT50 value corresponds to the half-life. A DT90 value indicates a 90% decrease in the starting material.

[0109] Soil organic carbon / water partitioning coefficient Koc (adsorption coefficient) is a ratio of the mass of a chemical that is adsorbed in the soil per unit mass of organic carbon to its concentration in dilute aqueous solution, at equilibrium.

[0110] The transpiration stream concentration factor (TSCF) is the ratio of a chemical concentration in the xylem sap to the concentration of that chemical in the solution passively transferred to leaves.

[0111] The physical and / or chemical properties can be measured values. The physical and / or chemical properties can be read from one or more databases and / or determined from the literature. Examples of databases that store physical and / or chemical properties include PubChem (https: / / pubchem.ncbi.nlm.nih.gov / ), NIST Chemistry WebBook

[0112] (https: / / webbook.nist.gov / chemistry / ), and CRC Handbook of Chemistry and Physics (https: / / hbcp.chemnetbase.com / ). The physical and / or chemical properties can be determined experimentally.

[0113] The physical and / or chemical properties can be or comprise calculated values. For example, the physical and / or chemical properties can be calculated based on the molecular structure of the crop protection agent. There are numerous methods for calculating physical and / or chemical properties based on molecular structure. These can be found in the literature under the name QSPR (Quantitative Structure Property Relationship), among others. There are also commercially and freely available computer programs for calculating physical and / or chemical properties of substances based on their molecular structure (e.g..: QSAR-Co: J. Chem. Inf. Model. 2019, 59, 6, 2538-2544; Molgen-QSPR: https: / / www.researchgate.net / publication / 266470632; RDKit: https: / / www.rdkit.org).

[0114] For example, a procedure for calculating the decadic logarithm of the partition coefficient for n- octanol / water is described in: A. J. Leo: Calculating log Poct from structures, Chem. Rev. 1993, 93, 4, 1281-1306. A method for calculating the melting point is described, for example, in: H. Modarresi et al.: QSPR Correlation of Melting Point for Drug Compounds Based on Different Sources of Molecular Descriptors, J. Chem. Inf. Model. 2006, 46, 2, 930-936. For example, a method for calculating aqueous solubility is described in: N. Meftahi et al.: Predicting aqueous solubility by QSPR modeling, Journal of Molecular Graphics and Modelling, Volume 106, July 2021, 107901. Additional methods for calculating physical and / or chemical properties can be found in: A.R. Katritzky et al.: QSPR as a means of predicting and understanding chemical and physical properties in terms of structure, Pure and Applied Chemistry 69(2):245-248).

[0115] Agent data may comprise the composition information of the crop protection product, e.g., by one- hot encoding as described in WO2023078914A1 or in any other way. The composition information indicates the ingredients and their proportion in the crop protection product.

[0116] Agent data can be provided by the manufacturer of the crop protection agent (e.g., in the form of a leaflet accompanying the crop protection product and / or via a website and / or other means).

[0117] Agent data may be manually entered into the computer system of the present disclosure by a user. The term “enter” is also understood to mean selecting an item from a menu and / or clicking a tick box.

[0118] The computer system may be configured to obtain additional agent data based on agent data provided by the user, for example by reading the additional agent data from one or more databases, which may be components of the computer system of the present disclosure or may be connected to the computer system via a network.

[0119] “Crop data” specifies the crop that is cultivated in the target area.

[0120] Crop data may include information on the crop species and / or the crop variety. Crop data may include information on whether and / or how the crop has been genetically modified. Crop information may include information about the stage of development of the crop and / or when it was planted or sown and / or whether and / or when it was fertilized and / or whether and / or when it was irrigated.

[0121] Crop data may specify the seed with which the crop was grown.

