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Ricardo Braga holds the following academic degrees: Bachelor degree in Agronomy / Agricultural Engineering (1993) from Instituto Superior de Agronomia / University of Lisbon, Portugal; Master of Science in Tropical Agricultural Production (1996) from Instituto Superior de Agronomia / University of Lisbon, Portugal; Doctor of Philosophy in Agricultural Operations Management (2000) from University of Florida, ISA. He currently is Assistant Professor (2013 - ) at Instituto Superior de Agronomia, University of Lisbon, where he teaches courses at the Bachelor and Master levels in general agriculture and machinery, precision agriculture, modeling of agricultural systems. He has participated in many projects in the areas of precision agriculture, crop management optimization, and technology dissemination and adoption.
This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.
Gonçalo Rodrigues; Ricardo Braga. Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate. Agronomy 2021, 11, 1207 .
AMA StyleGonçalo Rodrigues, Ricardo Braga. Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate. Agronomy. 2021; 11 (6):1207.
Chicago/Turabian StyleGonçalo Rodrigues; Ricardo Braga. 2021. "Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate." Agronomy 11, no. 6: 1207.
The FAO-56 Penman–Monteith (PM) equation is regarded as the most accurate equation to estimate reference evapotranspiration (ETo). However, it requires a broad range of data that may not be available or of reasonable quality. In this study, nine temperature-based methods were assessed for ETo estimation during the irrigation at fourteen locations distributed through a hot-summer Mediterranean climate region of Alentejo, Southern Portugal. Additionally, for each location, the Hargreaves–Samani radiation adjustment coefficient (kRs) was calibrated and validated to evaluate the appropriateness of using the standard value, creating a locally adjusted Hargreaves–Samani (HS) equation. The accuracy of each method was evaluated by statistically comparing their results with those obtained by PM. Results show that the calibration of the kRs, a locally adjusted HS method can be used to estimate daily ETo acceptably well, with RMSE lower than 0.88 mm day−1, an estimation error lower than 4% and a R2 higher than 0.69, proving to be the most accurate model for 8 (out of 14) locations. A modified Hargreaves–Samani method also performed acceptably for 4 locations, with a RMSE of 0.72–0.84 mm day−1, a slope varying from 0.95 to 1.01 and a R2 higher than 0.78. One can conclude that, when weather data is missing, a calibrated HS equation is adequate to estimate ETo during the irrigation season.
Gonçalo Rodrigues; Ricardo Braga. Estimation of Reference Evapotranspiration during the Irrigation Season Using Nine Temperature-Based Methods in a Hot-Summer Mediterranean Climate. Agriculture 2021, 11, 124 .
AMA StyleGonçalo Rodrigues, Ricardo Braga. Estimation of Reference Evapotranspiration during the Irrigation Season Using Nine Temperature-Based Methods in a Hot-Summer Mediterranean Climate. Agriculture. 2021; 11 (2):124.
Chicago/Turabian StyleGonçalo Rodrigues; Ricardo Braga. 2021. "Estimation of Reference Evapotranspiration during the Irrigation Season Using Nine Temperature-Based Methods in a Hot-Summer Mediterranean Climate." Agriculture 11, no. 2: 124.
The Food and Agricultural Organization of the United Nations (FAO) Penman–Monteith (PM) method is widely regarded as the most effective reference evapotranspiration (ETo) estimator; however, it requires a wide range of data that may be scarce in some rural regions. When feasible relative humidity, solar radiation and wind speed data are unavailable, a temperature-based method may be useful to estimate ETo and provide suitable data to support irrigation management. This study has evaluated the accuracy of two ETo estimations methods: (1) a locally and monthly adjusted Hargreaves–Samani (HS) equation; (2) a simple procedure that only uses maximum temperature and a temperature adjustment coefficient (MaxTET). Results show that, if a monthly adjusted radiation adjustment coefficient (kRs) is calibrated for each site, acceptable ETo estimations (RMSE and R2 equal to 0.79 for the entire region) can be achieved. Results also show that a procedure to estimate ETo based only on maximum temperature performs acceptably, when compared with ETo estimation using PM equation (RMSE = 0.83 mm day−1 and R2 = 0.77 for Alentejo). When comparing these results with the ones attained when adopting a monthly adjusted HS method, the MaxTET procedure proves to be an accurate ETo estimator. Results also show that both methods can be used to estimate ETo when weather data are scarce.
