Modelling, simulations and monitoring for sustainable plant and animal production systems through precision technique

Protected crop production is a modern and innovative approach to cultivating plants in a controlled environment to optimize growth, yield, and quality. This method involves using structures such as greenhouses or tunnels to create a sheltered environment. These productive solutions are characterized by a careful regulation of variables like temperature, humidity, light, and ventilation, which collectively contribute to creating an optimal microclimate for plant growth. Heating, cooling, and ventilation systems are used to maintain optimal conditions for plant growth, regardless of external weather fluctuations. Protected crop production plays a crucial role in addressing challenges posed by climate variability, population growth, and food security. Similarly, animal husbandry involves providing adequate nutrition, housing, medical care and environmental conditions to ensure animal welfare. Then, sustainability is a critical consideration in all forms of agriculture, including protected crop and animal production. Sustainability in animal production refers to the practice of producing animal products in a way that minimizes negative impacts on the environment, promotes animal welfare, and ensures the long-term viability of the industry. Then, the research activities performed during the PhD can be inserted exactly in the field of Precision Agriculture and Livestock farming. Here the focus is on the computational fluid dynamic (CFD) approach and environmental assessment applied to improve yield, resource efficiency, environmental sustainability, and cost savings. It represents a significant shift from traditional farming methods to a more technology-driven, data-driven, and environmentally conscious approach to crop and animal production. On one side, CFD is powerful and precise techniques of computer modeling and simulation of airflows and thermo-hygrometric parameters, that has been applied to optimize the growth environment of crops and the efficiency of ventilation in pig barns. On the other side, the sustainability aspect has been investigated and researched in terms of Life Cycle Assessment analyses.

A rapid single-step multiplex method for discriminating between Trichogramma (Hymenoptera: Trichogrammatidae) species in Australia

Inaccurate species identification confounds insect ecological studies. Examining aspects of Trichogramma ecology pertinent to the novel insect resistance management strategy for future transgenic cotton, Gossypium hirsutum L., production in the Ord River Irrigation Area (ORIA) of Western Australia required accurate differentiation between morphologically similar Trichogramma species. Established molecular diagnostic methods for Trichogramma identification use species-specific sequence difference in the internal transcribed spacer (ITS)-2 chromosomal region; yet, difficulties arise discerning polymerase chain reaction (PCR) fragments of similar base pair length by gel electrophoresis. This necessitates the restriction enzyme digestion of PCR-amplified ITS-2 fragments to readily differentiate Trichogramma australicum Girault and Trichogramma pretiosum Riley. To overcome the time and expense associated with a two-step diagnostic procedure, we developed a “one-step” multiplex PCR technique using species-specific primers designed to the ITS-2 region. This approach allowed for a high-throughput analysis of samples as part of ongoing ecological studies examining Trichogramma biological control potential in the ORIA where these two species occur in sympatry.

Pacific damsel bug: Friend of foe?

Determining the diet of insects present in cotton should be among the first steps in any applied research program. Omnivorous species are particularly important as they have the potential to suppress pest populations through prey feeding, but also to cause economic damage to the crop through plant feeding.

A simple regional-scale model for forecasting sorghum yield across North-Eastern Australia

Sorghum is the main dryland summer crop in NE Australia and a number of agricultural businesses would benefit from an ability to forecast production likelihood at regional scale. In this study we sought to develop a simple agro-climatic modelling approach for predicting shire (statistical local area) sorghum yield. Actual shire yield data, available for the period 1983-1997 from the Australian Bureau of Statistics, were used to train the model. Shire yield was related to a water stress index (SI) that was derived from the agro-climatic model. The model involved a simple fallow and crop water balance that was driven by climate data available at recording stations within each shire. Parameters defining the soil water holding capacity, maximum number of sowings (MXNS) in any year, planting rainfall requirement, and critical period for stress during the crop cycle were optimised as part of the model fitting procedure. Cross-validated correlations (CVR) ranged from 0.5 to 0.9 at shire scale. When aggregated to regional and national scales, 78-84% of the annual variation in sorghum yield was explained. The model was used to examine trends in sorghum productivity and the approach to using it in an operational forecasting system was outlined. (c) 2005 Elsevier B.V. All rights reserved.