Improving a dynamic farm level model by coupling empirically estimated pre-crop values with crop rotation optimization

The dominance of cereal-based rotations in Finnish agriculture contributes to adverse environmental impacts, and shifting towards diversified cropping systems has been minimal. Since economic viability and profit motives are strong among farmers, there is a need for analysis tools investigating the profitability of agronomically feasible, diversified cropping systems and rotations. While there is consensus on pre-crop effects, their impact is unclear and often overlooked in decision-making. We integrate recent empirically estimated pre-crop values into farm models to assess their influence on land use and farm economy and use dynamic optimization to solve the economically optimal crop rotations. The results show that including empirically estimated pre-crop values improves on subjective expert estimates of pre-crop effects and can eliminate the need for additional rotational constraints. Utilizing pre-crop effects consistently over time may lead to significant economic gains. This approach can contribute to sustainable agriculture by encouraging diversification and resilience, even amidst changing climates or novel crops.

Authors

Institutions

Publication Details

Journal
Agricultural and Food Science
Published
2024-10-03
DOI
https://doi.org/10.23986/afsci.144747
Citations
1
Primary Topic
Agricultural Economics and Policy
Type
article
Field-Weighted Citation Impact
1.34

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Improving a dynamic farm level model by coupling empirically estimated pre-crop values with crop rotation optimization

Taru Palosuo, Pirjo Peltonen‐Sainio, Domna Tzemi, Janne Rämö et al.
1 citations
Agricultural and Food Science
Agricultural Economics and Policy
1.34
article

Improving a dynamic farm level model by coupling empirically estimated pre-crop values with crop rotation optimization

Taru Palosuo, Pirjo Peltonen‐Sainio, Domna Tzemi, Janne Rämö, Heikki Lehtonen
article en
1 citations

Abstract

The dominance of cereal-based rotations in Finnish agriculture contributes to adverse environmental impacts, and shifting towards diversified cropping systems has been minimal. Since economic viability and profit motives are strong among farmers, there is a need for analysis tools investigating the profitability of agronomically feasible, diversified cropping systems and rotations. While there is consensus on pre-crop effects, their impact is unclear and often overlooked in decision-making. We integrate recent empirically estimated pre-crop values into farm models to assess their influence on land use and farm economy and use dynamic optimization to solve the economically optimal crop rotations. The results show that including empirically estimated pre-crop values improves on subjective expert estimates of pre-crop effects and can eliminate the need for additional rotational constraints. Utilizing pre-crop effects consistently over time may lead to significant economic gains. This approach can contribute to sustainable agriculture by encouraging diversification and resilience, even amidst changing climates or novel crops.

Agricultural and Food Science
Natural Resources Institute Finland (FI)
European Commission
Openalex Percentile: Top 14%
Agricultural Economics and Policy
1.34
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Improving a dynamic farm level model by coupling empirically estimated pre-crop values with crop rotation optimization — Taru Palosuo, Pirjo Peltonen‐Sainio, et al. · Agricultural and Food Science (2024) | TGRS Research Map | TGRS