Improving knowledge and practices in fresh produce chains

A successful supply chain must delivery the right product, value and satisfaction to the end customer, and profitability for its participants. Critical to getting the product right is the practices used to produce and maintain product quality through the supply chain from production to sale to the end customer. This paper describes the approach used by a R&D team to add value to supply chains through improving knowledge and practices. The desired outcome is better produce quality for consumers and more control and less wastage for chain participants. The team worked with specific supply chains to identify areas for improvement and to develop, test and implement improved practices. The knowledge gained was communicated to the industry to gain wider adoption of results. Three conditions were identified as critical for practice change - motivation, knowledge, and capacity for change. For improvement in practices to occur, a business must be motivated and have the knowledge and capacity to improve. Two case studies of working with Australian supply chains (mango and melons) are presented to illustrate our participatory methodology. A key activity is monitoring produce quality and handling practices and conditions to demonstrate to participants the points where quality deterioration occurs in the supply chain. This participatory approach is successful because working with supply chain participants generates knowledge and solutions to real problems. It enables the participants to observe the effect of handling practices and conditions on produce quality, gain knowledge and assess the benefits of improvements. Where existing knowledge is not present, research is conducted to fill the knowledge gaps. IV International Conference on Managing Quality in Chains - The Integrated View on Fruits and Vegetables Quality

High subsoil chloride concentrations reduce soil water extraction and crop yield on Vertosols in north-eastern Australia

Salinity, sodicity, acidity, and phytotoxic levels of chloride (Cl) in subsoils are major constraints to crop production in many soils of north-eastern Australia because they reduce the ability of crop roots to extract water and nutrients from the soil. The complex interactions and correlations among soil properties result in multi-colinearity between soil properties and crop yield that makes it difficult to determine which constraint is the major limitation. We used ridge-regression analysis to overcome colinearity to evaluate the contribution of soil factors and water supply to the variation in the yields of 5 winter crops on soils with various levels and combinations of subsoil constraints in the region. Subsoil constraints measured were soil Cl, electrical conductivity of the saturation extract (ECse), and exchangeable sodium percentage (ESP). The ridge regression procedure selected several of the variables used in a descriptive model, which included in-crop rainfall, plant-available soil water at sowing in the 0.90-1.10 m soil layer, and soil Cl in the 0.90-1.10 m soil layer, and accounted for 77-85% of the variation in the grain yields of the 5 winter crops. Inclusion of ESP of the top soil (0.0-0.10 m soil layer) marginally increased the descriptive capability of the models for bread wheat, barley and durum wheat. Subsoil Cl concentration was found to be an effective substitute for subsoil water extraction. The estimates of the critical levels of subsoil Cl for a 10% reduction in the grain yield were 492 mg cl/kg for chickpea, 662 mg Cl/kg for durum wheat, 854 mg Cl/kg for bread wheat, 980 mg Cl/kg for canola, and 1012 mg Cl/kg for barley, thus suggesting that chickpea and durum wheat were more sensitive to subsoil Cl than bread wheat, barley, and canola.

Estimating and influencing the duration of weed eradication programmes

1. Weed eradication efforts often must be sustained for long periods owing to the existence of persistent seed banks, among other factors. Decision makers need to consider both the amount of investment required and the period over which investment must be maintained when determining whether to commit to (or continue) an eradication programme. However, a basis for estimating eradication programme duration based on simple data has been lacking. Here, we present a stochastic dynamic model that can provide such estimates. 2. The model is based upon the rates of progression of infestations from the active to the monitoring state (i.e. no plants detected for at least 12 months), rates of reversion of infestations from monitoring to the active state and the frequency distribution of time since last detection for all infestations. Isoquants that illustrate the combinations of progression and reversion parameters corresponding to eradication within different time frames are generated. 3. The model is applied to ongoing eradication programmes targeting branched broomrape Orobanche ramosa and chromolaena Chromolaena odorata. The minimum periods in which eradication could potentially be achieved were 22 and 23 years, respectively. On the basis of programme performance until 2008, however, eradication is predicted to take considerably longer for both species (on average, 62 and 248 years, respectively). Performance of the branched broomrape programme could be best improved through reducing rates of reversion to the active state; for chromolaena, boosting rates of progression to the monitoring state is more important. 4. Synthesis and applications. Our model for estimating weed eradication programme duration, which captures critical transitions between a limited number of states, is readily applicable to any weed.Aparticular strength of the method lies in its minimal data requirements. These comprise estimates of maximum seed persistence and infested area, plus consistent annual records of the detection (or otherwise) of the weed in each infestation. This work provides a framework for identifying where improvements in management are needed and a basis for testing the effectiveness of alternative tactics. If adopted, our approach should help improve decision making with regard to eradication as a management strategy.