Gonçalo Rodrigues; Ricardo Braga. A Simple Procedure to Estimate Reference Evapotranspiration during the Irrigation Season in a Hot-Summer Mediterranean Climate. Sustainability 2021, 13, 349 .
AMA StyleGonçalo Rodrigues, Ricardo Braga. A Simple Procedure to Estimate Reference Evapotranspiration during the Irrigation Season in a Hot-Summer Mediterranean Climate. Sustainability. 2021; 13 (1):349.
Chicago/Turabian StyleGonçalo Rodrigues; Ricardo Braga. 2021. "A Simple Procedure to Estimate Reference Evapotranspiration during the Irrigation Season in a Hot-Summer Mediterranean Climate." Sustainability 13, no. 1: 349.
Forecasting vineyard yield with accuracy is one of the most important trends of research in viticulture today. Conventional methods for yield forecasting are manual, require a lot of labour and resources and are often destructive. Recently, image-analysis approaches have been explored to address this issue. Many of these approaches encompass cameras deployed on ground platforms that collect images in proximal range, on-the-go. As the platform moves, yield components and other image-based indicators are detected and counted to perform yield estimations. However, in most situations, when image acquisition is done in non-disturbed canopies, a high fraction of yield components is occluded. The present work’s goal is twofold. Firstly, to evaluate yield components’ visibility in natural conditions throughout the grapevine’s phenological stages. Secondly, to explore single bunch images taken in lab conditions to obtain the best visible bunch attributes to use as yield indicators.In three vineyard plots of red (Syrah) and white varieties (Arinto and Encruzado), several canopy 1 m segments were imaged using the robotic platform Vinbot. Images were collected from winter bud stage until harvest and yield components were counted in the images as well as in the field. At pea-sized berries, veraison and full maturation stages, a bunch sample was collected and brought to lab conditions for detailed assessments at a bunch scale.At early stages, all varieties showed good visibility of spurs and shoots, however, the number of shoots was only highly and significantly correlated with the yield for the variety Syrah. Inflorescence and bunch occlusion reached high percentages, above 50 %. In lab conditions, among the several bunch attributes studied, bunch volume and bunch projected area showed the highest correlation coefficients with yield. In field conditions, using non-defoliated vines, the bunch projected area of visible bunches presented high and significant correlation coefficients with yield, regardless of the fruit’s occlusion.Our results show that counting yield components with image analysis in non-defoliated vines may be insufficient for accurate yield estimation. On the other hand, using bunch projected area as a predictor can be the best option to achieve that goal, even with high levels of occlusion.
Gonçalo Filipe Victorino; Ricardo Braga; José Santos-Victor; Carlos M. Lopes. Yield components detection and image-based indicators for non-invasive grapevine yield prediction at different phenological phases. OENO One 2020, 54, 833 -848.
AMA StyleGonçalo Filipe Victorino, Ricardo Braga, José Santos-Victor, Carlos M. Lopes. Yield components detection and image-based indicators for non-invasive grapevine yield prediction at different phenological phases. OENO One. 2020; 54 (4):833-848.
Chicago/Turabian StyleGonçalo Filipe Victorino; Ricardo Braga; José Santos-Victor; Carlos M. Lopes. 2020. "Yield components detection and image-based indicators for non-invasive grapevine yield prediction at different phenological phases." OENO One 54, no. 4: 833-848.
