Measuring the dynamic photosynthome

Background: Photosynthesis underpins plant productivity and yet is notoriously sensitive to small changes inenvironmental conditions, meaning that quantitation in nature across different time scales is not straightforward. The ‘dynamic’ changes in photosynthesis (i.e. the kinetics of the various reactions of photosynthesis in response to environmental shifts) are now known to be important in driving crop yield. Scope: It is known that photosynthesis does not respond in a timely manner, and even a small temporal “mismatch” between a change in the environment and the appropriate response of photosynthesis toward optimality can result in a fall in productivity. Yet the most commonly measured parameters are still made at steady state or a temporary steady state (including those for crop breeding purposes), meaning that new photosynthetic traits remain undiscovered. Conclusions: There is a great need to understand photosynthesis dynamics from a mechanistic and biological viewpoint especially when applied to the field of ‘phenomics’ which typically uses large genetically diverse populations of plants. Despite huge advances in measurement technology in recent years, it is still unclear whether we possess the capability of capturing and describing the physiologically relevant dynamic features of field photosynthesis in sufficient detail. Such traits are highly complex, hence we dub this the ‘photosynthome’. This review sets out the state of play and describes some approaches that could be made to address this challenge with reference to the relevant biological processes involved

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Sensor based pre-symptomatic detection of pests and pathogens for precision scheduling of crop protection products

Providing global food security requires a better understanding of how plants function and how their products, including important crops are influenced by environmental factors. Prominent biological factors influencing food security are pests and pathogens of plants and crops. Traditional pest control, however, has involved chemicals that are harmful to the environment and human health, leading to a focus on sustainability and prevention with regards to modern crop protection. A variety of physical and chemical analytical tools is available to study the structure and function of plants at the whole-plant, organ, tissue, cellular, and biochemical levels, while acting as sensors for decision making in the applied crop sciences. Vibrational spectroscopy, among them mid-infrared and Raman spectroscopy in biology, known as biospectroscopy are well-established label-free, nondestructive, and environmentally friendly analytical methods that generate a spectral “signature” of samples using mid-infrared radiation. The generated wavenumber spectrum containing hundreds of variables as unique as a biochemical “fingerprint”, and represents biomolecules (proteins, lipids, carbohydrates, nucleic acids) within biological samples. Spectral “biomarkers” generated by biospectroscopy is useful for the discrimination of distinct as well as closely related biomaterials, for various applications. Applications within the plant and crop sciences has been limited to date, especially for the investigation of dynamic biological processes in intact plant tissues. Even more scarce is the application of biospectroscopy to plant interactions with pests and pathogens. To adequately probe in vivo plant-environment interactions, surface structures of intact plant tissues such as leaves, and fruit need to be characterized. Infrared light energy can measure plant epidermal structures including the cuticle and cell wall for chemical profiling of different varieties and cultivars, as well as physiological applications such as plant health monitoring and disease detection. A review of the application of biospectroscopy to study plant and crop biology reveals the potential of biospectroscopy as a prominent technology for fundamental plant research and applied crop science. The application of biospectroscopy for in vivo plant analysis, to elucidate spectral alterations indicative of pest and pathogen effects, may therefore be highly beneficial to crop protection. Highlighting the in vivo analysis capability and portability of modern biospectroscopy, ATR-FTIR provided an invaluable tool for a thorough spectrochemical investigation of intact tomato fruit during development and ripening. This contributes novel spectral biomarkers, distinct for each development and ripening stage to indicate healthy development. Concurrently, this approach demonstrates the effectiveness of using spectral data for machine learning, indicated by classifier results, which may be applied to crop biology. Complementary to monitoring healthy growth and development of plants and crops, is the detection of threats to plant products that compromise yield or quality. This includes physical damage and accelerated decay caused by pests and pathogens. Biochemical changes detected by ATR-FTIR using principal component analysis and linear discriminant analysis (PCA–LDA), for damage-induced pathogen infection of cherry tomato (cv. Piccolo), showed subtle biochemical changes distinguishing healthy tomato from damaged, early or late sour rot-infected tomato. Sour rot fungus Geotrichum candidum was detected in vivo and characterized based on spectral features distinct from tomato fruit providing biochemical insight and detection potential for intact plant–pathogen systems. Pre-harvest detection of pests and pathogens in growing plants is paramount for crop protection and for effective use of crop protection products. Established previously as an exceptionally versatile bioanalytical sensor, for post-harvest applications, biospectroscopy was applied for the pre-harvest detection of microscopic pathogen Botrytis cinerea fungus infecting developing tomato plants. Compact MIR spectroscopy using ATR mode was adapted for the biochemical investigation of the plant-microbe interaction S. lycopersicum and B. cinerea, on the whole-plant level. Chemometric modeling including principal component analysis, and linear discriminant analysis were applied. Fingerprint spectra (1800-900 cm-1) were excellent discriminators of plant disease in pre-symptomatic as well as symptomatic plants. Spectral alterations in leaf tissue caused by infection are discussed. Potential for automatic decision-making is shown by high accuracy rates of 100% for detecting plant disease at various stages of progression. Similar accuracy rates using similar chemometric models are obtained for fruit development and ripening also. Overall, this research showcases the biospectroscopy potential for development monitoring and ripening of fruit crops, damage and infection induced decay of fruit in horticultural systems post-harvest, complemented by pre-harvest detection of microscopic pathogens. Based on the results from experiments performed under semi-controlled conditions, biospectroscopy is ready for field applications directed at pest and pathogen detection for improved crop production through the mitigation of crop loss