Individual seed electrolyte leakage tests and evaluation of soaking injury using maize

1993 Spring.Covers not scanned.Includes bibliographical references.Determination of seed viability has traditionally involved germination, which is obviously destructive to the seed and also labor intensive. Both are far from being cost effective. The development of non-destructive or at least less injurious methods of testing seed quality i.e. viability and vigor determination using electroconductivity measurements of single seed leachate solutions could effectively replace the standard germination test. The objectives were to compare five indices of seed quality, all of which are based on individual seed leachate conductivity tests. Additionally, if the soak period is brief enough, there should be little injury to the seeds allowing for successive soaks on the same sample. Seeds of Zea mays L. were aged using two methods to obtain varying levels of viability and vigor for comparisons. The first method, modified controlled deterioration, involved placing two samples of seeds in separate desiccators over a saturated NaCl solution for 16 and 20 days for seed lots 88-2i and 88-1d, respectively. The seeds were sealed in aluminum foil packets, 110 per packet, and aged for 120 hours at 45°C. The second method involved placing two samples of seeds in separate desiccators over H2SO4 at 35°C for 238 and 484 days, respectively for desiccators one and two. After aging, seeds from each desiccator were kept in aluminum foil packets. Electroconductivity testing was done on samples of 100 seeds from each of the aging methods. The seed samples were soaked for six hours during which time 29 scans of data were obtained. The samples were dried at room temperature for seven days after which time they were germinated using the rolled paper towel method. An additional 100 unsoaked (control) seeds were germinated at the same time. Radicle lengths were measured at the end of 72 hours and final germination was counted at the end of seven days. Relative vigor was calculated as a ratio of the soaked seed radicle lengths divided by the unsoaked control seed radicle lengths. Electroconductivity data were concatenated and made compatible with the UNIX format. Five indices were derived from the data for determination of their ability to predict maize seed quality. Internal Slope (IS) and mean and median μAmps after five hours of imbibition were derived from a Richards function program, the Initial Leach Rate (ILR) was derived from the rectangular hyperbola and the Average Absolute Leach Rate (AALR) was derived from another Richards function program. The second aging method did not produce the desired range of seed quality and so the results discussed are based on the first aging technique. Internal Slope was the best predictor of seed viability, r2 = 0.91, followed very closely by the median μAmp value, r2 = 0.87, and the mean μAmp value, r2 = 0.81. The ILR and AALR indices did not predict seed quality with r2 values of 0.01 and 0.03, respectively. Relative vigor was not estimated as well as viability, probably due to the artificial aging. A second experiment was designed to study the effect of five successive soak cycles (C) and five cycle durations (CD) of 2, 4, 6, 7 and 8 hours on viability and vigor loss response. All subsets regression plus consideration between bias and random error led to the choice of the following two best subset models: YVIA = 99.14 - 0.0609 (CD*C2), R2 = 0.62, and YRV = 0.99 + 0.0229 (CD) - 0.0101 (CD*C), R2 = 0.52. Response surfaces were generated which suggested that 4 C of 5 hours each resulted in only an 8% loss of viability but a 20% loss of relative vigor. Conductivity measurements taken at the end of each CD for each C showed that 45% of the readily leachable electrolytes leached during the first soak period. Furthermore, a priming effect, invigoration, was observed when the seeds were soaked for a total of ten hours, taking into consideration both the number of cycles and the duration of each cycle

Multivariate NIR studies of seed-water interaction in Scots Pine Seeds (Pinus sylvestris L.)

This thesis describes seed-water interaction using near infrared (NIR) spectroscopy, multivariate regression models and Scots pine seeds. The presented research covers classification of seed viability, prediction of seed moisture content, selection of NIR wavelengths and interpretation of seed-water interaction modelled and analysed by principal component analysis, ordinary least squares (OLS), partial least squares (PLS), bi-orthogonal least squares (BPLS) and genetic algorithms. The potential of using multivariate NIR calibration models for seed classification was demonstrated using filled viable and non-viable seeds that could be separated with an accuracy of 98-99%. It was also shown that multivariate NIR calibration models gave low errors (0.7% and 1.9%) in prediction of seed moisture content for bulk seed and single seeds, respectively, using either NIR reflectance or transmittance spectroscopy. Genetic algorithms selected three to eight wavelength bands in the NIR region and these narrow bands gave about the same prediction of seed moisture content (0.6% and 1.7%) as using the whole NIR interval in the PLS regression models. The selected regions were simulated as NIR filters in OLS regression resulting in predictions of the same quality (0.7 % and 2.1%). This finding opens possibilities to apply NIR sensors in fast and simple spectrometers for the determination of seed moisture content. Near infrared (NIR) radiation interacts with overtones of vibrating bonds in polar molecules. The resulting spectra contain chemical and physical information. This offers good possibilities to measure seed-water interactions, but also to interpret processes within seeds. It is shown that seed-water interaction involves both transitions and changes mainly in covalent bonds of O-H, C-H, C=O and N-H emanating from ongoing physiological processes like seed respiration and protein metabolism. I propose that BPLS analysis that has orthonormal loadings and orthogonal scores giving the same predictions as using conventional PLS regression, should be used as a standard to harmonise the interpretation of NIR spectra

