Effect of defoliation interval and height on the growth and quality of Arachis pintoi cv. Amarillo

The effect of defoliation on Amarillo (Arachis pintoi cv. Amarillo) was studied in a glasshouse and in mixed swards with 2 tropical grasses. In the glasshouse, Amarillo plants grown in pots were subjected to a 30/20°C or 25/15°C temperature regime and to defoliation at 10-, 20- or 30-day intervals for 60 days. Two field plot studies were conducted on Amarillo with either irrigated kikuyu (Pennisetum clandestinum) in autumn and spring or dryland Pioneer rhodes grass (Chloris gayana) over summer and autumn. Treatments imposed were 3 defoliation intervals (7, 14 and 28 days) and 2 residual heights (5 and 10 cm for kikuyu; 3 and 10 cm for rhodes grass) with extra treatments (56 days to 3 cm for both grasses and 21 days to 5 cm for kikuyu). Defoliation interval had no significant effect on accumulated Amarillo leaf dry matter (DM) at either temperature regime. At the higher temperature, frequent defoliation reduced root dry weight (DW) and increased crude protein (CP) but had no effect on stolon DW or in vitro organic matter digestibility (OMD). On the other hand, at the lower temperature, frequent defoliation reduced stolon DW and increased OMD but had no effect on root DW or CP. Irrespective of temperaure and defoliation, water-soluble carbohydrate levels were higher in stolons than in roots (4.70 vs 3.65%), whereas for starch the reverse occured (5.37 vs 9.44%). Defoliating the Amarillo-kikuyu sward once at 56 days to 3 cm produced the highest DM yield in autumn and sprong (582 and 7121 kg/ha DM, respectively), although the Amarillo component and OMD were substantially reduced. Highest DM yields (1726 kg/ha) were also achieved in the Amarillo-rhodes grass sward when defoliated every 56 days to 3 cm, although the Amarillo component was unaffected. In a mixed sward with either kikuyu or rhodes grass, the Amarillo component in the sward was maintained up to a 28-day defoliation interval and was higher when more severely defoliated. The results show that Amarillo can tolerate frequent defoliation and that it can co-exist with tropical grasses of differing growth habits, provided the Amarillo-tropical grass sward is subject to frequent and severe defoliation.

Liveweight prediction from hip height, condition score, fetal age and breed in tropical female cattle

Hip height, body condition, subcutaneous fat, eye muscle area, percentage Bos taurus, fetal age and diet digestibility data were collected at 17 372 assessments on 2181 Brahman and tropical composite (average 28% Brahman) female cattle aged between 0.5 and 7.5 years of age at five sites across Queensland. The study validated the subtraction of previously published estimates of gravid uterine weight to correct liveweight to the non-pregnant status. Hip height and liveweight were linearly related (Brahman: P<0.001, R-2 = 58%; tropical composite P<0.001, R-2 = 67%). Liveweight varied by 12-14% per body condition score (5-point scale) as cows differed from moderate condition (P<0.01). Parallel effects were also found due to subcutaneous rump fat depth and eye muscle area, which were highly correlated with each other and body condition score (r = 0.7-0.8). Liveweight differed from average by 1.65-1.66% per mm of rump fat depth and 0.71-0.76% per cm(2) of eye muscle area (P<0.01). Estimated dry matter digestibility of pasture consumed had no consistent effect in predicting liveweight and was therefore excluded from final models. A method developed to estimate full liveweight of post-weaning age female beef cattle from the other measures taken predicted liveweight to within 10 and 23% of that recorded for 65 and 95% of cases, respectively. For a 95% chance of predicted group average liveweight (body condition score used) being within 5, 4, 3, 2 and 1% of actual group average liveweight required 23, 36, 62, 137 and 521 females, respectively, if precision and accuracy of measurements matches that used in the research. Non-pregnant Bos taurus female cattle were calculated to be 10-40% heavier than Brahmans at the same hip height and body condition, indicating a substantial conformational difference. The liveweight prediction method was applied to a validation population of 83 unrelated groups of cattle weighed in extensive commercial situations on 119 days over 18 months (20 917 assessments). Liveweight prediction in the validation population exceeded average recorded liveweight for weigh groups by an average of 19 kg (similar to 6%) demonstrating the difficulty of achieving accurate and precise animal measurements under extensive commercial grazing conditions.

Induced Variations in Brassinosteroid Genes Define Barley Height and Sturdiness, and Expand the Green Revolution Genetic Toolkit

Reduced plant height and culm robustness are quantitative characteristics important for assuring cereal crop yield and quality under adverse weather conditions. A very limited number of short-culm mutant alleles were introduced into commercial crop cultivars during the Green Revolution. We identified phenotypic traits, including sturdy culm, specific for deficiencies in brassinosteroid biosynthesis and signaling in semidwarf mutants of barley (Hordeum vulgare). This set of characteristic traits was explored to perform a phenotypic screen of near-isogenic short-culm mutant lines from the brachytic, breviaristatum, dense spike, erectoides, semibrachytic, semidwarf, and slender dwarf mutant groups. In silico mapping of brassinosteroid-related genes in the barley genome in combination with sequencing of barley mutant lines assigned more than 20 historic mutants to three brassinosteroid-biosynthesis genes (BRASSINOSTEROID-6-OXIDASE, CONSTITUTIVE PHOTOMORPHOGENIC DWARF, and DIMINUTO) and one brassinosteroid-signaling gene (BRASSINOSTEROID-INSENSITIVE1 [HvBRI1]). Analyses of F2 and M2 populations, allelic crosses, and modeling of nonsynonymous amino acid exchanges in protein crystal structures gave a further understanding of the control of barley plant architecture and sturdiness by brassinosteroid-related genes. Alternatives to the widely used but highly temperature-sensitive uzu1.a allele of HvBRI1 represent potential genetic building blocks for breeding strategies with sturdy and climate-tolerant barley cultivars.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.