Effects of seasonal variability on FT-NIR prediction of dry matter content for whole Hass avocado fruit

Fourier Transform (FT)-near infra-red spectroscopy (NIRS) was investigated as a non-invasive technique for estimating percentage (%) dry matter of whole intact 'Hass' avocado fruit. Partial least squares (PLS) calibration models were developed from the diffuse reflectance spectra to predict % dry matter, taking into account effects of seasonal variation. It is found that seasonal variability has a significant effect on model predictive performance for dry matter in avocados. The robustness of the calibration model, which in general limits the application for the technique, was found to increase across years (seasons) when more seasonal variability was included in the calibration set. The R-v(2) and RMSEP for the single season prediction models predicting on an independent season ranged from 0.09 to 0.61 and 2.63 to 5.00, respectively, while for the two season models predicting on the third independent season, they ranged from 0.34 to 0.79 and 2.18 to 2.50, respectively. The bias for single season models predicting an independent season was as high as 4.429 but <= 1.417 for the two season combined models. The calibration model encompassing fruit from three consecutive years yielded predictive statistics of R-v(2) = 0.89, RMSEP = 1.43% dry matter with a bias of -0.021 in the range 16.1-39.7% dry matter for the validation population encompassing independent fruit from the three consecutive years. Relevant spectral information for all calibration models was obtained primarily from oil, carbohydrate and water absorbance bands clustered in the 890-980, 1005-1050, 1330-1380 and 1700-1790 nm regions. These results indicate the potential of FT-NIRS, in diffuse reflectance mode, to non-invasively predict the % dry matter of whole 'Hass' avocado fruit and the importance of the development of a calibration model that incorporates seasonal variation. Crown Copyright (c) 2012 Published by Elsevier B.V. All rights reserved.

Resilience of a eucalypt forest woody understorey to long-term (34–55 years) repeated burning in subtropical Australia

We investigated the effects of annual burning since 1952, triennial burning since 1973, fire exclusion since 1946 and infrequent wildfire (one fire in 61 years) on woody understorey vegetation in a dry sclerophyll eucalypt forest, south-eastern Queensland, Australia. We determined the influence of these treatments, and other site variables (rainfall, understorey density, topsoil C : N ratio, tree basal area, distance to watercourse and burn coverage) on plant taxa density, richness and composition. The richness of woody understorey taxa 0–1 m in height was not affected by burning treatments, but richness of woody plants 1–7.5 m in height was lower in the annually burnt treatment than in the triennially burnt treatment from 1989 to 2007. Fire frequency and other site variables explained 34% of the variation in taxa composition (three taxon groups and 10 species), of which 33% of the explained variance was explained by fire treatment and 46% was explained by other site variables. Annual burning between 1974 and 1993 was associated with lower understorey densities mainly due to reduced densities of eucalypts 1–7.5 m in height. Triennial burning during the same period was associated with higher densities of eucalypts 0–7.5 m in height relative to the annually burnt and unburnt treatments. Most woody taxa persisted in the frequently burnt treatments through resprouting mechanisms (e.g. lignotuberous regeneration), and fire patchiness associated with low-intensity burning was also found to be important. Persistence of plants <1 m tall demonstrates the resilience of woody taxa to repeated burning in this ecosystem, although they mainly exist in a suppressed growth state under annual burning.