Conceptual Design of a Comprehensive Farm Nitrogen Management System
Abstract
:1. Introduction
1.1. Scientific Challenges
1.2. Need for Research and Development
1.3. Purpose and Objective
- -
- To describe, analyze, systemize, and evaluate the existing digital tools for N management (in crop and livestock production and on the farm level as a whole) and its various components related to system boundaries, relevant N flows and N pools, methods, and algorithms;
- -
- To assess the practicality of these systems regarding data requirements, availability, connectivity, performance, and informative value as well as the feasibility of recommendations in management processes; and
- -
- To evaluate the potential for integrating existing digital tools and their data into an FNMS (such as existing interfaces, linking components, and ensuring consistent calculations using the same primary data).
2. Materials and Methods
- (a)
- Systematization of digital models and components relevant for an FNMS due to the following aspects:
- They are required by law;
- They are widely used in operational farm management; and
- They are new conceptual approaches and innovative digital methods, such as sensor- and satellite-based N fertilization.
- (b)
- Analysis of data requirements for an FNMS and the assessment of data availability based on the following steps:
- Assessments and evaluations from the literature (self-assessment by system developers, practical tests, user reviews, scientific analyses, and comparative evaluations of digital tools);
- Experience in developing FNMS components;
- Expert workshops with farmers, consultants, and scientists; and
- (c)
- Analysis and evaluation of digital tools and their components, focusing on investigations of N fertilizer requirements and balancing.
- (d)
- Definition of an integrated FNMS with its required components and their interrelationships, as well as the goals and areas of application for a comprehensive FNMS.
3. Results and Discussion
3.1. Overview, Categorization, and Evaluation of Existing Digital Tools for N Management
3.1.1. N Requirement Calculation
- Methods for uniform fertilizer application on field level
- Methods for site-specific fertilization
3.1.2. N Balancing and N Cycle
- -
- System level and system boundary, namely the entire farm (farm-gate balance), crop production (field balance), and livestock production (barn balance);
- -
- N flows considered, such as N inputs, N outputs, and N flows within the system;
- -
- N pools considered, such as soil organic N (SON) and soil mineral N (SMN);
- -
- N balance parameters used, for example, the N contents of products and NH3 emission factors;
- -
- Algorithms used, for example, for calculating the N2-fixation;
- -
- Data basis, namely the measurement data, model data, and statistical data.
3.1.3. Soil C/N Turnover, N Uptake, and N Losses
3.2. Concept of a Farm N Management System
3.2.1. Stakeholder Requirements and Application Areas
3.2.2. Basic Functionalities and Data Requirements
3.2.3. Definition of a Farm N Management System
- Decision support in FNMS
- Decision support system requirements
- (a)
- Comprehensive access to data from individual programs or modules to combine and evaluate information in a context-related manner.
- (b)
- Evaluation methods and benchmarks (target values) to analyze results and identify optimization requirements.
- (c)
- Decision-making rules and algorithms to determine farm-specific recommendations for management and evaluate the field operations carried out by the farmers. The required algorithms and decision pathways can be modeled according to techniques from the field of decision support systems, for example best practices, decision trees, rules, or fuzzy logic. According to Fernandez-Mena et al. [123], a promising approach to optimize nutrient cycles is to combine nutrient flow models with agent-based modeling and analyze the environmental impacts. Notably, the quality of the recommendations depends heavily on the technical content (expert knowledge) or the quality of the knowledge base, the applied algorithms or their implied modeling (rule base), and the comprehensibility of the dialog component for the user.
- (d)
- A dialog component that allows users to interact with the FNMS and allows decision-relevant parameters to be given values.
3.3. Software Design for a Farm N Management System
3.3.1. Representation of Farm Structure and System Implementation
3.3.2. Software Design and Modularity
- -
- Master data component—contains data structures and functionality for managing and using master data, for example, climate data or generally applicable crop-specific data;
- -
- Site and farm data component—contains data structures and functionality for administering and using individual farm data;
- -
- Administration component—contains data structures and functionality for administering user data, user roles, and access rights.
- -
- Algorithms and calculations—completely independent of the overall system, they can be reused in further systems, and they define the necessary data requirements independent of the master data or core data model.
- -
- Model—the data model of a module for data transformation from data storage to algorithm-specific data structures. Both operational and site data, master data, and module-specific master data are processed and prepared for use in algorithms.
- -
- Data access—to access module-specific master data, for example, the database.
