This study integrates Discrete Element Method (DEM) simulations, soil bin experiments, and multi-objective optimization to develop an energy-efficient manure injector shank. Eighteen geometries were first screened using DEM, reducing the set to six designs (S_1–S_6) based on draft force–rupture area performance. The selected designs, varying in rake angle (30°, 45°, 60°), thickness (25 and 30 mm), and width (102, 110, and 118 mm), were tested in a soil bin to measure draft, trench width, spoil cross-sectional area, and soil rupture. Statistical analysis revealed significant differences among designs (p < 0.05), confirming that rake angle, width, and thickness have a strong influence on the soil–tool interaction. A multi-objective optimization framework was then used to minimize draft, trench width, and spoil area while maximizing rupture, with performance quantified through overall desirability values (0–1). Shank S_3 (45° rake, 25 mm thickness, 110 mm width) achieved the highest desirability (0.6676), representing the best trade-off between energy efficiency, minimal surface disturbance, and effective subsurface loosening. This integrated DEM–experimental–optimization approach demonstrates a reliable, data-driven workflow for implement design, reducing reliance on extensive field trials. However, future studies should validate the performance of S_3 and other candidate designs under diverse soil types, moisture levels, and operating conditions to confirm their agronomic and environmental benefits.

Whole-plant corn has been previously investigated as a biomass feedstock. Current approaches are analogous to harvesting whole-plant corn for livestock feed or biogas production. They include utilizing a self-propelled forage harvester to harvest the plant as a bulk material and storing it anaerobically. This process leads to grain damage, reducing the marketability of the grain after fractionation. This work investigated a process that included harvesting and anaerobically storing whole-ear corn with corn stover as an alternative. Over two harvest seasons, dry matter losses, moisture content changes, and grain damage were assessed after anaerobic storage. Less than 3% grain damage was observed across all treatments. Stover moisture decreased by 3% to 7% wet basis. Depending on the harvest year (p < 0.001), grain moisture content increased by 7 to 10 percentage points wet basis (p = 0.012). Cob moisture increased by about four percentage points wet basis regardless of harvest year (p = 0.49). Dry matter losses for the overall treatment were less than 3% across both harvest seasons, but high variability was observed when reviewing the losses in the ear and stover fractions. Based on this work, whole ear storage should be considered where subsequent grain fractionation and the marketability of the grain fraction are a concern.

Whole-plant maize (corn) (WPC) is a critical forage in ruminant diets, and rapid, reliable measurement of its nutritional composition is essential for precision feeding. We hypothesized that an on-site near-infrared spectroscopy (OS-NIRS—specifically, HarvestLab™ 3000) sensor would provide within-laboratory repeatability comparable to commercial analytical laboratories (ALs) and inter-laboratory reproducibility similar to conventional laboratory analyses. To test this, WPC samples were collected across three experiments and two countries (USA and Germany) and analyzed by both OS-NIRS and ALs, with precision metrics calculated according to ISO 5725. Results showed that OS-NIRS achieved intra-laboratory repeatability equal to or greater than ALs, particularly for protein and starch. The repeatability performance of the OS-NIRS sensors was similar to that of ALs for moisture and NDF. Inter-laboratory reproducibility varied widely across constituents and experiments. Including OS-NIRS data with AL measurements produced inconsistent effects—sometimes narrowing confidence intervals but more often widening them—while OS-NIRS data alone demonstrated repeatability on par with ALs but mixed reproducibility outcomes. Inclusion of OS-NIRS data did not introduce systematic bias and, in some cases, improved consistency. These findings indicate that OS-NIRS can complement laboratory analyses by providing timely, farm-level measurements that enhance decision-making in feed management.