Systems-Based Management
Sections in this Guide:
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Intro to Systems-Based Management and Differences from Conventional Management
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Identifying and Leveraging Agronomic Interconnections in Systems-Based Management
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Examples of Complex Systems Interactions in Agricultural Success Cases
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Additional Resources on Systems-Based Agricultural Management
Systems-based agricultural management is a holistic approach to farming that takes into account how the many different aspects of a farm operation interact and influence each other when making management decisions. Various elements of an agricultural operation, including soil health, plant nutrition, pest cycles, crop rotations, weather patterns, and market demands all interact and influence one another, but are typically managed in isolation in conventional farm management to simplify the decision making process as much as possible. Instead of focusing on isolated problems or seeking short-term fixes, systems-based management prioritizes long-term resilience, efficiency, and balance. This approach aims to address the root causes of issues that are limiting to yield and production, rather than treating symptoms with one-size-fits-all solutions. Systems-based management involves leveraging a farmer's understanding of biological, chemical, physical, and ecological processes and their interactions in order to minimize tradeoffs or unintended negative side effects of decisions. In other words, systems-based management promotes harmony and synergy across the whole farm operation and reduces inefficiencies and waste. There are many farmers, ecologists, and philosophers credited with contributing to the development of holistic systems-based management, but Zimbabwean farmer Allan Savory is widely regarded as the father of modern systems-based agricultural management, which he and many others commonly refer to as "holistic management".
Conventional, prescriptive agricultural management typically relies on standardized, input-driven recommendations based on general thresholds or models (e.g., apply X pounds of nitrogen per acre to obtain X yield, or spray Y pesticide when a certain pest is present). These prescriptions are often developed without fully accounting for the unique context of each farm operation and the potential negative tradeoffs or side effects of following the prescriptive one-size-fits-all recommendations. In contrast, systems-based management takes a more adaptive and site-specific approach. It prioritizes understanding the root causes of issues (such as soil compaction or imbalances in nutrients or soil microbial populations) and works to shift management to promoting efficiency, resilience, and regeneration. For example, rather than applying a fungicide every year as insurance, a systems-based approach might integrate cultural management practices such as disease suppressive crops into a rotation, like mustard or radish, or optimize irrigation, fertility, or planting dates to minimize disease pressure. This approach encourages farmers to be proactive land managers based on observations, data, and a working knowledge of their farm system, rather than making management practices reactively based on industry norms and prescriptive product labels, as is commonly the case in conventional management.
Intro to Systems-Based Management and Differences from Prescriptive Management
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Identifying and Leveraging Agronomic Interconnections in Systems-Based Management
Skilled agronomists and crop consultants frequently employ systems-based management when they make links between different areas of crop and soil management to make more informed decisions. Looking beyond individual nutrients, diseases, pests, and other aspects of management can often reveals that issues are not as simple as symptoms may have initially indicated. This can save significant resources compared to conventional management by alleviating the need for constant preventative crop protection products, mitigating fertilizer use dependency, and resolving yield bottlenecks. Agronomic concepts like the disease triangle and nutrient antagonisms (Mulder's Chart) are fundamental agronomy tools based on principles of wholistic systems-based agronomy. These concepts serve as prime examples of basic systems-based management tools in common agronomic practice.
Crop Disease Triangle
The crop disease triangle is a conceptual tool describing how interactions between pathogens, environmental conditions, and crop host susceptibility determine disease severity. As many farmers know, disease is not a predictable or straightforward area of farm management. Presence of pathogen does not always lead to disease impact. Some years diseases might be prevalent and other years they may not be noticeable despite presence in crop residue from the year prior. These inconsistencies occur because disease infection and spread is dependent on favorable environmental conditions for the pathogen and host susceptibility (lack of plant immunity) in addition to pathogen presence. Crop protection management programs should consider all three factors here instead of responding based on pathogen presence alone.
Mulder's Nutrient Relationship Chart
Dutch Scientist D. Mulder developed this diagram in 1953 showcasing the positive and negative interactions between different plant nutrients. This tool demonstrates how the abundance or deficiency of one nutrient can enhance or reduce the availability of another. Antagonisms exist when the overabundance of one nutrient reduces the availability of another, while stimulation occurs when the presence of one nutrient increases the availability of another. Employing this concept in systems-based farm management is important to ensure that fertilizer programs maximize farm ROI and do not cause unintended crop nutrient deficiencies or inefficiencies. While the "law of minimums" is a more common approach in conventional agronomy, Mulder's Chart is a reminder that balance is key to effective agricultural fertility system management.
Making prescriptive management decisions without considering concepts like the Crop Disease Triangle and Mulder's Chart can lead to ineffective short term solutions that reduce farm operational efficiency, limit ROI, and lead to further problems down the road. The flow diagrams below give two basic examples of cases where a conventional prescriptive management decision and a systems-based management decision result in entirely different outcomes to the same problems. The conventional managed decision isolated the issues and prescribed potential short term solutions, but did not address the root causes and may cost a grower significantly more investment in the long term. Whereas the more comprehensive systems-based solutions leveraged concepts like the Disease Triangle and Mulder's Chart to determine long-term solutions that address the root causes of the agronomic issues.
