Gustavo Ovando | Universidad Nacional de Córdoba (original) (raw)

Papers by Gustavo Ovando

Agricultural Research, Dec 22, 2023

Ponencia presentada en la 3° Reunion Internacional de Riego : Rendimientos potenciales con uso ef... more Ponencia presentada en la 3° Reunion Internacional de Riego : Rendimientos potenciales con uso eficiente de agua e insumos. Cordoba, Argentina, 30 y 31 de octubre de 2012.

Agricultura Tecnica, Mar 1, 2005

Pesquisa Agropecuaria Brasileira, Feb 1, 2006

International Journal of Remote Sensing, May 8, 2014

ABSTRACT Crop residues on the soil surface provide not only a barrier against water and wind eros... more ABSTRACT Crop residues on the soil surface provide not only a barrier against water and wind erosion, but they also contribute to improving soil organic matter content, infiltration, evaporation, temperature, and soil structure, among others. In Argentina, soybean (Glycine max (L.) Merill) and corn (Zea mays L.) are the most important crops. The objective of this work was to develop and evaluate two different types of model for estimating soybean and corn residue cover: neural networks (NN) and crop residue index multiband (CRIM) index, from Landsat images. Data of crop residue were acquired throughout the summer growing season in the central plains of Córdoba (Argentina) and used for training and validating the models. The CRIM, a linear mixing model of composite soil and residue, and the NN design, included reflectance and digital numbers from a combination of different TM bands to estimate the fractional residue cover. The results show that both methodologies are appropriate for estimating the residue cover from Landsat data. The best developed NN model yielded R2 = 0.95 when estimating soybean and corn residue cover fraction, whereas the best fit using CRIM yielded R2 = 0.87; in addition, this index is dependent on the soil and residue lines considered.

Agricultura Tecnica, Dec 1, 2007

El modelo CROPGRO-Peanut es una estructura modular del DSSAT que simula fenología y productividad... more El modelo CROPGRO-Peanut es una estructura modular del DSSAT que simula fenología y productividad del cultivo de maní bajo condiciones ambientales y prácticas de manejo, y ha demostrado alta precisión ante simulaciones de diversos escenarios. Para su calibración, el modelo requiere información de suelo, clima y prácticas de manejo; pero además, coeficientes genéticos específicos de cada cultivar. El objetivo de este trabajo fue calibrar los coeficientes genéticos del cultivar ASEM 400 INTA asociados a fenología, producción de biomasa y rendimiento, mediante simulaciones del modelo CROPGRO-Peanut.INTA. CR Córdoba. EEA Manfredi.Fil: Haro Juarez, Ricardo Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi. Agronomía. Grupo Manejo de Cultivos; ArgentinaFil: Ovando, Gustavo Gabriel. Universidad Nacional de Córdoba. Facultad de Ciencias Agropecuarias; ArgentinaFil: Mortigliengo, S. AGRO ARG SRL; ArgentinaFil: de la Barrera, Guillermo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Manfredi; Manfred

In this work models based on neural networks of the backpropagation type were developed in order ... more In this work models based on neural networks of the backpropagation type were developed in order to predict the occurrence of frosts from meteorological data such as temperature, relative humidity, cloudiness and wind direction and speed. The training and the validation of the networks were made on the basis of 24 years of meteorological data corresponding to the Río Cuarto station, Córdoba, Argentina. These data were grouped as follows: 10 years for the training data set and 14 years for the validation data set. Different models were built to evaluate the performance of the networks when different numbers of input variables and/or neurons in the hidden layer are used, and the probabilities of success in the prediction results on considering different input variables. In the models used, the percentage of days with prediction error was 2%, approximately, for the 14 years of application; when effective frosts days are considered the percentage varies between 10 and 23%, for the same period. The simulation results demonstrated the good performance and the relevance of this methodology for the estimation of the behavior of non-linear phenomena like frosts.

Journal of Agriculture and Ecology Research International, Jan 10, 2016

2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)

Resumen. Los modelos de simulación de cultivos permiten repr esentar su desarrollo y rendimiento,... more Resumen. Los modelos de simulación de cultivos permiten repr esentar su desarrollo y rendimiento, considerando además la ap icación de nuevas tecnologías o condiciones de manejo. Los dat os provenientes de satélites como inputs en modelos resuelven el probl ema de faltantes o errores. El objetivo de este trabajo fue evaluar cambios en la estimación del rendimiento de soja con CROPGRO soybean, al con siderar datos de radiación solar obtenidos de imágenes CERES. La apl icación se realizó para 6 campañas agrícolas en Oliveros (Santa Fe). L os resultados mostraron que el rendimiento estimado al utilizar l a adiación solar de CERES es equivalente al obtenido a partir de la rad iación registrada, cuando el porcentaje de sustitución de datos no sup era el 30%, independientemente del año, fecha de siembra y grup o de madurez. Se observó que en el 45% de las campañas, que al reemp lazar el 100% de los datos se obtienen valores menores al 3% del por centaje de la raíz del error cuadrático medio.

