The firmness of the flesh in 27 apple (Malus ×domestica Borkh.) cultivars and selections (genotypes) was measured as an indicator of storage potential at 20 days after harvest under 20 ± 2 °C, 80% ± 5%relative humidity storage conditions. Softening ranged from 9% to 58% of initial values among genotypes after 20 days of storage. In some genotypes, softening was not continuous, a minimum firmness being reached before day 20. After a period of rapid softening, firmness declined to at least 20% of that at harvest. For each genotype, linear regression analysis of firmness changes from harvest until when firmness decreased by 20% was carried out. In genotypes in which firmness did not drop >20% within 20 days of storage, the entire dates to 20 days were used for analysis. The homogeneity of the regression residual variances and their normal distribution was not rejected at P = 0.05, and the linear regression analysis was assumed to be applicable to the change in firmness for each genotype. Results of the regression analysis showed that the regression was significant for all genotypes except one. Therefore, storage potential could be evaluated by comparing the regression coefficient of each genotype.
Hiroshi Iwanami, Makoto Ishiguro, Nobuhiro Kotoda, Sae Takahashi, and Junichi Soejima
L.W. Lass, R.H. Callihan, and D.O. Everson
Predicting sweet corn (Zea mays var. rugosa Bonaf.) harvest dates based on simple linear regression has failed to provide planting schedules that result in the uniform delivery of raw product to processing plants. Adjusting for the date that the field was at 80% silk in one model improved the forecast accuracy if year, field location, cultivar, soil albedo, herbicide family used, kernel moisture, and planting date were used as independent variables. Among predictive models, forecasting the Julian harvest date had the highest correlation with independent variables (R2 = 0.943) and the lowest coefficient of variation (cv = 1.31%). In a model predicting growing-degree days between planting date and harvest, R2 (coefficient of determination) = 0.85 and cv = 2.79%. In the model predicting sunlight hours between planting and harvest, R2 = 0.88 and cv = 6.41%. Predicting the Julian harvest date using several independent variables was more accurate than other models using a simple linear regression based on growing-degree days when compared to actual harvest time.
Hudson Minshew, John Selker, Delbert Hemphill, and Richard P. Dick
Predicting leaching of residual soil nitrate-nitrogen (NO3-N) in wet climates is important for reducing risks of groundwater contamination and conserving soil N. The goal of this research was to determine the potential to use easily measurable or readily available soilclimatic-plant data that could be put into simple computer models and used to predict NO3 leaching under various management systems. Two computer programs were compared for their potential to predict monthly NO3-N leaching losses in western Oregon vegetable systems with or without cover crops. The models were a statistical multiple linear regression (MLR) model and the commercially available Nitrate Leaching and Economical Analysis Package model (NLEAP 1.13). The best MLR model found using stepwise regression to predict annual leachate NO3-N had four independent variables (log transformed fall soil NO3-N, leachate volume, summer crop N uptake, and N fertilizer rate) (P < 0.001, R 2 = 0.57). Comparisons were made between NLEAP and field data for mass of NO3-N leached between the months of September and May from 1992 to 1997. Predictions with NLEAP showed greater correlation to observed data during high-rainfall years compared to dry or averagerainfall years. The model was found to be sensitive to yield estimates, but vegetation management choices were limiting for vegetable crops and for systems that included a cover crop.
The annual yield variation in a Japanese plum (Prunus salicina Lindl.) germplasm collection [with 32 cultivars (cv)] was used to generate regression models to describe fruit yields in terms of climate. A Geographic Information System (GIS) combined with generated regression models was used for a regional analysis of potential areas for growing plums in Zacatecas, Mexico. Three distinct cv groups were obtained by principal component analysis and were included in the study: a) `Frontier'–`Santa Rosa', b) `Ozark Premier'–`Burbank', and c) `Shiro'. The amount of winter chilling and temperatures during bloom time were the climatic conditions most related to yield. `Frontier'–'Santa Rosa' had relatively low chilling requirements (700 chill units) compared to `Ozark Premier'–`Burbank', which required the most chilling (900 chill units). `Shiro' yields were more consistent than those of the other two groups, suggesting that it has less strict requirements and received sufficient chilling every year. High temperatures at bloom reduced fruit yield in all cultivars; however, the dependence of yield on temperatures during bloom in `Shiro' was modified by summer temperatures the previous year, suggesting that temperatures at the floral induction and formation stages affect flower primordia development. Using GIS, three potential areas for growing plums in the region were defined on maps, and the differences in potential yield between the cultivar groups were determined. `Frontier'–`Santa Rosa' may be good choices as plum cultivars for the region because they were the cultivars with the highest potential yield in the largest area; however, the flexibility of the method used allows the user to get a regional gradient of the expected yields with several plum cultivars. Using experimental information and a GIS can extend the applicability of germplasm collection data to regional planning in the establishment of orchards and new fruit industries.