[0122] Crop data may include information about the planting process of the crop such as information of the planting pattern (e.g. in-row), the distance between planted crops (e.g. the spacing between individual plants within a row or between rows), the planting depth (the depth at which seeds or seedlings are planted in the soil), the seed or seedling density (the number of seeds or seedlings planted per unit area), soil preparation (e.g. factors such as soil fertility, pH, and moisture levels etc.), planting time, planting method (e.g. direct seeding versus transplanting seedlings etc.).

[0123] Crop data may include information about which crops were previously grown in the area. It is possible, for example, that a crop rotation was carried out in the area. “Crop rotation” refers to the practice of systematically planting different crops in the same area over a sequence of growing seasons. This method involves alternating the types of crops grown in a specific field to achieve various agronomic, economic, and / or environmental benefits. Crop rotation aims to improve soil health, manage pests and / or diseases, and / or optimize nutrient utilization. By rotating crops, farmers can disrupt pest and / or disease cycles, reduce soil erosion, enhance soil fertility, and / or minimize the need for chemical inputs such as fertilizers and / or pesticides. Additionally, crop rotation can contribute to sustainable agricultural practices by promoting biodiversity and / or reducing the risk of soil depletion.

[0124] Crop data may be provided by the user of the computer system of the present disclosure, for example by entering the information into the computer system.

[0125] The computer system may be configured to obtain additional crop data based on the crop data provided by the user, for example by reading the additional crop data from one or more databases, which may be components of the computer system of the present disclosure or may be connected to the computer system via a network. It is also possible that the crop protection agent is only intended / suitable for the treatment of a specific crop and that the crop is already specified by the agent data specifying the crop protection agent.

[0126] Crop data may also be identified and / or collected by sensors / monitoring devices (e.g., remote sensing via satellite, drones, airplanes).

[0127] Crop data may include information on the amount of biomass present in the target area.

[0128] Crop data may include a vegetation index, for example. “Vegetation indices” are numerical measures usually derived from remote sensing data that help to quantify various aspects of vegetation health, density, and physiological condition. These indices are often calculated using the spectral reflectance properties of vegetation, typically obtained through satellite or aerial imagery. Vegetation indices provide information about plant growth, health, and overall vitality. Common vegetation indices include the Normalized Difference Vegetation Index (ND VI), which is widely used to monitor plant health and assess vegetation coverage. Another important index is the Enhanced Vegetation Index (EVI), which provides improved sensitivity in high biomass regions and better performance in areas with varying atmospheric conditions. Additionally, the Soil-Adjusted Vegetation Index (SAVI) is used to minimize the influence of soil brightness in the assessment of vegetation cover.

[0129] “Application data” is any information that specifies one or more (planned) applications of the crop protection agent in the target area.

[0130] The application data usually indicates when and how much crop protection agent was or is intended to be applied to the target area.

[0131] The application data can provide information on which application method was or is intended to be used for the application of the crop protection agent in the target area (e.g., foliar application or root application or seed coating or soil treatment).

[0132] The application data may indicate what type(s) of spraying device(s) (e.g., drone, robot, boom sprayer) as / were used and / or is / are available, what spray width the spraying device(s) has / have, how many nozzles are available, what types of nozzles are available, and other / fiirther specifications.

[0133] Application data may be provided by the user of the computer system of the present disclosure. The user may enter the information into the computer system of the present disclosure. However, it is also possible that the computer system is configured to read application data from one or more databases, which may be components of the computer system, or which may be connected to the computer system via a network, based on information entered into the computer system by the user. It is possible, for example, that the user specifies an application device (e.g., a spraying device) that is available to him / her for an application of the crop protection agent in the target area and the computer system uses the information provided by the user to determine application data associated with the application device from one or more databases (e.g., a database provided by the manufacturer of the application device).

[0134] Application data may also be provided by an application device (e.g., a spraying device) via an interface of the application device.

[0135] The input data on the basis of which the model determines the amount of a crop protection agent in one or more parts of a crop, may include destination data.

[0136] “Destination data” specifies the target area where a crop is cultivated and in which the crop protection agent was and / or is to be used.

