Filling the agronomic data gap through a minimum data collection approach

Article


Tenorio, Fatima A.M., Edreira, Juan I. Rattalino, Monzon, Juan Pablo, Aramburu-Merlos, Fernando, Dobermann, Achim, Gruere, Armelle, Brihet, Juan Martin, Gayo, Sofia, Conley, Shawn, Mourtzinis, Spyridon, Mashingaidze, Nester, Sananka, Alex, Aston, Stephen, Ojeda, Jonathan J. and Grassini, Patricio. 2024. "Filling the agronomic data gap through a minimum data collection approach." Field Crops Research. 308. https://doi.org/10.1016/j.fcr.2024.109278
Article Title

Filling the agronomic data gap through a minimum data collection approach

ERA Journal ID5309
Article CategoryArticle
AuthorsTenorio, Fatima A.M., Edreira, Juan I. Rattalino, Monzon, Juan Pablo, Aramburu-Merlos, Fernando, Dobermann, Achim, Gruere, Armelle, Brihet, Juan Martin, Gayo, Sofia, Conley, Shawn, Mourtzinis, Spyridon, Mashingaidze, Nester, Sananka, Alex, Aston, Stephen, Ojeda, Jonathan J. and Grassini, Patricio
Journal TitleField Crops Research
Journal Citation308
Article Number109278
Number of Pages11
Year2024
PublisherElsevier
Place of PublicationNetherlands
ISSN0378-4290
1872-6852
Digital Object Identifier (DOI)https://doi.org/10.1016/j.fcr.2024.109278
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0378429024000315
Abstract

Context
Agronomic data such as applied inputs, management practices, and crop yields are needed for assessing productivity, nutrient balances, resource use efficiency, as well as other aspects of environmental and economic performance of cropping systems. In many instances, however, these data are only available at a coarse level of aggregation or simply do not exist.

Objectives
Here we developed an approach that identifies sites for agronomic data collection for a given crop and country, seeking a balance between minimizing data collection efforts and proper representation of the main crop producing areas.

Methods
The developed approach followed a stratified sampling method based on a spatial framework that delineates major climate zones and crop area distribution maps, which guides selection of sampling areas (SA) until half of the national harvested area is covered. We provided proof of concept about the robustness of the approach using three rich databases including data on fertilizer application rates for maize, wheat, and soybean in Argentina, soybean in the USA, and maize in Kenya, which were collected via local experts (Argentina) and field surveys (USA and Kenya). For validation purposes, fertilizer rates per crop and nutrient derived at (sub-) national level following our approach were compared against those derived using all data collected from the whole country.

Results
Application of the approach in Argentina, USA, and Kenya resulted in selection of 12, 28, and 10 SAs, respectively. For each SA, three experts or 20 fields were sufficient to give a robust estimate of average fertilizer rates applied by farmers. Average rates at national level derived from our approach compared well with those derived using the whole database ( ± 10 kg N, ± 2 kg P, ± 1 kg S, and ± 5 kg K per ha) requiring less than one third of the observations.

Conclusions
The developed minimum crop data collection approach can fill the agronomic data gaps in a cost-effective way for major crop systems both in large- and small-scale systems.

Significance
The proposed approach is generic enough to be applied to any crop-country combination to guide collection of key agricultural data at national and subnational levels with modest investment especially for countries that do not currently collect data.

KeywordsData gaps; Agronomic data ; Nutrients; Fertilizer; Stratified sampling ; Climate zones
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020300403. Agronomy
Byline AffiliationsUniversity of Nebraska-Lincoln, United States
International Fertilizer Association, France
Buenos Aires Grain Exchange, Argentina
University of Wisconsin-Madison, United States
One Acre Fund, Kenya
Regrow Ag, Australia
Permalink -

https://research.usq.edu.au/item/z5vx5/filling-the-agronomic-data-gap-through-a-minimum-data-collection-approach

Download files


Published Version
1-s2.0-S0378429024000315-main.pdf
License: CC BY 4.0
File access level: Anyone

  • 14
    total views
  • 5
    total downloads
  • 3
    views this month
  • 2
    downloads this month

Export as

Related outputs

Field and in-silico analysis of harvest index variability in maize silage
Ojeda, Jonathan Jesus, Islam, M. Rafiq Islam, Correa-Luna, Martin, Gargiulo, Juan Ignacio, Clark, Cameron Edward Fisher, Rotili, Diego Hernan and Garcia, Sergio Carlos. 2023. "Field and in-silico analysis of harvest index variability in maize silage." Frontiers in Plant Science. 14. https://doi.org/10.3389/fpls.2023.1206535
Investigating the effects of APSIM model configuration on model outputs across different environments
Chapagain, Ranju, Remenyi, Tomas A., Huth, Neil, Mohammed, Caroline L. and Ojeda, Jonathan J.. 2023. "Investigating the effects of APSIM model configuration on model outputs across different environments." Frontiers in Agronomy. 5. https://doi.org/10.3389/fagro.2023.1213074
Assessing the effect of using different APSIM model configurations on model outputs
Chapagain, Ranju, Huth, Neil, Remenyi, Tomas A., Mohammed, Caroline L. and Ojeda, Jonathan J.. 2023. "Assessing the effect of using different APSIM model configurations on model outputs." Ecological Modelling. 483. https://doi.org/10.1016/j.ecolmodel.2023.110451
Calibrating APSIM for forage sorghum using remote sensing and field data under sub-optimal growth conditions
Della Nave, Facundo N., Ojeda, Jonathan J., Irisarri, J. Gonzalo N., Pembleton, Keith, Oyarzabal, Mariano and Oesterheld, Martin. 2022. "Calibrating APSIM for forage sorghum using remote sensing and field data under sub-optimal growth conditions." Agricultural Systems. 201, pp. 1-14. https://doi.org/10.1016/j.agsy.2022.103459
Modelling forage yield and water productivity of continuous crop sequences in the Argentinian Pampas
Ojeda, J. J., Pembleton, K. G., Caviglia, O. P., Islam, M. R., Agnusdei, M. G. and Garcia, S. C.. 2018. "Modelling forage yield and water productivity of continuous crop sequences in the Argentinian Pampas." European Journal of Agronomy. 92, pp. 84-96. https://doi.org/10.1016/j.eja.2017.10.004
Evaluation of the agricultural production systems simulator simulating lucerne and annual ryegrass dry matter yield in the Argentine pampas and south-eastern Australia
Ojeda, J. J., Pembleton, K. G., Islam, M. R., Agnusdei, M. G. and Garcia, S. C.. 2016. "Evaluation of the agricultural production systems simulator simulating lucerne and annual ryegrass dry matter yield in the Argentine pampas and south-eastern Australia." Agricultural Systems. 143, pp. 61-75. https://doi.org/10.1016/j.agsy.2015.12.005