Mineral-Ecological Cropping Systems—A New Approach to Improve Ecosystem Services by Farming without Chemical Synthetic Plant Protection
Abstract
:1. Introduction
2. Characterization of Farming Concepts
3. Implications for the Further Development of Agricultural Cropping Systems
4. Development of Mineral-Ecological Cropping Systems
4.1. Exclusion of Chemical Synthetic Plant Protection Products in Mineral-Ecological Cropping Systems
4.2. Mineral-Ecological Cropping Systems from a Production Technology Perspective
4.2.1. Cultivation Measures to Promote Natural Regulatory Processes
4.2.2. Cultivation Measures for Direct Yield Increase, Plant Strengthening, and Plant Protection
4.2.3. Use of Precision Agriculture Technologies
4.2.4. Impact on Natural Regulatory Processes
4.3. Mineral-Ecological Cropping Systems from the Perspective of Yield and Product Quality
4.4. Mineral-Ecological Cropping Systems from a Socio-Economic Perspective
4.4.1. Economic Aspects at Farm Level
4.4.2. Economic Aspects at Regional Level
4.4.3. Perspective of the Agricultural Sector
4.4.4. Perspective of Society and the Food Chain
4.5. Mineral-Ecological Cropping Systems from the Perspective of Ecosystem Services
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Industrialized Process Control | Natural Process Control |
---|---|
|
|
Objective | Promotion of Natural Regulatory Processes | Direct Yield Promotion and Promotion of Natural Regulatory Processes | Direct Yield Promotion | ||||||
---|---|---|---|---|---|---|---|---|---|
Point of Application | Plant Protection | Plant | Fertilization and Plant Strengthening | Curative Plant Protection | |||||
Category of Action | Pesticide Choice | Rotation Choice | Variety Choice | Plant Pattern | Fertilizer Choice | Effector Choice | Weed Control | Pest and Desease Control | |
MECSs Cultivation Measure | No Use of CSPs | Diverse Rotation | Resistant Varieties | Equidistant Seeding c | Miner. Fertilizer (Macro and Micro Nutrients) c | Placed, ammonium c | Bioeffectors c | Harrow, How c | Biocontrol Agents c |
Provisioning services | |||||||||
Yield | −[31,87,88,91,92,98,99,100,101,102] | +[97,103,104] | +[82,88,99,100,105,106,107] | +[108,109,110,111] | +[112] | +[99,113] | +[114,115,116] | +[117] | +[118,119] |
Product quality | +[103,120,121,122,123,124]−[84,85,86,124] | + | +[82,106,107] | +[125,126] | +[127] | + | +[114,115,116] | + | +[128,129] |
Regulating services | |||||||||
Climate change mitigation | +[87] −[130] | + | +[82] | +[131] −[113] | o[113] +[131] | +[131] | +[132] −[133] | ||
Water regulation and quality | +[83,134,135] −[130] | +[97,104] | +[82,105] | +[109] | +[121] −[136] | +[131] | −[137] | +[138] | |
Erosions prevention | + | +[108,110] | − | +[139] | |||||
Soil fertility | +[123,140,141,142] | +[23,97,104] | +[108,110] | +[112,136] − | + | −[137] | +[143,144,145,146] | ||
Nutrient cycles (efficiency) | −[82] | +[97,104] | +[82] | +[108,109,110,111] | +[131] | +[113,131] | +[131] | −[137] | +[147,148,149,150] |
Pollination | +[151,152,153] | +[22,23] | +[154] | ||||||
Weed supression | −[92,155] | + | +[108,110,111] | +[117,156,157] | +[158,159] | ||||
Biological control | +[140,160,161,162] | +[23,98,163] | +[82,105] | +[164,165,166] | −[159] | + | +[144,159,167,168] | ||
Habitat services | |||||||||
Life cycles and genetic diversity | +[22,123,140,141,142,169,170] | +[22,23,160,163] | +[125,126] | −[23,163] | |||||
Cultural services | |||||||||
Recreation and tourism | + | +[171] |
Area of Application | Seeding | Fertilization | Plant Protection | ||
---|---|---|---|---|---|
Weed Control | Desease Monitoring | Desease Control | |||
