2nd Edition of Agriculture, Forestry, and Horticulture World Conference 2026
Speakers - AFHWC2025
Marcia Figueira
Marcia Figueira
- Designation: Linking Landscape, Environment, Agriculture and Food Research Center, Instituto Superior de Agronomia
- Country: Portugal
- Title: Farmers Field Books and the Multi Level Social Ecological Technical Systems (ML SETS) Framework Green Finance, LCA, and a Bioregional Understanding of Portuguese Rice
Abstract
Green-finance instruments such as the EU Environmental Taxonomy are becoming central to agricultural investment, yet their strict thresholds risk oversimplifying farming realities and excluding farmers from support. To address this, we introduce the Multi-Level Social-Ecological-Technical Systems (ML-SETS) framework as a conceptual lens that integrates Social-Ecological Systems and the Multi-Level Perspective, while opening pathways to link them with sustainability metrics such as Life Cycle Assessment (LCA) and green-finance mechanisms.. ML-SETS provides a structured way to interpret farm-level and regional dynamics without neglecting ecological feedbacks, technical practices, climatic variability, or the social conditions of farmers.
In a case study, we apply ML-SETS to 12 rice farmers in Portugal’s Sado Delta, cultivating under three techniques (8 conventional, 2 direct seeding, 2 no till), using a three-year dataset of farmer field books—mandatory notebooks required for EU integrated crop production and subsidy access. These records provide time-stamped data on operations, fertilizers, pesticides, herbicides, machinery use, and pest or disease incidence. In Portugal, between 120,000 and 160,000 farmers file these field books each year. For this study, we focus on a coverage of ~590 ha of rice cultivation, with annual production ranging from 2,400 to 3,600 tonnes. We extracted, normalized, and analyzed this dataset by: (i) grouping operations and input intensities, (ii) tracing fertilizer and pesticide applications across crop stages, (iii) comparing yields across years and techniques using ANOVA and Kruskal–Wallis tests, and (iv) applying exploratory clustering methods, including Data Envelopment Analysis (DEA), to evaluate performance profiles. Regressions will also be explored to identify causal relationships between inputs, timing, and outcomes.
The results were then mobilized for multiple purposes, which in this plenary are presented through two guiding questions:
1. Green finance and LCA: How can nutrient-balance indicators required by the EU Taxonomy (e.g. farm-gate nitrogen balance, Nitrogen Use Efficiency) be aligned with LCA results covering fertilizers, crop-protection inputs, irrigation, and machinery?
2. Bioregional dynamics, climate, and governance: What information on pest and disease incidence can be extracted from this 590-ha dataset, and how can it be combined with climate signals (e.g. weather extremes, North Atlantic Oscillation phases) to inform governance of ecological risks and the timing of activities to support resilience? And how can this analysis serve as a first step toward larger bioregional assessments?
By integrating finance, ecology, climatic dynamics, and farmer networks, ML-SETS enables analysis at both farmer and bioregional levels. Using farmer field books—a governance tool required for EU integrated crop production and subsidy access—we repurpose top-down data structures to calculate bottom-up constraints and farming realities. Building from this application with 12 farmers, the approach not only exposes, and provides a lens to address, the exclusions that rigid conventional and emergent green-finance criteria can produce, but also demonstrates how farmer-level data can be mobilized to generate more grounded assessments and open pathways for more adaptive, just, and regionally attuned agricultural transitions.
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