Explore the vast range of titles available.

Weed species that escape control (hereafter called residual weeds) coupled with changing weather patterns are emerging challenges for snap bean processors and growers. Field surveys were conducted to identify associations among crop/weed management practices and environmental factors on snap bean yield and residual weed density. From 2019-2023, a total of 358 snap bean production fields throughout the major U.S. production regions (Northwest, Midwest and Northeast) were surveyed for residual weeds. Field-level information on crop/weed management, soils, and weather also were obtained. To determine associations among management and environmental variables on crop yield and residual weed density, the machine learning algorithm random forest was utilized. The models had 24 and 22 predictor variables for crop yield and residual weed density, respectively, and both were trained on 80% of the data with the remainder used as a test set to determine model accuracy. Both models had pseudo-R.sup.2 values of over 0.50 and accuracy over 80%. The models showed that crop yield was higher in the Northwest compared to the Midwest region, while higher average temperatures during early season growth and planting midseason (June-July) predicted greater crop yield compared to other time periods. The use of row cultivation was associated with lower snap bean yield and weed density, suggesting row cultivation had less-than-ideal selectivity between the crop and weed. Moreover, multiple spring tillage operations prior to planting were linked with an increase in weed density, implying that excessive tillage may favor the emergence of residual weeds in snap bean. Over the coming decades, climate change-driven weather variability is likely to influence snap bean production, both directly through crop growth and indirectly through weeds that escape control practices that also are influenced by the weather.

Understanding factors that influence a species' distribution and abundance across the annual cycle is required for range-wide conservation. Thousands of imperiled red knots (Calidris cantus rufa) stop on Virginia's barrier islands each year to replenish fat during spring migration. We investigated the variation in red knot presence and flock size, the effects of prey on this variation, and factors influencing prey abundance on Virginia's barrier islands. We counted red knots and collected potential prey samples at randomly selected sites from 2007-2018 during a two-week period during early and peak migration. Core samples contained crustaceans (Orders Amphipoda and Calanoida), blue mussels (Mytilus edulis), coquina clams (Donax variabilis), and miscellaneous prey (horseshoe crab eggs (Limulus polyphemus), angel wing clams (Cyrtopleura costata), and other organisms (e.g., insect larvae, snails, worms)). Estimated red knot peak counts in Virginia during 21-27 May were highest in 2012 (11,959) and lowest in 2014 (2,857; 12-year peak migration x[MACRON] = 7,175, SD = 2,869). Red knot and prey numbers varied across sampling periods and substrates (i.e., peat and sand). Red knots generally used sites with more prey. Miscellaneous prey (x[MACRON] = 2401.00/m.sup.2, SE = 169.16) influenced red knot presence at a site early in migration, when we only sampled on peat banks. Coquina clams (x[MACRON] = 1383.54/m.sup.2, SE = 125.32) and blue mussels (x[MACRON] = 777.91/m.sup.2, SE = 259.31) affected red knot presence at a site during peak migration, when we sampled both substrates. Few relationships between prey and red knot flock size existed, suggesting that other unmeasured factors determined red knot numbers at occupied sites. Tide and mean daily water temperature affected prey abundance. Maximizing the diversity, availability, and abundance of prey for red knots on barrier islands requires management that encourages the presence of both sand and peat bank intertidal habitats.

This study accounted for and analyzed the carbon emissions of 13 cities in Jiangsu Province from 1999 to 2021. We compared the simulation effects of four models—STIRPAT, random forest, extreme gradient boosting, and support vector regression—on carbon emissions and performed model optimization. The random forest model demonstrated the best simulation performance. Using this model, we predicted the carbon emission paths for the 13 cities in Jiangsu Province under various scenarios from 2022 to 2040. The results show that Xuzhou has already achieved its peak carbon target. Under the high-speed development scenario, half of the cities can achieve their peak carbon target, while the remaining cities face significant challenges in reaching their peak carbon target. To further understand the factors influencing carbon emissions, we used the machine learning interpretation method SHAP and the features importance ranking method. Our analysis indicates that electricity consumption, population size, and energy intensity have a greater influence on overall carbon emissions, with electricity consumption being the most influential variable, although the importance of the factors varies considerably across different regions. Results suggest the need to tailor carbon reduction measures to the differences between cities and develop more accurate forecasting models.

There is suboptimal early initiation of breastfeeding (EIBF) with widespread prelacteal feeding in Ghana. However, studies exploring the determinants of EIBF and prelacteal feeding are limited in Ghana. The study was conducted to assess the prevalence and determinants of EIBF and prelacteal feeding in Northern Ghana. This cross-sectional study was conducted among 508 mothers with infants aged 0-24 months in the Sagnarigu Municipality of Northern Ghana. The quantitative data were collected using a structured questionnaire adapted from Ghana's demographic and health survey. Multivariate logistic regression was used to identify the independent determinants of EIBF and prelacteal feeding. The prevalence of EIBF and prelacteal feeding was 72% and 21%, respectively. The independent positive determinants of EIBF were partner support to breastfeed [adjusted Odds ratio (AOR): 1.86, 95% Confidence interval (CI): 1.09-3.17] and exposure to breastfeeding information during pregnancy (AOR = 1.63 (95% CI: 1.01-2.64). Lower odds of EIBF were observed among mothers from extended family (AOR = 0.62, 95% CI: 0.41-0.95). Regarding prelacteal feeding, negative determinants were having a normal weight baby (AOR: 0.50, 95% CI: 0.27-0.90), exposure to breastfeeding information during pregnancy (AOR: 0.54, 95% CI: 0.31-0.92), while experiencing delayed onset of lactation was a risk factor for prelacteal feeding practice (AOR: 2.35, 95% CI: 1.41-3.94). In this study, EIBF was slightly higher than the 2030 global target on EIBF with widespread prelacteal feeding practice. Health programs aimed at improving EIBF should focus on the women partners, nutrition counselling, and support to mothers from the extended family. In the same vein, programs aimed at discouraging prelacteal feeding practice should also target women at risk, such as those with low birthweight babies and women experiencing delayed lactation onset.