Precision Agroecology

Duff, Hannah; Hegedus, Paul B.; Loewen, Sasha; Bass, Thomas M.; Maxwell, Bruce D.

doi:10.3390/su14010106

Cited by 20 publications

(17 citation statements)

References 121 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…First, a database management system was required to facilitate the storage and organization of ecological field-specific data (1A), followed by the development of on-farm experiments that were implemented to assess the ecological relationship between the crop and the agronomic input of interest (1B). PA equipment and technology were used for the application of experiments and collection of field-specific data [6,13]. Data from farms and open-source data repositories were gathered (2) and combined to generate analysis ready datasets for ecological modeling (3).…”

Section: Figurementioning

confidence: 99%

“…Management outcomes, ranging from farmer's status quo rates to sitespecific optimized rates, were evaluated and presented to farmers and crop managers in a probabilistic framework, leaving decisions about future management in the hands of the farmers (5C). Finally, a farmer or crop manager decided on the management for next year and continued to apply experimental rates to further experimentation, data collection, and ecological understanding of crop responses on the field with the goal of reducing areas reserved for experimental blocks to allow for increased farmer profitability (6). Currently, all steps of the OFPE framework are automated and available as an open-source R package called OFPE via GitHub (https://github.com/paulhegedus/OFPE).…”

Section: Figurementioning

confidence: 99%

“…precision agriculture (PA) hold great potential for sustainable management through quantification of the tradeoffs of agronomic inputs at the field scale where input management decisions are made [6][7].…”

Section: Introductionmentioning

confidence: 99%

“…PA is often perceived as a replacement of local stakeholder knowledge, giving rise to farmers' fears of the use of the technology for farmer replacement and the potential of "ecological dystopias" [10][11]. On-farm experimentation (OFE) is provided as a mechanism to improve upon local knowledge, use locally parameterized agroecological models, and incorporate and augment the farmers' knowledge necessary for locally relevant decision making, rather than replacing traditional knowledge [6].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On-Farm Precision Experiments (OFPE) framework: tapping local data to optimize crop sub-field scale decisions

Hegedus¹,

Maxwell²,

Sheppard³

et al. 2022

Preprint

View full text Add to dashboard Cite

Precision agriculture and open-source data repositories provide a plethora of field-specific ecological data about agroecosystems, but few mechanisms have been developed to turn that information into management recommendations for crop production. The On-Farm Precision Experiments (OFPE) framework is an agroecological model-based methodology to improve crop manager’s abilities to make field-scale agronomic input decisions. This work evaluates the use of field-specific experiments that employ open-source data and the data emanating from precision agriculture technologies to gain local knowledge of the spatial and temporal variability in agroeconomic performance at the sub-field scale. Quantification of the temporal variability in crop response to inputs (e.g., crop seeding rates, crop rotations, fertilizers, other soil amendments, pesticides, etc.) allows for estimation of the probability that a future management scenario will outcompete another, in terms of crop yield, crop quality, farmer net return, or environmental quality. The challenge is to integrate OFPE into applied management with minimal disruption of stakeholder practices while drawing on historic knowledge about the field and economic constraints. OFPE is the basis of a decision support system that includes a six-step cyclical process that harnesses precision agriculture technology to apply experiments and gather field-specific data, incorporates modern data management and analytical approaches, and generates management recommendations as probabilities of outcomes. The OFPE framework allows field managers to assess the tradeoffs in agronomic input management between the maximization of crop production, quality and profits from production while considering environmental effects.

show abstract

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On-Farm Precision Experiments (OFPE) framework: tapping local data to optimize crop sub-field scale decisions

Hegedus¹,

Maxwell²,

Sheppard³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The amount of farm data now available makes it possible for data science to aid in the management and analysis of agronomic data and inform local within-eld decision making (Gibert et al, 2018;Provost & Fawcett, 2013). Investigating the bene t of generalizable data science tools for modeling the relationship of NUE with soil parameters, N fertilizer rates, and other open-source data can inform precision agroecological approaches to N fertilizer management that are conscious of economic and ecological outcomes (Duff et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Using spatially variable nitrogen application and crop responses to evaluate crop nitrogen use efficiency

