Grape Harvest

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Chalutzim at Harvest

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Chalutzim at Harvest

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Threshing the Harvest

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Quantitative methods to measure pigmentation variation in farmed Giant Tiger Prawns, Penaeus monodon, and the effects of different harvest methods on cooked colour

Cooked prawn colour is known to be a driver of market price and a visual indicator of product quality for the consumer. Although there is a general understanding that colour variation exists in farmed prawns, there has been no attempt to quantify this variation or identify where this variation is most prevalent. The objectives of this study were threefold: firstly to compare three different quantitative methods to measure prawn colour or pigmentation, two different colorimeters and colour quantification from digital images. Secondly, to quantify the amount of pigmentation variation that exists in farmed prawns within ponds, across ponds and across farms. Lastly, to assess the effects of ice storage or freeze-thawing of raw product prior to cooking. Each method was able to detect quantitative differences in prawn colour, although conversion of image based quantification of prawn colour from RGB to Lab was unreliable. Considerable colour variation was observed between prawns from different ponds and different farms, and this variation potentially affects product value. Different post-harvest methods prior to cooking were also shown to have a profound detrimental effect on prawn colour. Both long periods of ice storage and freeze thawing of raw product were detrimental to prawn colour. However, ice storage immediately after cooking was shown to be beneficial to prawn colour. Results demonstrated that darker prawn colour was preserved by holding harvested prawns alive in chilled seawater, limiting the time between harvesting and cooking, and avoiding long periods of ice storage or freeze thawing of uncooked product.

Harvest weed seed control: is there a role in northern region farming systems?

Harvest weed seed control (HWSC) is a new approach which targets weed seed removal and/or destruction during the crop harvest operation. The success of HWSC is dependant upon weed seed retention at harvest. To identify and define the potential value of HWSC in northern farming systems, we conducted a field survey. In total 1400 transects across 70 paddocks assessed weed distribution, density and seed production at harvest time in wheat, chickpea and sorghum crops. Seventy weed species were identified, of which many had large seed numbers retained at crop harvest. The most prevalent included common sowthistle, flaxleaf fleabane, awnless barnyard grass, wild oat, and African turnip weed. Our field survey has shown there is a role for HWSC in the northern farming system. Therefore the efficacy of specific HWSC systems on problematic weeds should be evaluated in the northern region.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

An improved simulation model to predict pre-harvest aflatoxin risk in maize

Aflatoxin is a potent carcinogen produced by Aspergillus flavus, which frequently contaminates maize (Zea mays L.) in the field between 40° north and 40° south latitudes. A mechanistic model to predict risk of pre-harvest contamination could assist in management of this very harmful mycotoxin. In this study we describe an aflatoxin risk prediction model which is integrated with the Agricultural Production Systems Simulator (APSIM) modelling framework. The model computes a temperature function for A. flavus growth and aflatoxin production using a set of three cardinal temperatures determined in the laboratory using culture medium and intact grains. These cardinal temperatures were 11.5 °C as base, 32.5 °C as optimum and 42.5 °C as maximum. The model used a low (≤0.2) crop water supply to demand ratio—an index of drought during the grain filling stage to simulate maize crop's susceptibility to A. flavus growth and aflatoxin production. When this low threshold of the index was reached the model converted the temperature function into an aflatoxin risk index (ARI) to represent the risk of aflatoxin contamination. The model was applied to simulate ARI for two commercial maize hybrids, H513 and H614D, grown in five multi-location field trials in Kenya using site specific agronomy, weather and soil parameters. The observed mean aflatoxin contamination in these trials varied from <1 to 7143 ppb. ARI simulated by the model explained 99% of the variation (p ≤ 0.001) in a linear relationship with the mean observed aflatoxin contamination. The strong relationship between ARI and aflatoxin contamination suggests that the model could be applied to map risk prone areas and to monitor in-season risk for genotypes and soils parameterized for APSIM.

The effect of post harvest handling on selected native food plants

A commercial issue currently facing native plant food producers and food processors, and identified by the industry itself, is that of delivering quality products consistently and at reasonable cost to end users based on a sound food technology and nutrition platform. A literature survey carried out in July 2001 by the DPI&F’s Centre for Food Technology, Brisbane in collaboration with the University of Queensland to collect the latest information at that time on the functional food market as it pertained to native food plants, indicated that little or no work had been published on this topic. This project addresses two key RIRDC sub program strategies: to identify and evaluate processes or products with prospects of commercial viability and to assist in the development of integrated production, harvesting, processing and marketing systems. This project proposal also reflects a key RIRDC R&D issue for 2002-2003; that of linking with prospective members of the value chain. The purpose of this project was to obtain chemical data on the post harvest stability of functional nutritional components (bio actives) in commercially available, hand harvested bush tomato and Kakadu plum. The project concentrated on evaluating bioactive stability as a measure of ingredient quality.

Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials

Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.