Modeling seed germination and seedling emergence in winterfat (krascheninnikovia lanata (pursh) A.D.J. Meeuse & Smit) : physiological mechanisms and ecological relevance

Winterfat (Krascheninnikovia lanata) a native shrub has superior forage quality for livestock and wildlife, and is important in the structure and the function of the Northern Mixed Prairie of North America. Seedbeds in the Northern Mixed Prairie are characterized by high fluctuations in temperature and soil water, especially at the soil surface during the spring under unpredictable weather conditions. High seedling mortality is a major limitation for establishing winterfat from direct seeding. Objectives of this study were to: 1) quantify germination responses to temperature and water potential; 2) predict seed germination and seedling emergence using constructed threshold models; and 3) investigate physiological mechanisms and the ecological relevance of model parameters. The constructed thermal and hydrothermal time models predicted germination time in most controlled temperature and water potential regimes with the modification of model assumptions in winterfat. For the first time, it was proved that winterfat seeds have a subzero base temperatures (Tb) for germination, achieving 43 to 67% germination at – 3oC. The estimated Tb was lower in the large seeds (-4.5oC) than in the small seeds (-3.5oC) and the difference between seed collections was also about 1oC. Lower Tb favors large seeds to accumulate more thermal time at a given temperature, especially in early spring or fall when temperatures are low. Basic assumptions of hydrothermal time model, such as the constancy of model parameters, are invalid in winterfat. Model parameters varied with water potential, temperature and seed size within a seed collection. The predictability of constructed models is acceptable for seedling emergence only at optimal conditions in the field. Adverse seedbed conditions such as high soil temperatures (> 15oC) and limited soil water (< -0.5 MPa) reduced predictability of seedling emergence with the hydrothermal time model. Pre- and post-germination events that affect seed deterioration, seedling mortality and seedling elongation may reduce the predictability of the hydrothermal time model. Small seeds required approximately twice as long as large seeds to reach 50% germination at -1 to -3oC. Greater cold tolerance in large seeds was correlated with greater membrane integrity, less cold imbibition damage, higher contents of soluble cryoprotective sugars, such as glucose, raffinose and sucrose during germination at low temperature. These sugars prevent from dysfunctions of cell membrane and enzymes at freezing temperatures

A simple, fast and accurate screening method to estimate maize (Zea mays L) tolerance to drought at early stages

There is a great need for the selection of plants with higher drought tolerance, so that fast and effective techniques to identify variations in drought tolerance are mandatory for screening large numbers of genotypes. This work presents a protocol for easy and reliable assessment of responses of maize genotypes to water stress conditions imposed during early stages of development. Three experiments using 11 commercial maize hybrids under four levels of water stress were carried out: i) germination, ii) seedling growth, and iii) early growth bioas- says. Constant and uniform water stress was imposed using solutions of polyethylene glycol 6000 (PEG 6000). Plant material was evaluated for several morphological, physiological and biochemical traits and monitored for photosynthetic efficiency. Principal component analysis (PCA) of these joint experiments revealed that germination percentage, early root development and stomatal conductance were the most useful traits for discriminating maize hybrids according to their tolerance to water stress. A subsequent greenhouse assay performed with two hybrids with contrasting responses under soil drying conditions validated the previous results. According to our results, the key of drought tolerance was a rapid response of stomatal conductance, which allowed a longer survival to stress even under severe desiccation. This work provides the researcher with a simple and reliable screening method that could be implemented as a decision support tool in the selection of the most suitable genotypes for cultivation in areas where water availability is a problem, as well as for the selection of tolerant genotypes to early drought in breeding programs

Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands

Croplands are man-made ecosystems that have high net primary productivity during the growing season of crops, thus impacting carbon and other exchanges with the atmosphere. These exchanges play a major role in nutrient cycling and climate change related issues. An accurate representation of crop phenology and physiology is important in land-atmosphere carbon models being used to predict these exchanges. To better estimate time-varying exchanges of carbon, water, and energy of croplands using the Simple Biosphere (SiB) model, we developed crop-specific phenology models and coupled them to SiB. The coupled SiB-phenology model (SiBcrop) replaces remotely-sensed NDVI information, on which SiB originally relied for deriving Leaf Area Index (LAI) and the fraction of Photosynthetically Active Radiation (fPAR) for estimating carbon dynamics. The use of the new phenology scheme within SiB substantially improved the prediction of LAI and carbon fluxes for maize, soybean, and wheat crops, as compared with the observed data at several AmeriFlux eddy covariance flux tower sites in the US mid continent region. SiBcrop better predicted the onset and end of the growing season, harvest, interannual variability associated with crop rotation, day time carbon uptake (especially for maize) and day to day variability in carbon exchange. Biomass predicted by SiBcrop had good agreement with the observed biomass at field sites. In the future, we will predict fine resolution regional scale carbon and other exchanges by coupling SiBcrop with RAMS (the Regional Atmospheric Modeling System)