[0137] Destination data may include geocoordinates that indicate where the target area is located. Such geocoordinates may indicate at least one point in the target area or at the edge of the target area. Such geocoordinates may specify field boundaries, i.e., the boundaries that separate an agricultural field on which a crop is cultivated from its surroundings. Destination data may include information that has a direct or indirect influence on the distribution, spread, degradation and / or movement of the crop protection area to / from the target area where the crop protection agent is to be applied.

[0138] Destination data may include topographic data about the target area.

[0139] “Topographic data” refers to data related to the physical and natural features of the target area’s surface. This includes details about the elevation, slope, aspect, and contours of the terrain. Topographic data is important for understanding the landscape’s characteristics, drainage patterns, and / or soil properties.

[0140] Destination data may include topological data about the target area.

[0141] “Topological data” refers to data related to the spatial relationships and connectivity of features in the target area. This includes information about the arrangement, adjacency, and / or connectivity of agricultural fields, water bodies, infrastructure, and / or other landscape elements. Topological data helps in understanding how different elements within the agricultural landscape are spatially related to one another.

[0142] Destination data may include information about the type of soil in the target area. “Soil types” may be classified based on their physical and / or chemical properties, which significantly influence their suitability for different crops and agricultural practices. Some of the primary soil types include: sandy soil, clay soil, loamy soil, silt soil, peaty soil, chalky soil, silty clay soil.

[0143] Destination data may include further information about the soil in the target area, such as soil texture, pH, organic matter content, nutrient levels, cation exchange capacity, soil structure, water holding capacity, soil drainage characteristics, and / or soil depth.

[0144] Information on the soil can be obtained from soil samples, for example. Soil samples can be analysed in a laboratory and / or on site to determine the soil type and properties (see, e.g., Guidelines for soil description, 4th ed., Food and Agriculture Organization of the United Nations, Rome 2006, ISBN 92-5-105521-1).

[0145] Information on the soil can also be obtained from one or more sensors located in the target area, such as soil moisture sensors, soil nutrient sensors, soil pH sensors, soil salinity sensors, soil temperature sensors and / or other / further sensors.

[0146] Destination data may include static and dynamic data. Static data is data that does not change significantly over the course of a growing season. Static data includes, for example, topographic data, topological data, and soil type. Dynamic data is data that changes significantly over the course of a growing season. Dynamic data includes, for example, biomass, soil temperature and soil moisture.

[0147] Dynamic data can be captured with the aid of sensors, which can be located in or above the target area.

[0148] Static and / or dynamic destination information may be obtained using remote sensing.

[0149] “Remote sensing” refers to the use of various technologies, such as satellites, drones, and / or aircraft, to collect information about the Earth's surface without direct physical contact.

[0150] Remote sensing can assess the health and vigor of crops by measuring factors such as chlorophyll content, leaf area index, and biomass, which can indicate plant stress, nutrient deficiencies, and / or disease.

[0151] Remote sensing can track the growth stages of crops, providing insights into development, maturity, and potential yield estimates. Remote sensing data can contribute to plant growth models and / or yield prediction models by monitoring crop conditions and growth patterns. Remote sensing can estimate soil moisture levels. Remote sensing can help identify areas of water stress in crops.

[0152] Remote sensing can provide detailed information about the types of crops being grown, as well as changes in land use patterns over time.

[0153] Remote sensing can assess environmental factors such as erosion, deforestation, and habitat loss.

[0154] Destination data is typically provided by the user of the computer system of the present disclosure. The user may enter the destination data into the computer system of the present disclosure.

[0155] It is also possible that the computer system of the present disclosure is configured to read destination data from one or more databases after geocoordinates for the target area are provided (e.g., by a user). Destination data may be obtained from one or more databases, such as the Soil Survey Geographic Database (SSURGO).

[0156] It is also possible for the destination data to be captured by one or more sensors (e.g., local sensors and / or remote sensing) and automatically transmitted to the computer system of the present disclosure.

