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Although the effects of salinity on yield of tomato (Lycopersicon esculentum Mill.) grown under arid and semiarid conditions are well known, little information is available on the effects of salinity on crops grown in more humid conditions. In Florida, availability of high-quality water for irrigation may be reduced because of increased domestic consumption and sea water intrusion. Two greenhouse studies were conducted to determine the influence of irrigation system and water quantity and quality on the growth of tomato and snap bean (Phaseolus vulgaris L.). Bean plant heights and weights were greater with drip irrigation than with subirrigation. Bean seed germination percentage, plant height, and shoot weight decreased linearly with an increase in electrical conductivity of irrigation water (EC_i) from 1 to 4 dS·m^-1. Tomato leaf water potential and plant height decreased linearly with increasing salinity. Tomato stem and leaf weights were greatest at the intermediate salinity (2 dS·m^-1) during initial growth, and stem weights decreased linearly with increased salinity during flowering. With drip irrigation, concentration of N for both crops decreased and concentration of P increased with an increase in water application from 0.75 to 1.5 times the estimated evapotranspiration rate (ET_a). Tomato and bean tissue Na concentrations increased linearly with increased salinity. Total fruit yield and average fruit weight decreased linearly in tomato, and marketable fruit yield decreased quadratically with increased salinity.

The interactive effects of irrigation rate and nitrogen concentration of the irrigation water on the growth of seedlings of two citrus rootstocks were studied. Four-month old seedlings of Swingle citrumelo [Citrus paradisi Macf. × Poncirus trifoliata (L.) Raf.] and Volkamer lemon (C. volkameriana Ten. & Pasq.) were grown for ≈10 months in square citripots filled with a Candler fine sand. Plants were irrigated at 0.5, 0.75 or 1.0 times the evapotranspiration rate. Irrigation was applied using water containing 0, 7, 21, or 63 ppm nitrogen. Plant growth increased with irrigation rate and nitrogen concentration. Evapotranspiration rates, as determined from weight losses of reference plants, increased with nitrogen rate. Overall plant growth and weekly evaporation rates were greater with Volkamer than with Swingle. Leaf senescence of Swingle was more pronounced at low irrigation rates and/or low nitrogen concentrations than it was with Volkamer. Increasing nitrogen concentration of the irrigation water during the winter months reduced leaf senescence of both Swingle and Volkamer seedlings, and also promoted continuous growth in Volkamer. Leaf growth of Swingle ceased during the winter months, regardless of the nitrogen concentration of the irrigation water.

Improving our understanding of processes that control and limit nitrogen uptake by citrus can provide a scientific basis for enhancing nitrogen fertilizer use efficiency. Nitrogen uptake dynamics of two rootstock seedlings will be compared to those of young budded trees. Three-month old Swingle citrumelo [Citrus paradisi Macf. × Poncirus trifoliata (L.) Raf.] and Volkamer lemon (C. volkameriana Ten. & Pasq.) trees were planted in PVC columns filled with a Candler fine sand. Field experiments were conducted using 4-year-old `Hamlin' orange trees [Citrus sinensis (L.) Osb.] grafted on `Carrizo' [C. sinensis × Poncirus trifoliata (L.) Raf.] or on Swingle citrumelo. Trees were either grown in solution culture using 120-L PVC containers or in 900-L PVC tubs filled with a Candler fine sand. Additional trees were planted in the field during Spring 1998. Two lateral roots per tree were trained to grow in slanted, partly burried, 20-L PVC columns filled with a Candler fine sand. Nitrogen uptake from the soil was determined by comparing the residual N extracted by intensive leaching from planted units with that of non-planted (reference) units. With the application of dilute N solutions (7 mg N/L), plants reduced N concentrations to near-zero N concentrations within days. Applying N at higher concentrations (70 or 210 mg N/L) resulted in higher initial uptake rates, increased residual soil N levels, and reduced nitrogen uptake efficiency. Contributions of passive uptake to total nitrogen uptake ranged from less than 5% at soil solution concentrations around 3 ppm N to 20% to 30% at concentrations of 60 ppm N.

