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- Author or Editor: Kenneth Boote x
Citrus is one of the most important crops in Florida. During the past decade, increased international competition and urban development, diseases, and more stringent environmental regulations have greatly affected the citrus industry. Citrus growers transitioning to organic production may benefit from premium prices, but they also face many challenges, including development of effective weed management strategies. Cover crops (CC) may constitute an environmentally sound alternative for improved weed management in organic systems. Two field experiments were conducted at Citra in north central Florida from 2002 to 2005, to evaluate the effectiveness of annual and perennial CC to suppress weeds in organic citrus groves. To quantify and compare the effectiveness of CC to suppress weed growth, a new weed suppression assessment tool, the cover crop/weed index (CCWI), was developed using the ratio of biomass accumulation of CC and weeds. Annual summer CC accumulated more biomass in comparison with winter CC. Sunnhemp (Crotalaria juncea L.), hairy indigo (Indigofera hirsuta L.), cowpea (Vigna unguiculata L. Walp.), and alyceclover (Alysicarpus vaginalis L.) all provided excellent weed suppression, which was superior to tillage fallow. Single-species winter CC did not always perform consistently well. Use of winter CC mixtures resulted in more consistent overall CC performance, greater dry matter production, and more effective weed suppression than single species of CC. Initial perennial peanut (PP) growth was slow, and summer planting of PP (Arachis glabrata Benth.) was determined to be the most effective date in terms of weed suppression, which was improved gradually over time, but all planting dates resulted in slow initial growth compared with annual CC. For both PP and annual CC, weed biomass typically was inversely related to CC dry weight accumulation resulting from competition for resources. The CCWI was a suitable tool to quantify CC performance in terms of weed suppression.
Fruit quality is of increasing importance for consumers but is a complex trait for growers, as it is affected by environment, genotype, and crop management interactions. Decision support tools, such as computer models that simulate crop growth and development can help optimize production but require further improvement to simulate quality aspects. The goal of this study was to apply the newly developed CROPGRO-Strawberry model in the Decision Support System for Agrotechnology Transfer (DSSAT) model framework and develop a module for the dynamic prediction of quality traits for strawberry. Experimental data from Florida with quality measurements from multiple harvests were correlated with indices based on preharvest weather conditions (temperature, radiation, rainfall) and simulated model parameters (evapotranspiration) during fruit development. Two quality relationships based on linear equations were identified and integrated into the model to simulate strawberry fruit soluble solids content (r 2 = 0.89, d = 0.97) and titratable acidity (r 2 = 0.55, d = 0.85) based on preharvest temperature. A strategic analysis with historical weather data for a subtropical growing region over a 10-year period showed the importance of seasonal climate variability for simulated strawberry yield and fruit quality across different harvest months. The improved CROPGRO-Strawberry model is the first process-based crop model to predict selected quality traits across multiple harvests throughout the season and can be extended to other crop models for which quality traits are important.
Parameterizing crop models for more accurate response to climate factors such as temperature is important considering potential temperature increases associated with climate change, particularly for tomato (Lycopersicon esculentum Mill.), which is a heat-sensitive crop. The objective of this work was to update the cardinal temperature parameters of the CROPGRO-Tomato model affecting the simulation of crop development, daily dry matter (DM) production, fruit set, and DM partitioning of field-grown tomato from transplanting to harvest. The main adaptation relied on new literature values for cardinal temperature parameters that affect tomato crop phenology, fruit set, and fruit growth. The new cardinal temperature values are considered reliable because they come from recent published experiments conducted in controlled-temperature environments. Use of the new cardinal temperatures in the CROPGRO-Tomato model affected the rate of crop development compared with prior default parameters; thus, we found it necessary to recalibrate genetic coefficients that affect life cycle phases and growth simulated by the model. The model was recalibrated and evaluated with 10 growth analyses data sets collected in field experiments conducted at three locations in Florida (Bradenton, Quincy, and Gainesville) from 1991 to 2007. Use of modified parameters sufficiently improved model performance to provide accurate prediction of crop and fruit DM accumulation throughout the season. Overall, the average root mean square error (RMSE) over all experiments was reduced 44% for leaf area index, 71% for fruit number, and 36% for both aboveground biomass and fruit dry weight simulations with the modified parameters compared with the default. The Willmott d index was higher and was always above 0.92. The CROPGRO-Tomato model with these modified cardinal temperature parameters will predict more accurately tomato growth and yield response to temperature and thus be useful in model applications.