[0157] One or more sensors may be present in the target area that measure destination data such as soil temperature, air temperature, soil moisture, humidity, air pressure and / or the like and transmit the measured values to the computer system of the present disclosure (e.g., via a network) at defined times and / or upon defined events. Air pressure, air temperature and humidity can, for example, be recorded at a defined distance from the ground (e.g., 1 meter).

[0158] Destination data may also be collected by satellites and / or drones and / or airplanes and / or robots and / or sensors on agricultural machinery moving through the target area.

[0159] Destination data can be mean values (e.g., arithmetic mean values) that are averaged over the target area. Destination information can also be spatially resolved information in which individual subareas are assigned information that characterizes these sub-areas.

[0160] Destination data can be average values over a certain period of time (e.g., one day or one week or one month or one quarter or half a year or one year or several years). Destination data can also contain time-resolved values. Destination data can also contain maximum and / or minimum values (e.g., daily maximum temperature and / or daily minimum temperature).

[0161] The input data on the basis of which the model determines the amount of a crop protection agent in one or more parts of a crop, may include weather data.

[0162] “Weather data” refers to data and / or forecasts related to atmospheric conditions, including temperature, precipitation, humidity, wind speed and direction, solar radiation, and / or other meteorological parameters in the target area.

[0163] Weather information may be obtained from various sources, including government meteorological agencies, private weather services, and / or localized weather stations.

[0164] Weather information may be obtained using one or more sensors in the target area, such as temperature sensors, humidity sensors, sensors for determining the amount of precipitation, anemometers, air pressure meters, radiation sensors and / or other / further sensors.

[0165] It is also possible that the computer system of the present disclosure is configured to read weather data from one or more databases after the geocoordinates for the target area have been provided (e.g., by a user). The weather data may include historical data; the weather data may include forecast data. The forecast data may be updated. Preferably, weather data is available over at least the time period for which an amount of the crop protection agent is to be determined in one or more parts of the crop. With the help of the input data and the model an amount of the crop protection agent in one or more parts of the crop at one or more points in time is determined.

[0166] The input data is entered into the model, and the model provides as output data an amount of the crop protection agent in one or more parts of the crop at one or more points in time.

[0167] The one or more points in time may be points in time that lie at a certain distance from one or more applications of the crop protection agent.

[0168] If the application of the crop protection agent involves the treatment of seed with the crop protection agent, the one or more points in time may also be at a certain distance from the sowing of the seed.

[0169] The certain distance can be, for example, one day or several days, one week or several weeks, one month or several months.

[0170] In an embodiment of the present disclosure, the model is configured to output a time course of an amount of a crop protection agent in one or more parts of the crop. Preferably, the time course starts with an application of a crop protection agent (and / or the sowing of a seed). Preferably, the time course starts with the first application of a crop protection agent within a current vegetation period. Preferably, the time course has a length of several days, even more preferably several weeks. Preferably, the time course ends with a target harvest day or with a predicted end of a predicted pest and / or disease period and / or when the amount has fallen below a predefined threshold value. The predefined threshold value can, for example, be the amount of a crop protection agent below which no effect on a harmful organism is observed or below which a desired effect on a harmful organisms cannot be observed.

[0171] In an embodiment of the present disclosure, the one or more part of the crop comprises the leaves of the crop.

[0172] In an embodiment of the present disclosure, the one or more part of the crop comprises the fruits of the crop.

[0173] The amount of the crop protection agent in one or more crop parts can be, for example, a concentration. The concentration may be, for example, the mass of the crop protection product in relation to the mass of the crop part. The mass of the crop part may be the dry mass or the mass of a freshly prepared sample.

[0174] Based on the determined amount, an efficacy of the crop protection agent at the one or more points in time is determined.

[0175] The efficacy of a crop protection agent is defined as its ability to control, suppress, repel or eliminate the target pest (one or more harmful organism) including the ability to control, suppress or eliminate one or more diseases or - in case of a biostimulant - the ability to foster plant health. It is a measure of the effectiveness of the crop protection agent in achieving its intended purpose.

