Indian Meal Moth Survivability in Stored Corn With Different Levels of Broken Kernels

Survivability of Indian meal moth, Plodia interpunctella (Hübner) (Lepi-doptera: Pyralidae), larvae fed a standard laboratory diet and whole corn with 0, 5 to 7, and 100% broken corn kernels, was assessed under laboratory conditions at 28o C, 65% relative humidity, and 14:10 h (L:D) photoperiod. A conventional yellow dent corn hybrid (about 3.9% oil content, dry basis) and a high-oil corn hybrid (about 7.7% oil content, dry basis) were tested. Survivability was measured as the percentage of pre-pupae, pupae, and adults observed at the end of the rearing period. For the standard laboratory diet, a mean of 97.5% larvae survived. Percentage of larval survival increased as the percentage of broken corn increased. Mean percentages of larval survival for the conventional yellow dent corn were 6.7, 63.8, and 80.0 for 0, 7, and 100% broken kernels, respectively. The mean percentages of larval survival for the high-oil corn hybrid were 28.3, 81.3, and 100.0 for 0, 5, and 100% broken kernels, respectively. Larval growth rate for high-oil corn was faster than for conventional corn. Results indicate that cleaning corn before storage could reduce P. interpunctella problems

Selecting Fans and Determining Airflow for Grain Drying and Storage

Using fans to force air having the proper temperature and relative humidity through a crop is a valuable technique for maintaining quality after harvest. The air helps maintain the moisture and temperature of a crop at levels that prevent growth of harmful fungi and insects

End-to-End Learning on Multimodal Knowledge Graphs

Knowledge graphs enable data scientists to learn end-to-end on heterogeneous knowledge. However, most end-to-end models solely learn from the relational information encoded in graphs' structure: raw values, encoded as literal nodes, are either omitted completely or treated as regular nodes without consideration for their values. In either case we lose potentially relevant information which could have otherwise been exploited by our learning methods. We propose a multimodal message passing network which not only learns end-to-end from the structure of graphs, but also from their possibly divers set of multimodal node features. Our model uses dedicated (neural) encoders to naturally learn embeddings for node features belonging to five different types of modalities, including numbers, texts, dates, images and geometries, which are projected into a joint representation space together with their relational information. We implement and demonstrate our model on node classification and link prediction for artificial and real-worlds datasets, and evaluate the effect that each modality has on the overall performance in an inverse ablation study. Our results indicate that end-to-end multimodal learning from any arbitrary knowledge graph is indeed possible, and that including multimodal information can significantly affect performance, but that much depends on the characteristics of the data.Comment: Under submission. arXiv admin note: substantial text overlap with arXiv:2003.1238

Selecting fans and determining airflow for crop drying, cooling, and storage

1 online resource (PDF, 8 pages)This archival publication may not reflect current scientific knowledge or recommendations. Current information available from the University of Minnesota Extension: https://www.extension.umn.edu

Simulating preferential soil water flow and tracer transport using the Lagrangian Soil Water and Solute Transport Model

We propose an alternative model concept to represent rainfall-driven soil water dynamics and especially preferential water flow and solute transport in the vadose zone. Our LAST-Model (Lagrangian Soil Water and Solute Transport) is based on a Lagrangian perspective of the movement of water particles (Zehe and Jackisch, 2016) carrying a solute mass through the subsurface which is separated into a soil matrix domain and a preferential flow domain. The preferential flow domain relies on observable field data like the average number of macropores of a given diameter, their hydraulic properties and their vertical length distribution. These data may be derived either from field observations or by inverse modelling using tracer data. Parameterization of the soil matrix domain requires soil hydraulic functions which determine the parameters of the water particle movement and particularly the distribution of flow velocities in different pore sizes. Infiltration into the matrix and the macropores depends on their respective moisture state, and subsequently macropores are gradually filled. Macropores and matrix interact through diffusive mixing of water and solutes between the two flow domains, which again depends on their water content and matric potential at the considered depths. The LAST-Model is evaluated using tracer profiles and macropore data obtained at four different study sites in the Weiherbach catchment in southern Germany and additionally compared against simulations using HYDRUS 1-D as a benchmark model. While both models show qual performance at two matrix-flow-dominated sites, simulations with LAST are in better accordance with the fingerprints of preferential flow at the two other sites compared to HYDRUS 1-D. These findings generally corroborate the feasibility of the model concept and particularly the implemented representation of macropore flow and macropore–matrix exchange. We thus conclude that the LAST-Model approach provides a useful and alternative framework for (a) simulating rainfall-driven soil water and solute dynamics and fingerprints of preferential flow as well as (b) linking model approaches and field experiments. We also suggest that the Lagrangian perspective offers promising opportunities to quantify water ages and to evaluate travel and residence times of water and solutes by a simple age tagging of particles entering and leaving the model domain

Organizing research data

Research relies on ever larger amounts of data from experiments, automated production equipment, questionnaries, times series such as weather records, and so on. A major task in science is to combine, process and analyse such data to obtain evidence of patterns and correlations