Jonathan Ojeda

Jonathan Ojeda
Regrow Ag · Global Science/Modelling team

Agr. Eng. Ph.D.
I apply data analytics to understand the crop eco-physiology behind crop models and to quantify model uncertainties.

About

36
Publications
6,875
Reads
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219
Citations
Introduction
My research speciality is in quantifying the effects of crop management and climate variability on soil-crop processes and integrating data into models at different spatial scales. So that effects can be scaled up to predict crop growth, both yield and quality and environmental impacts for better outcomes in agricultural systems. This requires a broad understanding of how environment influence crop growth; how rainfall, irrigation and fertiliser influence soil conditions; how crops obtain water and nutrients from the soil; how soil processes contribute to the loss of carbon and nitrogen; and how all these processes interact.
Additional affiliations
April 2017 - November 2017
National Scientific and Technical Research Council
Position
  • PostDoc Position
April 2012 - April 2017
National Scientific and Technical Research Council
Position
  • PhD Student

Publications

Publications (36)
Conference Paper
Full-text available
Los objetivos del siguiente estudio fueron evaluar la biomasa aérea acumulada (BAA) y la eficiencia de uso de las lluvias (EULL) de distintas secuencias de cultivos forrajeros anuales (SCFA), en comparación con pasturas de alfalfa (A) en diferentes localidades, con manejo agronómico adecuado (genotipo, fecha de siembra, fertilización) y en condicio...
Article
CONTEXT Mechanistic sorghum models have been mostly used to estimate sorghum yield for grain sorghum for a range of genotype, management, and environmental conditions. There is a lack of model testing for crop growth and development responses for forage genotypes and information for phenological parameterization under sub-optimal water and nitrogen...
Article
Full-text available
Crop models are essential tools for analysing the effects of climate variability, change on crop growth and development and the potential impact of adaptation strategies. Despite their increasing usage, crop model estimations have implicit uncertainties which are difficult to classify and quantify. Failure to address these uncertainties may result...
Article
Full-text available
This study investigates the main drivers of uncertainties in simulated irrigated maize yield under historical conditions as well as scenarios of increased temperatures and altered irrigation water availability. Using APSIM, MONICA, and SIMPLACE crop models, we quantified the relative contributions of three irrigation water allocation strategies, th...
Article
Full-text available
Regional scale estimations of sorghum biomass production allow identification of optimum genotype × environment × management (G×E×M) combinations for bioenergy generation. The objective of this study was to determine the degree of contributions of G, E and M toward variability in sorghum biomass in the USA. Using the Agricultural Production Systems...
Article
Argentina grows the second‐largest area of lucerne in the world. Despite its importance, a yield gap exists between potential and measured yields, but factors contributing to it are still unclear. This study aimed to identify management factors and research needs to reduce the lucerne yield gap to improve the livestock systems in this region. We us...
Article
Full-text available
Enhancing and maintaining on-farm diversity is a potential strategy to improve farming systems’ sustainability and resilience. However, diversification is driven or constrained by different factors and dynamics that vary across environmental, socio-economic and political contexts. Identifying drivers and constraints of diversification can help to s...
Article
Crop models are usually developed using a test set of data and simulations representing a range of environment, soil, management and genotype combinations. Previous studies demonstrated that errors in the configuration of test simulations and aggregation of observed data sets are common and can cause major problems for model development. However, t...
Article
The uncertainties associated with crop model inputs can affect the spatio-temporal variance of simulated yields, particularly under suboptimal irrigation. The aim of this study was to determine and quantify the main drivers of irrigated potato yield variance; as influenced by crop management practices as well as climate and soil factors. Using a lo...
Article
Full-text available
Crop models were originally developed for application at the field scale but are increasingly used to assess the impact of climate and/or agronomic practices on crop growth and yield and water dynamics at larger scales. This raises the question of how data aggregation approaches affect outputs when using crop models at large spatial scales. This st...
Article
Full-text available
Forage availability is the most important variable for estimating livestock stock rates in alfalfa-based perennial pastures (PP) and forage winter crops (VI) in the southwest of Entre Rios, Argentina. Despite its importance, there is little information about methods for estimating forage availability under these environments. The objective of this...
Conference Paper
Full-text available
Enhancing and maintaining on-farm diversity represent a potential strategy to improve farming systems sustainability, by reducing the pressure on the natural environment, alleviating farmers' risks and vulnerabilities, and increasing farms resilience. However, farms are complex systems and on-farm diversification, intended as the production of mult...
Data
This dataset has been uploaded on figshare to complement the review article: "Drivers and constraints of on-farm diversity. A review". This review article has been submitted to the journal "Agroforestry for sustainable development". It consists in the data extracted from 97 articles (published between Jan 2010 and Dec 2019) selected for the review...
Article
Extreme temperatures at critical developmental phases reduce grain yield. Combinations of sowing date and cultivar that favour faster development reduce the likelihood of heat stress but increase the risk of frost at critical phases. Current models are unable to predict pulse yield in response to frost and heat, hence our focus on phenology. Our ai...