[0176] The determined efficacy is intended to express whether the crop protection agent (still) fulfils the intended purpose at one or more points in time.

[0177] The determined efficacy can be a yes-or-no statement. The determined amount of the crop protection agent in one or more crop parts can be compared with a predefined value obtained from a database. The predefined value in the database may indicate the minimum amount that must be present in the one or more crop parts in order for the crop protection agent to fulfil its desired purpose. If the comparison shows that the determined amount is below the predefined value, the determined efficacy may contain the statement that the crop protection agent has no effect (efficacy: no). If the comparison shows that the determined amount is equal to the predefined value or above the predefined value, the determined efficacy can contain the statement that the crop protection agent has an effect / the desired effect (efficacy: yes). Instead of a binary statement (yes / no), the efficacy can also be a graded statement, e.g. “no effect, low effect, maximum effect” or “no effect to low effect, sufficient to good effect, very good effect”. In such a case, the determined amount can be compared with several predefined values.

[0178] Predefined values can be determined experimentally and / or provided by the manufacturer of the crop protection agent.

[0179] It is also possible that there is an upper threshold value that should not be exceeded. It is possible that an excessive amount of crop protection agent could damage the crop and / or impair the quality of the harvest. It is possible that the determined amount is compared with a predetermined maximum amount and, if the predetermined maximum amount is exceeded, a warning is issued that the amount of crop protection agent in one or more parts of the crop has reached / will reach a level that could have negative effects (e.g. cytotoxic effects) on the crop and / or the harvest.

[0180] Efficacy can also be expressed as a number.

[0181] The number can be representative of a category, e.g. “0” for no effectiveness and “1” for a desired efficacy.

[0182] However, it is also possible that the number indicates the efficacy in a unit in which the efficacy can be measured and / or is usually stated.

[0183] Efficacy can indicate, for example, the percentage of harmful organisms successfully controlled (e.g., the percentage of insects killed).

[0184] Efficacy can indicate, for example, the percentage of crop parts that are not infested by harmful organisms.

[0185] Efficacy can indicate, for example, the expected yield of the crop. “Crop yield” usually refers to the quantity of a crop that is harvested per unit of land area. It is a measure of the productivity of a crop in a given area and is usually expressed in units such as kilograms per hectare, bushels per acre, or tons per acre.

[0186] Efficacy can indicate, for example, the quality of the crop and / or crop parts (e.g., colour, size, and / or content of one or more nutrients in the crop parts).

[0187] The correlation between the determined amount of the crop protection agent and the efficacy can be determined empirically, for example.

[0188] In an alternative embodiment efficacy can indicate whether one or more parts of the crop (such as pollen, nectar) have a toxic concentration for pollinators such as bees. In this context, if the determined efficacy is e.g. above a predefined threshold in the (pollinator relevant) one or more parts of the crop it should be avoided that pollinators are pollinating crops in the target area. If e.g. the determined efficacy is beneath the predefined threshold pollinators can pollinate crops in the target area.

[0189] The determined efficacy can be outputted, e.g., displayed on a monitor and / or printed out on a printer and / or stored in a data memory and / or transferred to a separate computer system.

[0190] The determined efficacy can be used to give a user (e.g. a farmer) - in line with the required regulations - a recommendation as to when the farmer should make one or more applications of a crop protection agent, which crop protection agent(s) the farmer should use and / or what amount(s) of the crop protection product(s) the farmer should apply one or more times.

[0191] EXAMPLES

[0192] Fig. 2 shows an example of biomass development overtime in the form of a graph. The course of the biomass was calculated using a plant growth model. In the graph, the time in days is plotted on the x-axis (abscissa). The time begins on November 1. The dry mass of parts of a crop per square meter of target area is plotted on the ordinate (y-axis). The crop is winter wheat. The crop parts are roots, stem, leaves, and fruits.

