Scholarly Work - Land Resources & Environmental Sciences

Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/8680

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Momentum for agroecology in the USA
(Springer Science and Business Media LLC, 2024-07) Ong, Theresa W.; Roman-Alcalá, Antonio; Jiménez-Soto, Estelí; Jackson, Erin; Perfecto, Ivette; Duff, Hannah
The alarming convergence of ecological, health and societal crises underpins the urgent need to transform our agricultural and food systems. The global food system, with industrial agriculture at its core, poses a major threat to our planet’s health, contributing to climate change, biodiversity loss and food insecurity, which is known as the triple threat to humanity. The hidden costs of a global food system that relies on industrial agriculture are estimated to be US$12.7 trillion, with the vast majority driven by public-health crises due to unhealthy foods that disproportionately burden people on the lowest incomes.
Precision Agroecology
(MDPI AG, 2021-12) Duff, Hannah; Hegedus, Paul B.; Loewen, Sasha; Bass, Thomas; Maxwell, Bruce D.
In response to global calls for sustainable food production, we identify two diverging paradigms to address the future of agriculture. We explore the possibility of uniting these two seemingly diverging paradigms of production-oriented and ecologically oriented agriculture in the form of precision agroecology. Merging precision agriculture technology and agroecological principles offers a unique array of solutions driven by data collection, experimentation, and decision support tools. We show how the synthesis of precision technology and agroecological principles results in a new agriculture that can be transformative by (1) reducing inputs with optimized prescriptions, (2) substituting sustainable inputs by using site-specific variable rate technology, (3) incorporating beneficial biodiversity into agroecosystems with precision conservation technology, (4) reconnecting producers and consumers through value-based food chains, and (5) building a just and equitable global food system informed by data-driven food policy. As a result, precision agroecology provides a unique opportunity to synthesize traditional knowledge and novel technology to transform food systems. In doing so, precision agroecology can offer solutions to agriculture’s biggest challenges in achieving sustainability in a major state of global change.
Towards a Low-Cost Comprehensive Process for On-Farm Precision Experimentation and Analysis
(MDPI, 2023-02) Hegedus, Paul B.; Maxwell, Bruce; Sheppard, John; Loewen, Sasha; Duff, Hannah; Morales-Luna, Giorgio; Peerlinck, Amy
Few mechanisms turn field-specific ecological data into management recommendations for crop production with appropriate uncertainty. Precision agriculture is mainly deployed for machine efficiencies and soil-based zonal management, and the traditional paradigm of small plot research fails to unite agronomic research and effective management under farmers’ unique field constraints. This work assesses the use of on-farm experiments applied with precision agriculture technologies and open-source data to gain local knowledge of the spatiotemporal variability in agroeconomic performance on the subfield scale to accelerate learning and overcome the bias inherent in traditional research approaches. The on-farm precision experimentation methodology is an approach to improve farmers’ abilities to make site-specific agronomic input decisions by simulating a distribution of economic outcomes for the producer using field-specific crop response models that account for spatiotemporal uncertainty in crop responses. The methodology is the basis of a decision support system that includes a six-step cyclical process that engages precision agriculture technology to apply experiments, gather field-specific data, incorporate modern data management and analytical approaches, and generate management recommendations as probabilities of outcomes. The quantification of variability in crop response to inputs and drawing on historic knowledge about the field and economic constraints up to the time a decision is required allows for probabilistic inference that a future management scenario will outcompete another in terms of production, economics, and sustainability. The proposed methodology represents advancement over other approaches by comparing management strategies and providing the probability that each will increase producer profits over their previous input management on the field scale.