Hegedus

Ewing

Jones

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Low nitrogen use efficiency (NUE) is ubiquitous in agricultural systems, with mounting global scale consequences for both atmospheric aspects of climate and downstream ecosystems. Since NUE related soil characteristics such as water holding capacity and organic matter are likely to vary at small scales (<1 ha), understanding the influence of soil characteristics on NUE at the subfield scale (<32 ha) could increase fertilizer NUE. Here, we quantify NUE in four conventionally managed dryland winter-wheat fields in Montana following multiple years of sub-field scale variation in experimental N fertilizer applications. To inform farmer decisions that incorporates NUE, we developed a generalizable model to predict subfield scale NUE by comparing six candidate models, using ecological and biogeochemical data gathered from open-source data repositories and from normal farm operations, including yield and protein monitoring data. While NUE varied across fields and years, efficiency was highest in areas of fields with low N availability from both fertilizer and estimated mineralization of soil organic N (SON). At low levels of applied N, distinct responses among fields suggest distinct capacities to supply non-fertilizer plant-available N, suggesting that mineralization supplies more available N in locations with higher total N, reducing efficiency for any applied rate. Comparing modelling approaches, a random forest regression model of NUE provided predictions with the least error relative to observed NUE. Subfield scale predictive models of NUE can help to optimize efficiency in agronomic systems, maximizing both economic net return and NUE, which provides a valuable approach for optimization of nitrogen fertilizer use.

show abstract

Genetic Study and Temporal Dynamics of the Reaction to Ascochyta rabiei Epidemic in Improved Lines and Cultivars of Chickpea (Cicer arietinum L.)

Bayahi,

Chaar

2024

Legume Science

View full text Add to dashboard Cite

Ascochyta blight is the most devastating worldwide disease of chickpea (Cicer arietinum L.). It is caused by Ascochyta rabiei (teleomorph: Didymella rabiei) an airborne pathogenic fungus which is common in rainy climates and warm temperatures. Despite the efforts deployed by genetic improvement to develop highly resistant varieties, a total resistance to Ascochyta blight is lacking in chickpea. From 2015 to 2019 season, a study was carried out to develop highly resistant lines at the experimental station of the National Agronomical Institute of Tunis (INAT).In this genetic program, four crosses were realized: Nour x ILC154986, Nour x ILC154445, Béja1x ILC155064, and Béja1x ILC154449. Nour and Béja1 varieties were respectively tolerant and susceptible to Ascochyta blight and were used as female parents. The genetic study showed a total of six genes controlling the resistance to Ascochyta blight. The disease progress was analyzed under inoculation in the field on seven dates, and the plant infection rate r was recorded on the parental cultivars and the lines developed from crosses. The temporal progress of the disease was studied by using statistical and mathematical tools. A logistic model was tested and then applied to describe the Ascochyta rabiei progress over time in the field. The fitting adequacy of the logistic model was estimated by the determination coefficient R2 which value exceeded 0.98 and justified this selection. The logistic curves analysis showed a slow blighting and an infection progress decrease of lines issued from Béja1 x desi1 and Nour x Kabuli1 crosses. These lines obtained by transgressive segregation in the Nour x Kabuli1 and Beja1 x Desi2 populations can develop a stable resistance and prevent its overcoming.

show abstract

Precision Agroecology

Cited by 20 publications

References 121 publications

On-Farm Precision Experiments (OFPE) framework: tapping local data to optimize crop sub-field scale decisions

On-Farm Precision Experiments (OFPE) framework: tapping local data to optimize crop sub-field scale decisions

Using spatially variable nitrogen application and crop responses to evaluate crop nitrogen use efficiency

Genetic Study and Temporal Dynamics of the Reaction to Ascochyta rabiei Epidemic in Improved Lines and Cultivars of Chickpea (Cicer arietinum L.)

Contact Info

Product

Resources

About