[0193] The calculations are based on the humid central European FOCUS groundwater scenario Hamburg (soil and weather).

[0194] The graph illustrates the typical development of the above-ground biomass (ABG) of winter wheat: low biomass in winter and early spring, exponential growth in mid-spring, linear growth until early summer, then decline in living ABG biomass due to leaf and stem decay.

[0195] The living biomass of the plant compartments follows the distribution of assimilates, moving from the vegetative (— > root, stem, leaf) to the reproductive phase (— > fruit).

[0196] The final fruit mass (= grain mass) (8.5 t / ha) corresponds well with the yield statistics for this region (7.9 t / ha).

[0197] Fig. 3 shows an example of the result of linking a plant growth model with an uptake model and a translocation and metabolization model. The plant growth model is the model whose calculation results are shown in the form of a graph in Fig. 2.

[0198] The amounts of a crop protection agent in different parts of a crop were calculated as a function of time. Fig. 3 shows the result of the calculations in the form of a graph. In the graph, the amounts of the crop protection agent are plotted as a function of time. The amounts are the concentrations of the crop protection agent in freshly prepared samples of roots, stem, leaves, and fruit. The time axis in Fig. 3 corresponds to the time axis shown in Fig. 2. The crop is winter wheat.

[0199] The agent data are:

[0200] DT50 (in soil): 100 days.

[0201] Koc: 100 L / kg

[0202] Ig(Kow): 0.8

[0203] TSCF: 0.5

[0204] The application data of the application of the crop protection agent are:

[0205] 100 g / ha at emergence of winter wheat in soil. The crop protection agent is a systemic crop protection agent applied to soil.

[0206] As shown in Fig. 3, the crop protection agent is continuously taken up from the soil, roughly in proportion to the development of the biomass (not shown).

[0207] The crop protection agent passes relatively quickly through the root and stem and accumulates in the leaves as terminal compartment for transpiration-induced xylem transport.

[0208] Thereafter, leaf concentration decreases due to the dilution of growth during the exponential growth phase.

[0209] The reproductive phase begins in mid-May: the growing fruits lead to translocation of the crop protection agent from the leaves to fruits by the phloem flow.

[0210] The efficacy of the crop protection agent for the different parts of the crop can be deduced from the amounts of the crop protection agent in the different parts of the crop over time.

[0211] Fig. 4 shows an example of the result of linking a plant growth model with an uptake model and a translocation and metabolization model. The plant growth model is the model whose calculation results are shown in the form of a graph in Fig. 2.

[0212] The amounts of a crop protection agent in different parts of a crop were calculated as a function of time. Fig. 4 shows the result of the calculations in the form of a graph. In the graph, the amounts of the crop protection agent are plotted as a function of time. The amounts are the concentrations of the crop protection agent in freshly prepared samples of roots, stem, leaves, and fruit. The time axis in Fig. 4 corresponds to the time axis shown in Fig. 2. The crop is winter wheat.

[0213] The agent data are: DT50 (in soil): 100 days.

[0214] Koc: 100 L / kg

[0215] Ig(Kow): 0.8

[0216] TSCF: 0.5

[0217] The application data of the application of the crop protection agent are: 100 g / ha applied once at the reproductive phase of the crop. The crop protection agent is a systemic crop protection agents applied to the canopy of the crop.

[0218] As shown in Fig. 4, the crop protection agent is quickly taken up and exhibits a distinct leaf concentration peak. The leaf concentration declines continuously due to growth dilution and later due to phloem transport of the crop protection agent to the fruit via the stem. The efficacy of the crop protection agent for the different parts of the crop can be deduced from the amounts of the crop protection agent in the different parts of the crop over time.

