Advances in Unmanned Aerial Vehicle (UAV) technology and data processing capabilities have made it feasible to obtain high-resolution imagery and three dimensional (3D) data which can be used for forest monitoring and assessing tree attributes. This study evaluates the applicability of low consumer grade cameras attached to UAVs and structure-from-motion (SfM) algorithm for automatic individual tree detection (ITD) using a local-maxima based algorithm on UAV-derived Canopy Height Models (CHMs). This study was conducted in a private forest at Cache Creek located east of Jackson city, Wyoming. Based on the UAV-imagery, we allocated 30 field plots of 20 m × 20 m. For each plot, the number of trees was counted manually using the UAV-derived orthomosaic for reference. A total of 367 reference trees were counted as part of this study and the algorithm detected 312 trees resulting in an accuracy higher than 85% (F-score of 0.86). Overall, the algorithm missed 55 trees (omission errors), and falsely detected 46 trees (commission errors) resulting in a total count of 358 trees. We further determined the impact of fixed tree window sizes (FWS) and fixed smoothing window sizes (SWS) on the ITD accuracy, and detected an inverse relationship between tree density and FWS. From our results, it can be concluded that ITD can be performed with an acceptable accuracy (F > 0.80) from UAV-derived CHMs in an open canopy forest, and has the potential to supplement future research directed towards estimation of above ground biomass and stem volume from UAV-imagery.

Cover crops are the plants which are grown to improve soil fertility, prevent soil erosion, enrichment and protection of soil, and enhance nutrient and water availability, and quality of soil. Cover crops provide several benefits to soils used for agriculture production. Cover crops are helpful in increasing and sustaining microbial biodiversity in soils. We summarized the effect of several cover crops in soil properties such as soil moisture content, soil microbial activities, soil carbon sequestration, nitrate leaching, soil water, and soil health. Selection of cover crops usually depends on the primary benefits which are provided by cover crops. Other factors may also include weather conditions, time of sowing, either legume or non-legume and timing and method of killing of a cover crop. In recent times, cover crops are also used for mitigating climate change, suppressing weeds in crops and increasing exchangeable nutrients such as Mg 2+ and K +. Cover crops are also found to be economical in long-term experiment studies. Although some limitations always come with several benefits. Cover crops have some problems including the method of killing, host for pathogens, regeneration, and not immediate benefits of using them. Despite the few limitations, cover crops improve the overall health of the soil and provide a sustainable environment for the main crops.

One of the major breeding objectives for watermelon (Citrullus lanatus [Thumb.] Matsum & Nakai) is improved fruit yield. High yielding genotypes have been identified, so we measured their stability for fruit yield and yield components over diverse environments. The objectives of this study were to (i) evaluate the yield of watermelon genotypes over years and locations, (ii) identify genotypes with high stability for yield, and (iii) measure the correlations among univariate and multivariate stability statistics. A diverse set of 40 genotypes was evaluated over 3 yr (2009, 2010, and 2011) and eight locations across the southern United States in replicated trials. Yield traits were evaluated over multiple harvests, and measured as marketable yield, fruit count, percentage cull fruit, percentage early fruit, and fruit size. There were strong effects of environment as well as genotype ´ environment interaction (G´E) on watermelon yield traits. Based on multiple stability measures, genotypes were classified as stable or unstable for yield. There was an advantage of hybrids over inbreds for yield components in both performance and responsiveness to favorable environments. Cultivars Big Crimson and Legacy are inbred lines with high yield and stability. A significant (P < 0.001) and positive correlation was measured for Shukla's stability variance (s i 2 ), Shukla's squared hat (sˆi 2 ), Wricke's ecovalence (W i ), and deviation from regression (S 2 d ) for all the traits evaluated in this study.

Genotype ´ environment interaction (G´E) can lead to diff erences in the performance of genotypes across environments. A G´E analysis can be used to analyze the stability of genotypes and the value of test locations. We developed a SAS program (SASG´E) that calculates univariate stability statistics, descriptive statistics, pooled and yearly ANOVA, genotypic and location variation, cluster analysis for location, and correlations among stability parameters. Univariate stability statistics calculated are Wricke's ecovalence (W i 2), Shukla's variance (s i 2), Lin and Binns cultivar superiority measure (P i), Francis and Kannenberg coeffi cient of variation (CV i), Kang's yield stability statistic (YS i), Perkins and Jinks b (b i), regression slope (b i), and deviation from regression (S d 2). Other output includes input fi les for analyzing stability in R soft ware using AMMI and GGEBiplotGUI packages. SASG´E uses SAS programming language features (macro and structured query language [SQL]) for repetitive tasks, making it effi cient and fl exible for the simultaneous analysis of multiple dependent variables. SASG´E is free and intended for use by scientists studying the performance of polygenic or quantitative traits in multiple environments. Th e SASG´E program is presented here and is also available at

Genotype x environmental interaction (GxE) can lead to differences in performance of genotypes over environments. GxE analysis can be used to analyze the stability of genotypes and the value of test locations. We developed an Rlanguage program (RGxE) that computes univariate stability statistics, descriptive statistics, pooled ANOVA, genotype F ratio across location and environment, cluster analysis for location, and location correlation with average location performance. Univariate stability statistics calculated are regression slope (b i ), deviation from regression (S 2 d ), Shukla's variance (σ i 2 ), S square Wricke's ecovalence (W i ), and Kang's yield stability (YS i ). RGxE is free and intended for use by scientists studying performance of polygenic or quantitative traits over multiple environments. In the present paper we provide the RGxE program and its components along with an example input data and outputs. Additionally, the RGxE program along with associated files is also available on GitHub at https://github.com/mahendra1/RGxE,