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

Post-harvest strategies for the prevention of fungal growth and mycotoxin production in corn

The overall objective of the research was to assess preservation techniques for corn including methods based on the addition of bicarbonates and phenolic compounds and the application of modified atmospheres. In light of the emergence of ‘Predictive Mycology’, the study also had the goal of developing validated predictive models to describe the growth of the most important fungal contaminators in corn and to assess their ability to model the trends observed in the preservation studies. The research started by fully characterizing the water relations of the growth substrate — yellow dent corn — via the development and modelling of its sorption isotherms. The study then focused on the development of validated predictive models to describe the growth of Aspergillus and Fusarium isolates of greatest importance to corn. Variability of growth by assessing the lag phase durations and colony growth rates of single spores of A. flavus and F. verticillioides was then evaluated. The relationship between fumonisin production and growth of F. verticillioides and F. proliferatum was then investigated. The research then focused on the evaluation of various preservation techniques. The use of modified atmospheres initially containing high levels of CO2 was found to have a large inhibitory effect on growth and fumonisin B1 production by the Fusarium isolates. Models were developed that adequately described the interaction observed between the CO2 and water activity on the growth of the Fusarium isolates. Although the initial headspace O2 level did not affect the rate of growth of the fungi, it had a large impact on fumonisin B1 production, with a shift in the optimum headspace level to 10% from 21% (air) being noted when the water activity of the corn was lowered. Comparing the application of bicarbonate salts and phenolic compounds, bicarbonate salts (in particular ammonium bicarbonate) proved to have the greatest potential for application. Ammonium bicarbonate was the only chemical tested that could completely inhibit both growth and mycotoxin production (fumonisin B1 and aflatoxin B1) at levels that were still sensorially acceptable. The phenolic compounds only significantly reduced aflatoxin B1 and fumonisin B1 production but did not affect growth

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

Inline disinfestation of canola seeds from red flour beetles using a 50-ohm RF technology

Disinfestation of insect pests in stored grains is a crucial unit operation to save the quality of the grains during the storage. Several methods of disinfestation are available including chemical and non-chemical methods. However, the use of the chemical method is avoided because of its adverse effects on the environment and studies show that chemical methods have failed frequently in recent years. So, this research focus on investigation of the usage of radio waves, which is a non-chemical method to disinfest insects in stored grains. A pilot-scale 50-ohm radio frequency (RF) heating system was used to disinfest adult red flour beetles (Tribolium castaneum) in bulk canola seeds (Brassica napus L.) of 9 % moisture content (MC) in a tubular applicator with parallel electrodes. The heating characteristics of the bulk canola seeds was studied using the 50-ohm RF system and non-uniformity of the temperature distribution of bulk canola was observed. The hottest spot was observed at the front side of the tubular cavity of the applicator adjacent to the hot electrode. The RF heating rate depends on the distribution of the electromagnetic (EM) field, geometry, and position of the sample in the RF applicator, thermal, physical, and electrical properties of the sample. The average temperature (Tavg) and uniformity index (θ) of the bulk canola during RF heating were also observed. The thermal mortalities of adult red flour beetles infesting canola seeds at 9% moisture content (MC) were determined treated using a 50-ohm radio frequency (RF) heating system. The infested seeds were treated between 297 K and 338 K at RF heating power of 3 kW, 5 kW, and 7 kW. The survival rate of the adult T. castaneum infesting the canola seeds at 9% MC decreased with an increase in temperature (297 K to 338 K) and increase in RF power levels (3 kW to 7 kW). Desirable selective heating effect on mortality was more predominant at higher RF powers. An inverse simulation was used to estimate kinetic parameters of the thermal death of the adult T. castaneum. 4th order Runge-Kutta method was used to solve the ordinary differential equation (ODE) based kinetic model which has an Arrhenius temperature-dependent reaction rate constant. The thermal death kinetics of the adult T. castaneum followed first order reaction with an activation energy of 97.50 kJ/mol. Satisfactory agreements were observed between the mortalities predicted using the kinetic model and the experiments. Also, the physicochemical properties of canola seeds were affected by the RF heating at various end temperatures and power levels although the changes were not very significant and were in an acceptable range. Thus, the research was a successful in disinfesting adult red flour beetles in bulk canola seeds of 9% MC using a pilot-scale 50-ohm RF heating system with a tubular applicator with parallel electrodes