Claims

CLAIMS1. A computer-implemented method comprising the steps: providing a model, wherein the model is configured to determine an amount of a crop protection agent in one or more parts of a crop based on input data, wherein the input data comprises agent data, application data and crop data, receiving agent data, application data, and crop data wherein agent data specifies the crop protection agent, wherein the application data specifies an application of the crop protection agent at one or more points in time in a target area, and wherein the crop data specifies the crop that is cultivated in the target area, determining an amount of the crop protection agent in one or more parts of the crop cultivated in the target area at one or more points in time using the model, determining an efficacy of the crop protection agent at the one or more points in time based on the determined amount, outputting the determined efficacy.

2. A computer-implemented method according to claim 1, wherein the model configured to determine an amount of a crop protection agent in one or more parts of a crop based on input data comprises sub-models.

3. A computer-implemented method according to claim 2, wherein the sub-models comprise one or more mechanistic sub-model(s) and / or one or more data-driven sub-model(s).

4. A computer-implemented method according to claim 3, wherein the mechanistic sub-model is selected from the group of: soil water flow model, solute transport model, plant uptake model, plant growth model, translocation and metabolization model, degradation model, distribution model, environmental fate model, residue and / or metabolism model.

5. A computer-implemented method according to claim 4, wherein the mechanistic sub-models used are at least a plant growth model, a plant uptake model and a translocation and metabolization model.

6. A computer-implemented method according to one of the claims 4 and 5, wherein at least the translocation and metabolization model is dynamic and capable of simulating changes over time.

7. A computer-implemented method according to one of the preceding claims wherein the crop protection agent is a systemic crop protection agent.

8. A computer-implemented method according to claim 7, wherein the crop protection agent is a systemic crop protection agent applied to the soil and wherein the model preferably comprises as a mechanistic sub-model also a soil water flow model and / or a solute transport model.

9. A computer-implemented method according to one of the preceding claims wherein the input data comprises destination data which specifies the target area.

10. A computer-implemented method according to claim 9, wherein the destination data at least comprises information about the geocoordinates of the target area.

11. A computer-implemented method according to one of the preceding claims, wherein determining an amount of the crop protection agent in one or more parts of the crop cultivated in the target area at one or more points in time by using the model comprises the determination of a concentration.

12. A computer-implemented method according to claim 11, wherein the concentration of the crop protection agent comprises the determination of the mass of the crop protection product in relation to the mass of the one or more part of the crop.

13. A computer-implemented method according to one of the preceding claims wherein determining an efficacy of the crop protection agent at the one or more points in time based on the determined amount comprises a comparison of the determined amount of the crop protection agents in one or more parts of the crop with a predefined value and the assessment whether the determined amount is below, equal or above the predefined value.

14. A computer system comprising: a processor; and a memory storing an application program comprising a model configured to determine an amount of a crop protection agent in one or more parts of a crop based on input data, wherein the input data comprises agent data, application data and crop data; the application program configured to perform, when executed by the processor, an operation, the operation comprising: receiving agent data, application data, and crop data wherein agent data specifies the crop protection agent, wherein the application data specifies an application of the crop protection agent at one or more points in time in a target area, and wherein the crop data specifies the crop that is cultivated in the target area, determining an amount of the crop protection agent in one or more parts of the crop cultivated in the target area at one or more points in time using the model, determining an efficacy of the crop protection agent at the one or more points in time based on the determined amount, outputting the determined efficacy.

15. A non-transitory computer readable storage medium having stored thereon software instructions that, when executed by a processor of a computer system, cause the computer system to execute the following steps: receiving agent data, application data, and crop data wherein agent data specifies the crop protection agent, wherein the application data specifies an application of the crop protection agent at one or more points in time in a target area, and wherein the crop data specifies the crop that is cultivated in the target area, determining an amount of the crop protection agent in one or more parts of the crop cultivated in the target area at one or more points in time using a model, wherein the model is configured to determine an amount of a crop protection agent in one or more parts of a crop based on input data, wherein the input data comprises agent data, application data and crop data, determining an efficacy of the crop protection agent at the one or more points in time based on the determined amount,outputting the determined efficacy.

Images