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Fertilization System | Basic Principle to Calculate N Requirement | Data Origin | Benefits 1 (+) | Drawbacks 1 (−) | Examples |
---|---|---|---|---|---|
1. Uniform fertilization on field level | |||||
1.1. Basic software | N requirement calculation for field level based on crop type, target quality, and target yield + Correction factors, depending on previous crop and SMN 2 content at the beginning of vegetation period | Reference values, farm or field-related data, measured values or regional averages (pre-crop, fertilization pre-crop, SMN testing 3) | +/(−) Standalone application + Mostly functionality in farm management information systems (FMISs) + Easy to use | − No consideration of soil heterogeneity, crop stand development, SMN content | [48] |
1.2. Extended software | Calculation like 1.1 + Additional soil and climate parameters + N nutrition status (N content) of the crop stand | Like 1.1. + N content of the plant biomass (analysis, simulation) 3 + Site factors (altitude, soil humus content, soil-climate area) | + Consideration of crop stand + Dynamic specification of fertilizing strategy | − Additional effort − N content measurement not representative of the entire field − No consideration of the variability in soil parameters | [49,50] |
2. Site-specific fertilization | |||||
2.1. Mapping system | Generating of fertilizer application maps on the basis of site conditions (e.g., soil texture or yield potential) | Soil maps, soil conductivity measurement, and volume flow measurement harvester (yield map), remote sensing data | + Consideration of the spatial variability in soil and crop parameters + Optimal yield and environmental relief + Time-delayed adjustment during vegetation period + Easy to use | − Yield potential determination partly non-transparent for users − Costs for yield maps − Investment costs (fertilization technology) | [12,51] |
2.2. Sensor system | Measurement of the crop biomass or N content of the biomass with sensors in the field, calculation of vegetation indices and N requirement with algorithms | Sensor data, vegetation indices 4, and algorithms 5 | + (Indirect) Consideration of annual effects (e.g., water supply, N mineralization) + Dynamic real-time reaction to crop development at time of fertilizer application | − Investment costs (soft- and hardware) − User qualification − Partly nontransparentcalculations | [52,53,54] |
2.3. Map-Overlay system | Combination of mapping (2.1) and sensor systems (2.2) | Digital yield maps, sensor data | + Optimized fertilization + Reducing N losses | − High requirements on system compatibility and − User qualifications | [55,56,57] |
System Level | N Inputs | N Outputs | N Turnover, N Pools | N Surplus 6, NUE 7 | Data Origin | Spatial/ Temporal Scale 8 | Scope of Application | Examples |
---|---|---|---|---|---|---|---|---|
1. Crop production system (field balance) | Organic and mineral fertilizer, seeds, N2 fixation 9, (atmospheric N deposition) | Harvest products | Δ SON 10 | N surplus: potential loss of reactive N compounds (NH3, N2O, NO3−) NUE: crop production | Farm management information system (FMIS), farm record keeping | Field, farm/growing season, year | Easy to implement the analysis, Generated results easy to understand, Control function, Differing accuracies and validities of these balance sheets 11 | Legal N balance [48] |
2. Livestock production system (barn balance) | Feed (own production, purchase) 12, bedding material, animals (purchase) | Livestock products, manure | Change in livestock numbers 13: feed stock, manure storage | N surplus: N emissions of livestock keeping (NH3) (stable, manure storage) NUE: livestock production | Livestock management system | barn, pasture/ fattening period, year | REPRO [76], Dairy-CropSyst [15] | |
3. Farming system (farm-gate balance) | Market purchase | Market products | No consideration | N surplus: potential N loss of reactive N compounds on farm level NUE: farming system | Farm accounting data, purchase and sale documents | Farm, across-farm/year | Analysis of the total N efficiency and N loss potential of the farm | Nutrient flow balance [14], REPRO [76] |
Focus | Specification of the Requirements 14 | Impact on FNMS Development |
---|---|---|
User group | Support of user groups with different requirements and needs 15: Support simple and complex tasks depending on the abilities and intentions of the users and the objective to be achieved, e.g.,
|
|
Interoperability | Data management with minimal effort of data originating from different sources:
| Specification and implementation of interfaces to external systems, e.g.,
|
Scaling | Analysis and evaluation on different spatial and temporal scales:
|
|
Systemic approach |
|
|
Transparency |
|
|
Analysis and decision support |
|
|
Usability |
|
|
Data | Data Origin | System Level | Acquisition Method |
---|---|---|---|
1. Site conditions | |||
1.1. Climate and Weather | WS | R, F | MP, A, M |
1.2. Soil | PS, SM, M | Fi, SubFi | MP, LA |
2. Crop production | |||
2.1. Field and Cropping structure | M, RK | Fi, SubFi | HR |
2.2. Production system | PD, M, PS, SM, RK | Fi, SubFi | MP, LA, VI, A, M, HR |
3. Livestock production | |||
3.1. Husbandry system | SM, RK, PD, SM | F, Fi/GA | MP, LA, HR |
3.2. Livestock inventory and performance | RK, SM | F/GA, IA | MP, LA, A, M, HR |
3.3. Feeding system | SM, RK | F/GA, IA | MP, LA, A, M, HR |
3.4. Manure management | RK, PD, SM, M, PS | F, Fi, SubFi/GA | MP, LA |
Abbreviations | WS—weather station PS—physical samples SM—sensor measurements M—GIS Maps RK—record keeping software PD—process data | R—regional F—farm Fi—field SubFi—subfield GA—group of animals IA—individual animal | MP—measured physical on-site LA—laboratory analyzes VI—vegetation index A—algorithms M—models HR—human records (qualitative/quantitative data) |
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Weckesser, F.; Leßke, F.; Luthardt, M.; Hülsbergen, K.-J. Conceptual Design of a Comprehensive Farm Nitrogen Management System. Agronomy 2021, 11, 2501. https://doi.org/10.3390/agronomy11122501
Weckesser F, Leßke F, Luthardt M, Hülsbergen K-J. Conceptual Design of a Comprehensive Farm Nitrogen Management System. Agronomy. 2021; 11(12):2501. https://doi.org/10.3390/agronomy11122501
Chicago/Turabian StyleWeckesser, Fabian, Frank Leßke, Marco Luthardt, and Kurt-Jürgen Hülsbergen. 2021. "Conceptual Design of a Comprehensive Farm Nitrogen Management System" Agronomy 11, no. 12: 2501. https://doi.org/10.3390/agronomy11122501