Abstract
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 afield. 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.

Abstract
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.

Abstract
ABSTRACT - 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.

Abstract
Climatic data collected between 1963 and 2010 in the Portuguese Dão wine growing region were analysed to evaluate the relationship between climatic indices, seasonal weather and the berry composition of the red cultivar 'Touriga Nacional'. The trends over time for the classical temperature-based indices (growing season temperature, growing degree days, biologically effective degree days, Huglin index and cool night index) were significantly positive and can be mostly attributed to the effects of climate change. The dryness index showed a negative trend although not significant. These indices were able to explain 9 and 45% of the variability in total soluble solids and titratable acidity, respectively, using a multiple stepwise regression analysis. The proportion of explained variability was much improved, to 52% for total soluble solids and 65% for titratable acidity, when new climatic indices were used. The new indices resulted from the generalisation of the classical indices based upon chronological time specification as well as taking into consideration the phenological time instead. Our data shows that the classical climatic indices were not able to sufficiently explain the berry composition, and that new climatic indices should be used for a better understanding of the climate drivers of berry quality.

Abstract
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.