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Lentil (Lens culinaris Medik.) production has increased exponentially in Montana in the last two decades. However, there are important gaps in knowledge on best management practices for lentil. Agronomic recommendations are based on performance of old cultivars outside of the area for seeding rate, and on anecdotal evidence for proper roll timing, particularly since the widespread adoption of no‐till farming. Replicated field experiments were conducted at three sites during the 2019, 2020, and 2021 growing seasons in Montana to determine the impacts of roll timing and seeding rate on lentil yield and identify best practices. Overall, rolling at emergence and at the 10‐leaf stage decreased yields by 5% and 8%, respectively, but rolling just after planting or at the early vegetative stage (two‐ to four‐leaf stage) did not decrease yields. Higher yields were achieved at higher seeding rates, with yields increasing between 6 and 52 lb ac−1 for each additional plant established per square foot, but emergence rates were variable and relatively low, so a higher seeding rate may be necessary to achieve plant densities above 12 plants ft−2 in this region. In five out of nine site years, the largest partial economic returns were achieved with 22.5 or 30 live seeds ft−2 seeding rate, corresponding to achieved plant densities of 12 to 16 plants ft−2. It was generally economical to increase seeding rate from 15 to 22.5 live seeds ft−2, thus increasing average achieved plant density from 8 to 13 plants ft−2, except when seed costs were high (>$0.45 lb−1) combined with low market prices (< $0.20 lb−1). Plain Language Summary: Lentil production has increased exponentially in Montana in the last two decades. However, there are important gaps in knowledge on best management practices for lentil regarding the correct timing for rolling lentil and what seeding rates to use. We conducted an experiment at three sites in Montana during the 2019, 2020, and 2021 growing seasons and found that rolling immediately after planting and at the —two‐to four‐leaf stage was safe, but rolling at emergence or later in the season (10‐leaf stage) led to reductions in yields. In addition, we found it was generally economical to increase seeding rates up to 22.5 live seeds ft−2 to ensure a target plant population density of 12 to 16 plants ft−2, as emergence rates in semi‐arid areas can be low and variable. [ABSTRACT FROM AUTHOR]

Abstract: We used an object-oriented approach in conjunction with the Random Forest algorithm to classify agricultural practices, including tillage (till or no-till (NT)), crop intensity, and grassland-based conservation reserve (CR). The object-oriented approach allowed for per-field classifications and the incorporation of contextual elements in addition to spectral features. Random Forest is a classification tree-based advanced classifier that avoids data over-fitting associated with many tree-based models and incorporates an unbiased internal classification accuracy assessment. Landsat satellite imagery was chosen for its continuous coverage, cost effectiveness, and image accessibility. Classification results for 2007 included producer''s accuracies of 91% for NT and 31% for tillage when applying Random Forest to image objects generated from a May Landsat image. Low classification accuracies likely were attributed to the misclassification of conservation-based tillage practices as NT. Results showed that the binary separation of tillage from NT management is likely not appropriate due to surface spectral and textural similarities between NT and conservation-type tillage practices. Crop and CR lands resulted in producer''s accuracies of 100% and 90%, respectively. Crop and fallow producer''s accuracies were 95% and 82% in the 2007 classification, despite post-senesced vegetation; misclassification within the fallow class was attributed to pixel-mixing problems in areas of narrow (<100 m) strip management. A between-date normalized difference vegetation index approach was successfully used to detect areas having “changed” in vegetation status between the 2007 and prior image dates; classified “changed” objects were then merged with “unchanged” objects to produce crop status maps. Field crop intensity was then determined from the multi-year analysis of generated crop status maps. [Copyright &y& Elsevier]