Claudio C. Pasian and J. Heinrich Lieth
Nondestructive dry-weight (DW) estimates of plant parts are important for analyzing production and partitioning patterns of horticultural crops, particularly when repeated measurements of the same plant must be made without affecting growth. Equations were developed for estimating leaf, stem, and flower bud DW (LDW, SDW, and FDW, respectively) from linear measurements of the flowering shoot parts of Rosa hybrida L. `Cara Mia'. We used a stepwise forward polynomial regression to develop a set of equations that represented the data well; from these, we chose equations to make data collection as simple as possible. LDW was computed from leaf length. LDW of the whole shoot was calculated by adding the computed LDW of each leaf on a shoot. Each stem was divided into 30-mm segments and the DW of each segment was correlated with its diameter. SDW was calculated by adding all of the stem segments' DWs. FDW was directly correlated with flower bud diameter. The selected models can be used for rose shoot DW prediction; although in some cases, errors were encountered. Despite these errors, this approach may represent the only feasible method for DW estimation when destructive methods cannot be used.
Douglas A. Hopper and P. Allen Hammer
A central composite rotatable design was used to estimate quadratic equations describing the relationship of irradiance, as measured by photosynthetic photon flux (PPF), and day (DT) and night (NT) temperatures to the growth and development of Rosa hybrida L. in controlled environments. Plants were subjected to 15 treatment combinations of the PPF, DT, and NT according to the coding of the design matrix. Day and night length were each 12 hours. Environmental factor ranges were chosen to include conditions representative of winter and spring commercial greenhouse production environments in the Midwestern United States. After an initial hard pinch, 11 plant growth characteristics were measured every 10 days and at flowering. Four plant characteristics were recorded to describe flower bud development. Response surface equations were displayed as three-dimensional plots, with DT and NT as the base axes and the plant character on the z-axis while PPF was held constant. Response surfaces illustrated the plant response to interactions of DT and NT, while comparisons between plots at different PPF showed the overall effect of PPF. Canonical analysis of all regression models revealed the stationary point and general shape of the response surface. All stationary points of the significant models were located outside the original design space, and all but one surface was a saddle shape. Both the plots and analysis showed greater stem diameter, as well as higher fresh and dry weights of stems, leaves, and flower buds to occur at flowering under combinations of low DT (≤ 17C) and low NT (≤ 14C). However, low DT and NT delayed both visible bud formation and development to flowering. Increased PPF increased overall flower stem quality by increasing stem diameter and the fresh and dry weights of all plant parts at flowering, as well as decreased time until visible bud formation and flowering. These results summarize measured development at flowering when the environment was kept constant throughout the entire plant growth cycle.
Deepu Mathew, Zakwan Ahmed, and N. Singh
The phenomenon of flowering and aerial bulbil production in Asiatic garlic was observed under long photoperiodic conditions of Ladakh, India. Flowers were sterile and the bulbils produced on the umbel were true to type. Observations on a large number of flowering and nonflowering plants have led to the formulation of a precise flowering index (FI) in garlic. Plants with a minimum leaf number of 7, height 25 cm, collar width 0.6 cm, bulb diameter 3.7 cm, bulb weight 22.5 g, and functional leaf area of 182.4 cm2 had only shown the flowering. The flowering index formulated was a product of leaf number, plant height, functional leaf area, and bulb weight. For flowering, FI should be more than 788, and availability of a minimum photoperiod of 4020 hours during a growth period of 11 months was another prerequisite. Nonfulfillment of any one of the factors of flowering, although FI and photoperiod were satisfactory, led to nonflowering. Garlic aerial bulbil yield was positively correlated with leaf number, plant height, bulb weight, bulb diameter, length of flower stalk, 100 seed weight, and head diameter. Following the multiple regression model y = –11.9 – (0.00031 × number of bulbils) + (0.147 × 100 bulbil weight) + (4.95 × head diameter) + (0.0460 × length of flower stalk), aerial bulbil yield prediction was possible at a mean accuracy of 87%.
Carolina Contreras, Nihad Alsmairat, and Randy Beaudry
), accumulated precipitation, log IEC, and starch index using a stepwise multiple linear regression. All data from 2009, 2010, and 2011 were used for the regression. Daily GDD and accumulated precipitation data were collected from Michigan Enviro
Thomas G. Ranney and David Davidson
Dale T. Lindgren, Kent M. Eskridge, James R. Steadman, and Daniel M. Schaaf
Severity of rust (Uromyces appendiculatus) and yield of dry edible beans (Phaseolus vulgaris L.) were recorded for 9 years in west-central Nebraska in fungicidal efficacy trials. A weighted analysis of covariance was used to estimate yield loss due to rust. The model fit the data well (R2=0.94), and the slope over all years had a 19 kg.ha−1 decrease in yield for each 1% increase in severity of rust. Yield response within years occurred only through reduction of rust for most fungicide treatments.