Pea-barley intercrop N dynamics in farmers fields

Knowledge about crop performances in farmers’ fields provides a link between on-farm practice and re-search. Thereby scientists may improve their ability to understand and suggest solutions for the problems facing those who have the responsibility of making sound agricultural decisions. Nitrogen (N) availability is known to be highly heterogeneous in terrestrial plant communities (Stevenson and van Kessel, 1997), a heterogeneity that in natural systems is often associated with variation in the distri-bution of plant species. In intercropping systems the relative proportion of component crops is influenced by the distribution of growth factors such as N in both time and space (Jensen, 1996). In pea-barley intercrops, an increase in the N supply promotes the growth of barley thereby decreasing the N accumulation of pea and giving rise to changes in the relative proportions of the intercropped components (Jensen, 1996). The pres-sure of weeds may, however, significantly change the dynamics in intercrops (Hauggaard-Nielsen et al., 2001). Data from farmers’ fields may provide direct, spatially explicit information for evaluating the poten-tials of improving the utilisation of field variability by intercrops

The Impact of Carbohydrate and Protein Level and Sources on Swine Manure Foaming Properties

This study explored the impact of swine diet on the composition, methane production potential, and foaming properties of manure. Samples of swine manure were collected from controlled feeding trials with diets varying in protein and carbohydrate levels and sources. Protein sources consisted of corn with amino acids, corn-soybean meal with amino acids, corn-soybean meal, corn-canola meal, corn-corn gluten meal, and corn-poultry meal. Carbohydrate sources consisted of corn-soybean meal, barley, beet pulp, distillers dried grains with solubles (DDGS), soy hulls, and wheat bran. Manure samples were tested for a number of physical and biochemical parameters, including total solids, volatile solids, viscosity, density, methane production rate, biochemical methane potential, foaming capacity, and foam stability. Statistical analyses were performed to evaluate whether different carbohydrate and/or protein ingredients affected these physico-chemical properties or the samples’ ability to produce methane gas. After conducting these trials, another feeding trial was performed to evaluate if the addition of Narasin into rations (corn-soybean and DDGS) could reduce the methane production rate or potential of the manure. These samples were also tested for the physical and biochemical parameters mentioned previously. Finally, an additional manure foaming study was conducted involving the addition of specific carbohydrates ground to different particle sizes and corn oil to observe the effects that the additives had on foaming capacity and stability

Three-Phase Foam Analysis and the Development of a Lab-Scale Foaming Capacity and Stability Test for Swine Manures

Foam accumulation on the manure slurry at deep pit swine facilities has been linked to flash fire incidents, making it a serious safety concern for pork producers. In order to investigate this phenomenon, samples of swine manure were collected from over 50 swine production facilities in Iowa with varying levels of foam accumulation over a seven month period. These samples were tested for a number of physical and chemical parameters including temperature, pH, total solids, volatile solids, volatile fatty acid concentration, biochemical methane potential, and methane production rate. After establishing these parameters, a foaming capacity and stability test was performed where samples were placed in clear PVC tubes with air diffusers at the bottom to simulate biogas production. The amount of foam produced at a set aeration rate was recorded as a measure of foaming capacity, and foam stability was assessed by measuring the height of foam remaining at certain time intervals after aeration had ceased. The results of this test indicated that samples collected from foaming barns showed a greater capacity to produce and stabilize foam. In addition, statistical analysis indicated that manures with foam produced methane at significantly greater rates than non-foaming manures (0.154 ± 0.010 and 0.052 ± 0.003 L CH4./L slurry*day respectively, average standard error), and consequently had significantly greater fluxes of methane moving through the manure volume. On the other hand, the biochemical methane production assay suggested that manure from foaming pits had less potential to generate methane (112 ± 9 mL CH4/g VS) than non-foaming pits (129 ± 9 mL CH4/g VS), and the VFA analysis showed significantly lower concentrations in foaming pits (4472, 3486, and 1439 μg/g for the surface level and descending depths of the pit, respectively) as compared to non-foaming pits (9385,8931, and 6938 μg/g for the same sample depths). When taken together, these assays suggest enhanced anaerobic digestion efficiency from foaming barns, as well as the possible accumulation of a surfactant at the manure-air interface of foaming deep pits. Overall, this work supports a three-phase system conceptualization of foam production in swine manure deep pits, and that the control of one or more of these phases will be required for mitigation

How Precisely Can Easily Accessible Variables Predict Achilles and Patellar Tendon Forces during Running?

Patellar and Achilles tendinopathy commonly affect runners. Developing algorithms to predict cumulative force in these structures may help prevent these injuries. Importantly, such algorithms should be fueled with data that are easily accessible while completing a running session outside a biomechanical laboratory. Therefore, the main objective of this study was to investigate whether algorithms can be developed for predicting patellar and Achilles tendon force and impulse during running using measures that can be easily collected by runners using commercially available devices. A secondary objective was to evaluate the predictive performance of the algorithms against the commonly used running distance. Trials of 24 recreational runners were collected with an Xsens suit and a Garmin Forerunner 735XT at three different intended running speeds. Data were analyzed using a mixed-effects multiple regression model, which was used to model the association between the estimated forces in anatomical structures and the training load variables during the fixed running speeds. This provides twelve algorithms for predicting patellar or Achilles tendon peak force and impulse per stride. The algorithms developed in the current study were always superior to the running distance algorithm