Harvest: A biotextile future

- Description of the work Harvest: A biotextile future consists of four bags constructed from kombucha, each utilizing a different approach to this material. The kombucha material is a byproduct of the fermented green tea, kombucha, and is comprised of a symbiotic culture of bacteria and yeast (SCOBY) that forms a fast growing curd or pellicle on the surface of the tea. This pellicle is harvested, washed, and dried to make a material with characteristics that can range between leather and paper in handle. The pellicle is one hundred per cent cellulose, with the individual fibres growing together to produce a durable and strong non-woven textile. Techniques explored with the dry kombucha material include folding, stitching, and laser etching. The final bags were designed with reference to classic tropes of fashion accessories: the briefcase, the clutch, the valise and the handbag. The valise included three jars in which the kombucha was displayed as ‘growing’ within the bag. - Research Background This work sits within an emerging field of practice in which fashion design intersects with biotechnology. Designers such as Suzanne Lee have explored constructing garments from bacteria byproducts, and bio-artists Oron Catts and Ionat Zurr have created ‘victimless leather’ grown from cultured cells. Although still speculative, these collaborations between science and design point to new material applications for fashion. Our work contributes to this area through testing both the growing of the textile and its application to construct durable fashion artefacts. - Research Contribution Harvest: A biotextile future makes two contributions to new knowledge in the area of design for sustainability within fashion. The first contribution lies in extending the technical experimentation required to grow and manipulate the textile. For the briefcase, the pattern shape was ‘grown’ into the required shape, using a shaped container. Other techniques used in the bags included weaving, folding and laser etching the material to extend its functional and decorative properties. Experimentation with the growing and drying of the material led to the production of a wide range of physical properties, in which the material was more brittle or flexible as required. The second research contribution lies in the proposal of this material for use in durable fashion accessories. The material is still speculative and small-scale in production, however the four bags illustrate the potential for kombucha as a biodegradable alternative to leather or synthetic materials. - Research Significance This interplay of science and design research opens up an exploration for a speculative future of sustainable, biodegradable textiles using live bacteria to enable ‘homegrown’ vegan apparel. The collaborators on this project include scientist Peter Musk and fashion designers Alice Payne and Dean Brough. Harvest: A biotextile future was exhibited at the State Library of Queensland’s Asia Pacific Design Library, 1-5 November 2015, as part of The International Association of Societies of Design Research’s (IASDR) biannual design conference. The work was chosen for display by a panel of experts, based on the criteria of design innovation and contribution to new knowledge in design.

Pre-harvest measurement of pine stands for sawing production planning.

To enhance the utilization of the wood, the sawmills are forced to place more emphasis on planning to master the whole production chain from the forest to the end product. One significant obstacle to integrating the forest-sawmill-market production chain is the lack of appropriate information about forest stands. Since the wood procurement point of view in forest planning systems has been almost totally disregarded there has been a great need to develop an easy and efficient pre-harvest measurement method, allowing separate measurement of stands prior to harvesting. The main purpose of this study was to develop a measurement method for pine stands which forest managers could use in describing the properties of the standing trees for sawing production planning. Study materials were collected from ten Scots pine stands (Pinus sylvestris) located in North Häme and South Pohjanmaa, in southern Finland. The data comprise test sawing data on 314 pine stems, dbh and height measures of all trees and measures of the quality parameters of pine sawlog stems in all ten study stands as well as the locations of all trees in six stands. The study was divided into four sub-studies which deal with pine quality prediction, construction of diameter and dead branch height distributions, sampling designs and applying height and crown height models. The final proposal for the pre-harvest measurement method is a synthesis of the individual sub-studies. Quality analysis resulted in choosing dbh, distance from stump height to the first dead branch (dead branch height), crown height and tree height as the most appropriate quality characteristics of Scots pine. Dbh and dead branch height are measured from each pine sample tree while height and crown height are derived from dbh measures by aid of mixed height and crown height models. Pine and spruce diameter distribution as well as dead branch height distribution are most effectively predicted by the kernel function. Roughly 25 sample trees seems to be appropriate in pure pine stands. In mixed stands the number of sample trees needs to be increased in proportion to the intensity of pines in order to attain the same level of accuracy.

Estimation of cutting volume with three inventory methods for harvest planning in Canadian boreal forests.

Two methods of pre-harvest inventory were designed and tested on three cutting sites containing a total of 197 500 m3 of wood. These sites were located on flat-ground boreal forests located in northwestern Quebec. Both methods studied involved scaling of trees harvested to clear the road path one year (or more) prior to harvest of adjacent cut-blocks. The first method (ROAD) considers the total road right-of-way volume divided by the total road area cleared. The resulting volume per hectare is then multiplied by the total cut-block area scheduled for harvest during the following year to obtain the total estimated cutting volume. The second method (STRATIFIED) also involves scaling of trees cleared from the road. However, in STRATIFIED, log scaling data are stratified by forest stand location. A volume per hectare is calculated for each stretch of road that crosses a single forest stand. This volume per hectare is then multiplied by the remaining area of the same forest stand scheduled for harvest one year later. The sum of all resulting estimated volumes per stand gives the total estimated cutting-volume for all cut-blocks adjacent to the studied road. A third method (MNR) was also used to estimate cut-volumes of the sites studied. This method represents the actual existing technique for estimating cutting volume in the province of Quebec. It involves summing the cut volume for all forest stands. The cut volume is estimated by multiplying the area of each stand by its estimated volume per hectare obtained from standard stock tables provided by the governement. The resulting total estimated volume per cut-block for all three methods was then compared with the actual measured cut-block volume (MEASURED). This analysis revealed a significant difference between MEASURED and MNR methods with the MNR volume estimate being 30 % higher than MEASURED. However, no significant difference from MEASURED was observed for volume estimates for the ROAD and STRATIFIED methods which respectively had estimated cutting volumes 19 % and 5 % lower than MEASURED. Thus the ROAD and STRATIFIED methods are good ways to estimate cut-block volumes after road right-of-way harvest for conditions similar to those examined in this study.