Avances en Energías Renovables y Medio Ambiente, 2009

Agriscientia, Jun 30, 2022

Air temperature is a key variable in a wide range of environmental applications, including land-a... more Air temperature is a key variable in a wide range of environmental applications, including land-atmosphere interaction, climate change research and hydrology and crop growth models, among others. The objective of this study was to estimate daily air maximum (Tmax) and minimum (Tmin) temperatures, based on MODIS AQUA/TERRA land surface temperature (LST), NDVI, extraterrestrial solar radiation and precipitation data. Artificial neural networks (ANN) and random forests (RF) models were developed to predict these temperatures covering weather stations in Córdoba (Argentina) for 2018-2020. The results show that RF and ANN machine learning algorithms are capable of modeling non-linear relationships between registered temperatures and LST MODIS data, in a very robust way. The validation of the models confirms that Tmax and Tmin can be accurately estimated using, jointly or separately, AQUA and TERRA LST. The best models present determination coefficients equal to 0.81/0.91 and root mean square error of 2.7/2.1 ºC for Tmax/Tmin, when using AQUA LST day/ night satellite overpass time data, respectively. The robustness and confidence of the models developed, and the ease and free accessibility of input data at a global scale, suggest that these methodologies have the potential to be applied to other regions.

Agriscientia, Jun 29, 2018

Development of models for crop yield prediction using remote sensing allows accurate, reliable an... more Development of models for crop yield prediction using remote sensing allows accurate, reliable and timely estimations over large areas. Particularly, this information is necessary to ensure the adequacy of a nation's food supply as well as to aid policy makers and farmers. In Argentina, soybean (Glycine max (L.) Merr.) and corn (Zea mays L.) are the most important crops. The goal of this research was to develop and evaluate linear and non-linear models to estimate crop yield from satellite data. Particularly, we proposed and applied those models to obtain soybean and corn yield in the central region of Córdoba (Argentina) using Landsat and SPOT images. The models were designed taking into account all or some bands included in the images from one or both satellites. Results showed that models provided a good fit when all images are used, being superior the accuracy obtained by neural networks (NN). For soybean, the best estimation presented a coefficient of determination equal to 0.90 with NN and 0.82 with multiple linear regression models, and for corn 0.92 and 0.88, respectively. This study concludes that Landsat and SPOT images can be effectively used to predict, in early to mid-season crop growth stages, corn and soybean yield.

South American Sciences, Nov 10, 2022

Almost 99% of Argentine peanut production is localized in Córdoba province, mainly under a rainfe... more Almost 99% of Argentine peanut production is localized in Córdoba province, mainly under a rainfed regime. In this region, rainfall fluctuations can lead to droughts of varying severity. The peanut optimum sowing date can be determined using a crop growth model and historical climatic data, estimating the impact of drought on yields. This simulation aimed to identify optimum sowing dates of peanuts growing under three available water contents at seeding, in Córdoba. A secondary objective was to determine the responses of yield and dry matter to crop evapotranspiration and transpiration for the different treatments. CROPGRO-Peanut model seasonal analysis was carried out. For this, weather data from 1973 to 2019 at Manfredi Experimental Station, and crop coefficients of cultivar ASEM 485 INTA were used. The soil employed was a silty loam Typic Haplustoll. Treatments were: three available water contents up to 150 cm deep (30%, 60%, and 100%) at seeding, and two sowing dates (21/Oct. and 9/Dec.). The optimal planting date, determined by CSM-CROPGRO-peanut for Córdoba is influenced by the soil water content at sowing. In both sowing dates, a higher median seed yield and a smaller interquartile difference were determined when soil water content increased. In each soil moisture, the late sowing date presented lower median values but less variability. The number of bad years was 15 when the initial moisture content was 30%, regardless of the sowing date. The remaining planting date-initial water combinations did not determine bad years. Increases in early/late planting ranged from 19/12 36/31 and 46/42 good years when increasing moisture content. The highest water content at planting is associated with luxury consumption. Dry matter production/yield best fits a linear relationship when compared to transpiration rather than crop evapotranspiration. This behavior is accentuated in the early planting date.