Conference Paper
Full-text available
Los verdeos tienen la capacidad de ofrecer altas producciones de forrajes en períodos relativamente cortos de tiempo, siendo un complemento de las pasturas permanentes. La demanda de nutrientes está estrechamente asociada con la producción de forrajes por lo que, para alcanzar elevados niveles productivos se debe recurrir al agregado de los mismos....
Article
Input data aggregation affects crop model estimates at the regional level. Previous studies have focused on the impact of aggregating climate data used to compute crop yields. However, little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) on irrigation water requirement (IWR) in cool-temperate and spatially he...
Presentation
Full-text available
Input data aggregation influences crop model estimates at the regional level. Previous studies have focused on the impact of aggregating the climate data used to compute crop yields. Little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) model inputs. This study explores the implications of using coarse resolut...
Conference Paper
Full-text available
We analysed potential yield of lucerne crops and establish hypothesis to further understand yield gaps
Article
Full-text available
In livestock systems of the Argentinean Pampas, its forage production stability relies on the integration of two landcovers, annual forage crop sequences and perennial pastures. Despite the key role that these forage cropping systems have on current milk and beef production, it is unclear how year-by-year variability of precipitation affect forage...
Article
Full-text available
Livestock production systems of Argentina show an ongoing process of change in the composition of their forage base, with a gradual increase in the proportion of their area assigned to forage crop sequences (FCS) –in particular that involving successive winter and summer annual forage crops–, at the expense of the area assigned to perennial pasture...
Article
Full-text available
Background and aims Forage cropping systems may differentially affect the vertical distribution of roots and soil organic C stock (C-OM). In annual crops sequences (ACS) and perennial pastures (PP), we assessed the association between root biomass, C-OM, C in mineral-associated organic matter (C-MAOM), and C in particulate organic matter (C-POM) an...
Article
Full-text available
In recent years, the use of forage crop sequences (FCS) has been increased as a main component into the animal rations of the Argentinian pasture-based livestock systems. However, it is unclear how year-by-year rainfall variability and interactions with soil properties affect FCS dry matter (DM) yield in these environments. Biophysical crop models,...
Article
Full-text available
The Agricultural Production Systems Simulator (APSIM) is a key tool to identify agricultural management practices seeking to simultaneously optimize agronomic productivity and input use efficiencies. The aims of this study were to validate APSIM for prediction of stover and grain yield of corn in four contrasting soils with varied N fertilizer appl...
Thesis
Full-text available
The growing demand for beef and dairy products requires technological options to improve the productivity and resource use efficiency of forages crops with less environmental impact. Livestock production systems based on forage crop sequences (FCS) could be more productive and efficient than those based on perennial pastures (PP). However, there ar...
Article
Full-text available
Simulation models for perennial energy crops such as switchgrass (Panicum virgatum L.) and Miscanthus (Miscanthus x giganteus) can be useful tools to design management strategies for biomass productivity improvement in US environments. The Agricultural Production Systems Simulator (APSIM) is a biophysical model with the potential to simulate the gr...
Article
Full-text available
Modelling plant growth provides a tool for evaluating interactions between environment and management of forage crops for pasture-based livestock systems. Consequently, biophysical and farm systems models are becoming important tools for studying production systems that are based on forage crops. The Agricultural Production Systems Simulator (APSIM...
Conference Paper
Full-text available
En la última década, la actividad lechera ha tenido una fuerte competencia con la agricultura, motivando el cierre de numerosos establecimientos productivos. Para poder contrarrestar este fenómeno y aumentar la rentabilidad de estos sistemas, una opción viable sería la intensificación, produciendo más forraje por unidad de superficie con un uso efi...
Conference Paper
Full-text available
En la última década, la actividad lechera ha tenido que intensificarse para poder competir con la agricultura. En este contexto, existe un fuerte interés de técnicos y productores en generar información del potencial productivo de los cultivos en estos nuevos escenarios. El Clúster Lechero Regional agrupa productores e instituciones lácteas ubicada...
Conference Paper
Full-text available
Uno de los principales intereses del Clúster Lechero Regional, que agrupa empresas lácteas del NO de Santa Fe y SE de Santiago del Estero, es determinar el potencial de producción de forraje en secano de cultivos ensilables. El sorgo es uno de los cultivos de verano más utilizados en esta región como cultivo para silo. Frente a un evento de déficit...

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Projects

Projects (8)
Project
Estimating Crop Model Uncertainty in Agricultural Systems
Project
We will assess how crop-livestock practices impact farm performance regionally under different climate change scenarios. Coupling existing multiple-scaling and remote-sensing techniques with advanced biophysical models we will evaluate drivers of yield variability for feed systems in Argentina, Uruguay and Australia.
Project
Research questions -What are the dominant sources of crop model uncertainty in model predictions? -How much weight does each uncertainty source contribute to the total uncertainty? -Which key gaps exist in understanding, characterising, and assessing crop model uncertainty? -Which are possible ways to reduce crop model uncertainty?