- The objective of this study was to evaluate some crop management strategies to improve silage production by family farmers in the micro-region of Pelotas, southern State of Rio Grande do Sul (RS), Brazil. The seasonal analysis tool of de CSM-CERES-Maize model was used to assess aboveground dry biomass production under rainfed conditions. The simulations comprised scenarios involving four cultivars (Amarelão, AL 30, AG 5011 and AG 122), six nitrogen (N) fertilization strategies, 52 sowing dates, and 21 years of daily weather data. Silage productivity and quality were assessed, and a sowing window was established for each one of the cultivars based on this information. Regardless of the N rate and cultivar, sowings performed between June 26 and December 19 were not at risk of exceeding the deadline for silage harvesting in the region. For sowing occurred on December 19, regardless of the N rate, the average productivity of silage and the average amount of energy per unit area were lower for the creole variety Amarelão. For the same sowing date the average values of energy per unit of biomass weight indicated good silage quality, for all cultivars regardless of the N rate.Keywords: family farming, DSSAT, aboveground biomass, Zea mays L. ABORDAGEM DE MODELAGEM PARA ESTABELECER ESTRATÉGIAS DE PRODUÇÃO DE SILAGEM NA MICRORREGIÃO DE PELOTAS, BRASIL RESUMO - O objetivo deste estudo foi avaliar estratégias de manejo para melhorar a produção de silagem pelos agricultores familiares na microrregião de Pelotas, sul do Rio Grande do Sul (RS), Brasil. A ferramenta de análise sazonal do modelo CSM-CERES-Maize foi usada para avaliar a produção de fitomassa seca em condições de sequeiro. Foi considerado nas simulações um cenário com quatro cultivares (Amarelão, AL 30, AG 5011 e AG 122); seis estratégias de adubação nitrogenada; 52 épocas de semeadura; e 21 anos de dados meteorológicos diários. A produtividade e a qualidade da silagem foram avaliadas e, com base nessas informações, uma janela de semeadura foi estabelecida para cada uma das cultivares. Independentemente da dose de nitrogênio e cultivar, as semeaduras realizadas entre 26 de junho e 19 de dezembro não correm risco de exceder o prazo para a colheita de silagem na região. Para semeadura realizada em 19 de dezembro, independentemente da taxa de N, a produtividade média de silagem e a quantidade média de energia por unidade de área foram menores para a variedade crioula Amarelão. Para esta mesma época de semeadura, os valores médios de energia por unidade de peso de fitomassa seca indicaram boa qualidade de silagem, para todas as cultivares, independentemente da dose de N.Palavras-chave: agricultura familiar, DSSAT, fitomassa seca da parte aérea, Zea mays L.
Tales Antonio Do Amaral; Ricardo Braga; Ana Cláudia Rodrigues De Lima; Camilo De Lelis Teixeira De Andrade. A MODELING APPROACH TO ESTABLISH STRATEGIES FOR MAIZE SILAGE PRODUCTION IN THE MICRO-REGION OF PELOTAS, BRAZIL. Revista Brasileira de Milho e Sorgo 2017, 16, 536 -555.
AMA StyleTales Antonio Do Amaral, Ricardo Braga, Ana Cláudia Rodrigues De Lima, Camilo De Lelis Teixeira De Andrade. A MODELING APPROACH TO ESTABLISH STRATEGIES FOR MAIZE SILAGE PRODUCTION IN THE MICRO-REGION OF PELOTAS, BRAZIL. Revista Brasileira de Milho e Sorgo. 2017; 16 (3):536-555.
Chicago/Turabian StyleTales Antonio Do Amaral; Ricardo Braga; Ana Cláudia Rodrigues De Lima; Camilo De Lelis Teixeira De Andrade. 2017. "A MODELING APPROACH TO ESTABLISH STRATEGIES FOR MAIZE SILAGE PRODUCTION IN THE MICRO-REGION OF PELOTAS, BRAZIL." Revista Brasileira de Milho e Sorgo 16, no. 3: 536-555.
Carlos Lopes; R. Egipto; V. Pedroso; P.A. Pinto; Ricardo Braga; M. Neto. Can berry composition be explained by climatic indices? Comparing classical with new indices in the Portuguese Dão region. Acta Horticulturae 2017, 59 -64.
AMA StyleCarlos Lopes, R. Egipto, V. Pedroso, P.A. Pinto, Ricardo Braga, M. Neto. Can berry composition be explained by climatic indices? Comparing classical with new indices in the Portuguese Dão region. Acta Horticulturae. 2017; (1157):59-64.
Chicago/Turabian StyleCarlos Lopes; R. Egipto; V. Pedroso; P.A. Pinto; Ricardo Braga; M. Neto. 2017. "Can berry composition be explained by climatic indices? Comparing classical with new indices in the Portuguese Dão region." Acta Horticulturae , no. 1157: 59-64.