Understanding the growth pattern of fibrous, orange tree [Citrus sinensis (L.) Osbeck] roots enables proper fertilizer placement to improve nutrient uptake efficiency and to reduce nutrient leaching below the root zone. The objective of this study was to develop relationships defining citrus fibrous root length density (FRLD) as a function of soil depth, distance from the tree trunk, and tree size. Root systems of 18 trees with tree canopy volumes (TCV) ranging from 2.4 to 34.3 m³ on two different rootstocks and growing in well-drained sandy soils were sampled in a systematic pattern extending 2 m away from the trunk and 0.9 m deep. Trees grown on Swingle citrumelo [Citrus paradisi Macf. × Poncirus trjfoliata (L.) Raf.] rootstock had significantly greater FRLD in the top 0.15 m than trees on Carrizo citrange (C. sinensis × P. trifoliata). Conversely, Carrizo citrange had greater FRLD from 0.15 to 0.75 m below the soil surface. FRLD was significantly greater for ‘Hamlin’ orange trees grown on Swingle citrumelo rootstock at distances less than 0.75 m from the tree trunk compared with those on Carrizo citrange. Fibrous roots of young citrus trees developed a dense root mat above soil depths of 0.3 m that expanded both radially and with depth with time as trees grow and TCV increased. Functional relationships developed in this study accounted for changes in FRLD with increase in tree size.

Current production of sweet corn (Zea mays L.) in the United States is 4.0 million Mg with a value of $807 million. The fresh market component amounts to three-fourths of this value with California, Florida, and Georgia harvesting half of the U.S. fresh market production. Existing maize simulation models have limited potential to assist sweet corn production as a result of the distinctive nature of the marketed end product (i.e., fresh market ears versus dry mature kernels). The purpose of this study was to develop a sweet corn simulation model. The Cropping System Model-Crop-Environment Resource Synthesis CSM-CERES-Maize simulation model, version 4.0, was modified to improve the simulation of ear growth, to predict ear fresh market yield, and to predict fresh market ear quality according to U.S. standards. A field experiment conducted in Florida in 2003 was used for model development. Five nitrogen fertilization levels (0, 67, 133, 200, and 267 kg·ha⁻¹ N) were applied to a sh2-based commercial hybrid with a Bt gene sown at 8.2 plants/m². Three additional experiments conducted in 2002, 2004, and 2005 provided independent data to evaluate the new model. In 2002, the treatments and hybrid were the same as mentioned, but the population density was 5.5 plants/m². A yellow sh2-based hybrid with a Bt gene was planted at 6.1 plants/m² in 2004. In 2005, a bicolor sh2-based hybrid with a Bt gene was planted at 8.1 plants/m². The 2004 and 2005 experiments had 100% and 150% of the Florida N recommendations applied to the crop. Results indicated that the new model was able to simulate adequately crop and ear growth of sweet corn. The ear dry weight simulation was improved as indicated by 30% reduction of root mean square of the error (RMSE) when the new model was compared with the original CSM-CERES-Maize. Total ear fresh weight yield and marketable yield were also simulated reasonably well with RMSE values of 3367 and 3502 kg·ha⁻¹, respectively. The simulation of ear quality was consistently overpredicted at intermediate levels of N fertilization, indicating the need to further examine the impact of limited N on ear quality.

Growth and nitrogen (N) accumulation relationships based on tree size, rather than age, may provide more generic information that could be used to improve sweet orange [Citrus sinensis (L.) Osbeck] N management. The objectives of this study were to determine how orange trees accumulate and distribute biomass and N as they grow, investigate yearly biomass and N changes in mature orange trees, determine rootstock effect on biomass and N distribution, and to develop simple mathematical models describing these relationships. Eighteen orange trees with canopy volumes ranging between 2 and 43 m³ were dissected into leaf, twig, branch, and root components, and the dry weight and N concentration of each were measured. The N content of each tree part was calculated, and biomass and N distribution throughout each tree were determined. The total dry biomass of large (mature) trees averaged 94 kg and contained 0.79 kg N. Biomass allocation was 13% in leaves, 7% in twigs, 50% in branches/trunk, and 30% in roots. N allocation was 38% in leaves, 8% in twigs, 27% in branches/trunk, and 27% in roots. For the smallest tree, above-/below-ground distribution ratios for biomass and N were 60/40 and 75/25, respectively. All tree components accumulated biomass and N linearly as tree size increased, with the above-ground portion accumulating biomass about 2.5 times faster than the below-ground portion due mostly to branch growth. The growth models developed are currently being integrated in a decision support system for improving fertilizer use efficiency for orange trees, which will provide growers with a management tool to improve long-term N use efficiency in orange orchards.

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.

Modeling the growth of field-grown tomato (Lycopersicon esculentum Mill.) should assist researchers and commercial growers to outline optimal crop management strategies for specific locations and production systems. A generic crop-growth model (CROPGRO) was previously adapted to simulate the growth of fresh-market tomato under field conditions. Plant growth and development of field-grown tomato, and fruit yields, will be outlined and compared to model predictions for a number of locations in Florida, nitrogen fertilizer rates, and irrigation management practices. Possible application of the model to quantify effects of crop management on crop production will be discussed using simulated yield values for a wide range of environmental conditions.