Tillage practices influence winter wheat grain yield prediction using seasonal precipitation

Making the best use of limited precipitation in semi-arid dryland cropping systems is important for crop production. Tillage practices may influence how this precipitation is utilized to predict winter wheat grain yield (Triticum aestivum L.). This study examined how tillage practices influence winter wheat grain yield prediction accuracy using precipitation received at three different periods of the season. Data were obtained from the period of 1972 to 2010 from a long-term tillage experiment. The study was designed as a winter wheat-fallow experiment. Each phase of the winter wheat-fallow rotation was present each year. The trial was set up as a randomized complete block design with three replications. Tillage treatments included no-till (NT), stubble mulch (SM), and moldboard plow (MP). Feed-forward neural network and multiple linear regression (ordinary least squares) were used to fit models under each tillage practice. No-till had the highest yield prediction accuracy with a root mean square error (RMSE) of 0.53 Mg ha-1 and accounted for 81% of the variability in grain yield. Stubble mulch had an RMSE of 0.55 Mg ha-1 and explained 73% of the variability in yield. Stubble mulch and NT were more accurate in yield prediction than MP which had an RMSE of 0.77 Mg ha-1 and accounted for 53% of the variability in yield. The multiple linear regression model was less accurate than the feed-forward neural network model since it had at least 0.30 Mg ha-1 more RMSE and accounted for only 5-8% of the variability in yield. Relative RMSE classified all neural network models as fair (21.6-27.3%) while linear regression models for the different tillage practices was classified as poor (33.3-43.6%), an illustration that the neural network models improve yield prediction accuracy. This study demonstrated that a large proportion of the variability in grain yield may be accounted for under NT and SM systems when using precipitation as predictors with neural networks

Assessing Tourism Development from Sen’s Capability Approach

The purpose of this study is to assess tourism development in the context of Sen’s capability approach. The study developed a model to investigate the relationship between tourism development and human development while focusing on two countries, Nicaragua and Costa Rica. The study applied a cointegration technique based on the Granger representation theorem. Overall, tourism development and human development reveals a tenuous relationship in both cases, reflecting some threshold effect. The importance of tourism growth is merited in the distribution of its benefits and the extent that tourism receipts are allocated to support human development (public health, education, safety, etc.). Rising incomes will not necessarily translate into human development performance, thereby rendering support to Sen’s contention that well-being should not be measured by its instrumental antecedents (such as income) alone. Private incomes through tourism expansion seem to matter most at lower levels of human development

Winter Wheat Seeding Decisions for Improved Grain Yield and Yield Components

The continual re-evaluation of agronomic practices is necessary to improve crop performance and sustainability of the production of winter wheat (Triticum aestivum L.), particularly as genetics and climate conditions change. Recommendations made about winter wheat planting dates, spacing, variety, and seed rates under normal climatic conditions may not be suitable in current times with more climate variability. Our experiment investigated the effect of planting date (early, historic-optimum, and late), row spacing (19 and 25 cm), variety (Goodstreak, Robidoux, and Wesley), and seed rate (1.8, 2.1, 2.3, 2.4, 2.6, 2.8, 3.1, and 3.4 M seeds ha−1) on winter wheat grain yield and yield components. The seeding rate was nested within row spacing in nested-factorial design. A nested-factorial treatment design was used with testing at several locations in Nebraska across two years. Variety had a substantial effect on winter wheat grain yield (p p ≤ 0.01). At Hemingford, for example, Wesley planted at 19 cm had 5.9% more yield when compared to Robidoux planted at 19 cm (5.5 Mg ha−1). Similarly, biomass was influenced by variety across sites (p −1) when planted with Goodstreak at two of the sites. While planting date by itself did not affect any of the responses evaluated, this research highlights the importance of comprehensive and holistic approaches to wheat production in the High Plains

Effects of cultivars and nitrogen management on wheat grain yield and protein

Low grain protein in hard red winter (HRW) wheat (Triticum aestivum L.) is a serious challenge for rainfed wheat growers, particularly in years with elevated grain yield. Proper nitrogen (N) management with adequate N rate and application timing is critical for optimizing grain yield and protein content. This 2-yr experiment evaluated the effects of different N rates and application timings (fall, spring, and split) on grain yield and protein of two HRW wheat cultivars. Field studies were conducted at four different sites across Nebraska under rainfed conditions in 2018/2019 (Year 1) and 2019/2020 (Year 2). A split plot randomized complete block design with wheat cultivars as the whole plots and factorial combinations of six N rates and three application timings as the sub-plots was used in four replications. Grain yield was associated positively and grain protein negatively with the water supply to demand ratio (WS:WD) in the season. Freeman cultivar yielded better in a year with higher WS:WD and a newly developed Ruth yielded better in a lower WS:WD year. Nitrogen fertilization significantly increased grain yield in the site-years with moderately higher WS:WD. There was an increase in grain protein with increasing N rates at all site-years. Spring and split applied N resulted in better grain yield than fall application in the site-year when there was a risk of N loss. This experiment suggested that an effective N management strategy for winter wheat should account for and be adaptable to weather variability to optimize grain yield and protein content