Maize (Zea mays L.) silage is of major importance for milk production in the Northwest of Portugal. Farmers typically have a variety of maize hybrids to choose from according to cycle length and sowing date. The general recommendation regarding cultivar selection is to use long cycle cultivars for early sowing dates and vice versa. Cycle length, sowing date and temperature regime will determine the harvest date. Because weather regime is unknown at sowing date, there is a need to develop decision support based on historical weather series to help farmers optimize silage production. Production optimization occurs through a better matching of cycle length to sowing date to produce more and better silage at optimal harvest dates. The CERES-Maize crop model was used to establish decision support to help farmers identify the best cultivar and sowing date combinations. Cultivar parameters were estimated from 3-year field experiments involving five planting dates and six cycle lengths (FAO 200 to 700). The model was run with 39 years of historical weather data, simulating 18 sowing dates and 6 cycle lengths. Decision support was developed based on the analysis of simulation outputs and three integrated risk management strategies. Tactical use of guidelines is illustrated with examples. Current limitations of the model for maize silage simulation are also discussed.
Ricardo Braga; M.J. Cardoso; J.P. Coelho. Crop model based decision support for maize (Zea mays L.) silage production in Portugal. European Journal of Agronomy 2008, 28, 224 -233.
AMA StyleRicardo Braga, M.J. Cardoso, J.P. Coelho. Crop model based decision support for maize (Zea mays L.) silage production in Portugal. European Journal of Agronomy. 2008; 28 (3):224-233.
Chicago/Turabian StyleRicardo Braga; M.J. Cardoso; J.P. Coelho. 2008. "Crop model based decision support for maize (Zea mays L.) silage production in Portugal." European Journal of Agronomy 28, no. 3: 224-233.
Predicting the spatial variability of grain yield is of crucial importance for site-specific management (SSM) because it allows for testing of management prescriptions and for correct assessment of agronomic and economic outcomes. One common limitation of crop simulation model use in SSM is the need for accurate values of many inputs from numerous sites in a field. Optimization can be of great help in the estimation of parameters using more easily measured variables such as yield. We have used simulated annealing and compared parameter estimates and yield predictions resulting from the use of two distinct objective function variables: grain yield and soil-water content. Estimating site-specific soil parameters from grain yield measurements led to acceptable errors in grain yield estimates. However, soil-water was not accurately predicted, which made the strategy unreliable. The errors in soil-water were particularly high in the bottom soil layer. In addition, most of the soil-water holding limits were not valid, especially for the lower limit and saturation. Estimating site-specific soil parameters from soil-water content measurements led to acceptable errors in grain yield and soil-water estimates. The estimated soil-water holding limits were valid with an exception of saturation for the intermediate soil layers.
Ricardo Braga; R. P. Braga And J. W. Jones. USING OPTIMIZATION TO ESTIMATE SOIL INPUTS OF CROP MODELS FOR USE IN SITE-SPECIFIC MANAGEMENT. Transactions of the ASAE 2004, 47, 1821 -1831.
AMA StyleRicardo Braga, R. P. Braga And J. W. Jones. USING OPTIMIZATION TO ESTIMATE SOIL INPUTS OF CROP MODELS FOR USE IN SITE-SPECIFIC MANAGEMENT. Transactions of the ASAE. 2004; 47 (5):1821-1831.
Chicago/Turabian StyleRicardo Braga; R. P. Braga And J. W. Jones. 2004. "USING OPTIMIZATION TO ESTIMATE SOIL INPUTS OF CROP MODELS FOR USE IN SITE-SPECIFIC MANAGEMENT." Transactions of the ASAE 47, no. 5: 1821-1831.
Agricultural Systems 68 (2001) 97-112. doi:10.1016/S0308-521X(00)00063-9Received by publisher: 2000-07-31Harvest Date: 2016-01-04 12:22:12DOI: 10.1016/S0308-521X(00)00063-9Page Range: 97-11
Bruno Basso; J.T. Ritchie; F.J. Pierce; R.P. Braga; J.W. Jones. Spatial validation of crop models for precision agriculture. Agricultural Systems 2001, 68, 97 -112.
AMA StyleBruno Basso, J.T. Ritchie, F.J. Pierce, R.P. Braga, J.W. Jones. Spatial validation of crop models for precision agriculture. Agricultural Systems. 2001; 68 (2):97-112.
Chicago/Turabian StyleBruno Basso; J.T. Ritchie; F.J. Pierce; R.P. Braga; J.W. Jones. 2001. "Spatial validation of crop models for precision agriculture." Agricultural Systems 68, no. 2: 97-112.