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Examples of Complex Systems Interactions in Agricultural Management Success Cases
The examples of systems-based management shown in the flow diagrams above are relatively simple and straightforward cases. But in many real world farm management scenarios, regenerative growers connect the dots between a wider range of agronomic factors influencing their day-to-day decisions. It is not uncommon to consider connections between very different areas of management, including crop protection programs, fertilizer management, and crop stress management. While conventional agronomy commonly divides problems into specific areas of management (e.g. fertility, crop protection, and irrigation management programs), regenerative farmers practicing systems-based management commonly integrate these different areas of management to make holistic decisions and minimize conflicts or "antagonisms" between each area. Below are some more complex examples of agricultural systems interactions taken from real world success cases and research.
Biomimicry: Looking to nature as a blueprint for regenerative agricultural systems
Moving beyond prescriptive agricultural management and embracing systems-based management is no easy or straightforward task. Unlike prescriptive management which offers easy to follow playbooks and prescribed solutions, systems-based management takes on different forms in different farming context. Successful systems-based management is dependent on having an in-depth understanding of the biological, chemical, physical, and ecological processes that function across a farm operation and investigating the interconnections between these factors. This can be more data intensive and cognition-driven in real world practice than conventional management, making the transition to systems-based management a daunting challenge. One piece of wisdom that many regenerative farmers commonly advise is to seek solutions in nature by exploring how you can emulate or mimic the processes that sustain healthy natural ecosystems in your particular area or region. World renowned soil health expert and regenerative farmer Ray Archuleta phrases this as practicing "biomimicry" or in other words imitating nature. Below are some examples of biomimicry, which in essence are often explanations for how natural healthy ecosystems, like forests, prairies, savannas, and wetlands are often able to produce massive levels of plant biomass annually without human input.
Common examples biomimicry practices in systems-based regenerative farming:
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Rotational Grazing: Many livestock animals, particularly large ruminants, are species that adapted to grazing across vast land areas gradually as they migrated. The vegetation they grazed typically had long periods of time to recover after these animals passed through foraging. Adopting more frequent rotations for livestock can simulate environmental conditions that mimic these rangeland ecosystems that their ancestors thrived on without human intervention for many years. Shorter grazing rotations partitioned across parcels or paddocks allows the vegetation to have more time to recover, and can help select for native forage plant species that are well adapted and sometimes very nutritionally beneficial for livestock.
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Diverse crop rotations and mixes: Natural ecosystems are very rarely made up of plant monocultures. Productive and healthy natural ecosystems are typically composed of a wide community of different plant species, which develop niches that provide synergistic benefits to each other. The simplest example of this is the benefit of soy on the following corn rotation thanks to its nitrogen fixation associations with beneficial Rhizobium bacteria. But that barely scratches the surface of the benefits that diverse crop rotations and mixes can offer by mimicking the natural interactions of plant species. Some "companion plants" attract beneficial insects that consume pests of other plants, while others scavenge excess or locked up nutrients that other plants wouldn't be able to obtain. Some help break up compacted soils while others form mats that shelter the stems and root systems of other plants. Healthy natural ecosystems tend to produce massive "yields" of plant biomass without any fertilizer or pesticides thanks to the balance between different species and the mutually beneficial adaptations they offer each other. World renowned soil health expert Dr. Christine Jones recommends cover crop mixes consisting of at least four different plant families. Long before modern agricultural inputs, Native Americans practiced biomimicry with the "Three Sisters" farming method that involved intercropping beans, corn, and squash. In this classic example of ancient farming wisdom, beans fix nitrogen for all three crops, corn provides structure for beans to climb, and squash serves as a ground cover to protect the soil and suppress weeds.
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Transitioning from fallow land to cover crops can stimulate rainfall: One reason why rainforests receive so much rainfall each year is because they are made up of high levels of vegetation, which recycle water back into the atmosphere through processes like condensation and evaporation, contributing to high atmospheric moisture levels that are needed to form clouds and sustain regular rainfall. In arid regions like the Great Plains of North America, rainfall is a major limiting factor to crop production. The general consensus is that farmers are at the mercy of drought when it comes to having a productive yield. However, fascinatingly there is increasingly compelling evidence that establishing vegetation coverage in arid regions can help increase localized rainfall levels. Just like rainforests, establishing vegetation in the form of cover crops can spur cloud formation and rain, creating a positive feedback loop that mimics natural processes rainforests have engineered. A study conducted in Africa's semi-arid Sahel region showed that vegetation accounts for about 30% of variation in annual rainfall. While establishing cover crops can be a major challenge in arid regions with low annual rainfall, it can also be a biomimicry practice that offers a solution to lack of rainfall.
Additional Resources on Systems-Based Management