Precision Agriculture Technology | A sowing unit is mounted on a mobile robot platform [172]. The robot uses different sensors to navigate autonomously and to adapt the sowing parameters to the actual soil conditions [172] and crop requirements. | The modified sowing unit can be used for insoil fertilization. The robot uses image-based row recognition to precisely place the fertilizer between the crop rows [173]. | A real-time camera-based automatic guidance system is used to steer hoeing blades in the center between the crop row [174,175]. | Unmanned aerial vehicles (UAVs) are combined with hyperspectral imaging sensors to analyse the plants spectral signature for pathogen detection [176,177,178]. Machine learning and AI are used to analyse sensor data, allowing automatic detection and detection of subtle changes in early stages of pathogen development [179]. | UAVs are used for the application of biological control agents (BCAs). UAV-application can easily schedule the release of BCAs exactly when and where they are needed according to their modes of action for better control of plant pathogens [180]. |
Research focus to optimize cultivation measures in MECSs | ● Investigation and modification of technology to improve quality of incorporation of seeds into soils. | ● Investigation of effectiveness and efficiency of in soil fertilization during vegetation period in grains. ● Site-specific and individual plant-adapted fertilization by variable rate technology. | ● Accuracy of steering and efficacy of weed control by camera-guided inter-row hoeing. ● Crop response and crop yield in cereals, soybean, and maize with different row spacings, growth stages, driving speeds and hoeing elements. | ● Mapping of problematic areas in the field for precision farming. ● Refinement of forecast methods. ● Objective assessment of disease severity and plant development in MECSs. | ● Effective BCAs with al-ready elucidated modes of action will be evaluated under field conditions. ● Investigation of different application schemes to establish BCAs in the field according to monitored forecast results. |
Expected benefits | ● Higher field emergence with less seeds. ● More even seeding depth and soil coverage of seeds. ● More even spatial crop plant distribution according to site and variety-based requirements. ● Better aeration of crop plant stands and equal access of plants to resources. ● Quality over quantity through autonomous work. | ● Better access of crop plants to micro- and macronutrients, and bioeffectors. ● Optimize fertilizer use and minimize losses. ● Improving nutrient use efficiency. | ● Higher weed control efficacy. ● Less crop damage due to higher selectivity. ● Higher labor efficiency due to higher driving speeds. | ● Early and precise detection of disease symptoms and abiotic stress factors. ● Assessment of disease severity and identification of disease species through specific changes within the plants spectral signature. ● Drone-based measurements permit high throughput coupled with variable resolution. ● Automated and objective data assessment through data analysis methods. | ● Drone-based biocontrol offers a new tool for effective and sustainable control of plant diseases. ● Minimizing yield losses quantitatively and qualitatively through combating pathogen infestation and minimize the risk of food safety hazards (e.g., mycotoxins). |
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Zimmermann, B.; Claß-Mahler, I.; von Cossel, M.; Lewandowski, I.; Weik, J.; Spiller, A.; Nitzko, S.; Lippert, C.; Krimly, T.; Pergner, I.; et al. Mineral-Ecological Cropping Systems—A New Approach to Improve Ecosystem Services by Farming without Chemical Synthetic Plant Protection. Agronomy 2021, 11, 1710. https://doi.org/10.3390/agronomy11091710
Zimmermann B, Claß-Mahler I, von Cossel M, Lewandowski I, Weik J, Spiller A, Nitzko S, Lippert C, Krimly T, Pergner I, et al. Mineral-Ecological Cropping Systems—A New Approach to Improve Ecosystem Services by Farming without Chemical Synthetic Plant Protection. Agronomy. 2021; 11(9):1710. https://doi.org/10.3390/agronomy11091710
Chicago/Turabian StyleZimmermann, Beate, Ingrid Claß-Mahler, Moritz von Cossel, Iris Lewandowski, Jan Weik, Achim Spiller, Sina Nitzko, Christian Lippert, Tatjana Krimly, Isabell Pergner, and et al. 2021. "Mineral-Ecological Cropping Systems—A New Approach to Improve Ecosystem Services by Farming without Chemical Synthetic Plant Protection" Agronomy 11, no. 9: 1710. https://doi.org/10.3390/agronomy11091710