Cotton root systems and recovery of applied P and K fertilisers

With potential to accumulate substantial amounts of above-ground biomass, at maturity an irrigated cotton crop can have taken up more than 20 kg/ha phosphorus and often more than 200 kg/ha of potassium. Despite the size of plant accumulation of P and K, recovery of applied P and K fertilisers by the crop in our field experiment program has poor. Processing large amounts of mature cotton plant material to provide a representative sample for chemical analysis has not been without its challenges, but the questions regarding mechanism of where, how and when the plant is acquiring immobile nutrients remain. Dry matter measured early in the growing season (squaring, first white flower) have demonstrated a 50% increase in crop biomass to applied P (in particular), but it represents only 20% of the total P accumulation by the plant. By first open boll (and onwards), no response in dry matter or P concentration could be detected to P application. A glasshouse study indicated P recovery was greater (to FOB) where it was completely mixed through a profile as opposed to a banded application method suggesting cotton prefers a more diffuse distribution. The relative effects of root morphology, mycorrhizal fungi infection, seasonal growth patterns and how irrigation is applied are areas for future investigation on how, when and where cotton acquires immobile nutrients.

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.

On Systems Thinking, Systems Biology, and the in Silico Plant

The recent summary report of a Department of Energy Workshop on Plant Systems Biology (P.V. Minorsky [2003] Plant Physiol 132: 404-409) offered a welcomed advocacy for systems analysis as essential in understanding plant development, growth, and production. The goal of the Workshop was to consider methods for relating the results of molecular research to real-world challenges in plant production for increased food supplies, alternative energy sources, and environmental improvement. The rather surprising feature of this report, however, was that the Workshop largely overlooked the rich history of plant systems analysis extending over nearly 40 years (Sinclair and Seligman, 1996) that has considered exactly those challenges targeted by the Workshop. Past systems research has explored and incorporated biochemical and physiological knowledge into plant simulation models from a number of perspectives. The research has resulted in considerable understanding and insight about how to simulate plant systems and the relative contribution of various factors in influencing plant production. These past activities have contributed directly to research focused on solving the problems of increasing biomass production and crop yields. These modeling approaches are also now providing an avenue to enhance integration of molecular genetic technologies in plant improvement (Hammer et al., 2002).

DAQ00129 Improving the Integration of Legumes in Grain and Sugarcane Farming Systems in Southern Queensland

The farming systems and agribusinesses of the inland Burnett and southern coastal cropping regions of Queensland are becoming increasingly interlinked as grain legume crops, a key component of dryland cropping systems, become more firmly entrenched in the coastal sugarcane cropping areas. Soybeans, peanuts and possibly winter cereals like barley have a real and demonstrated role in sugarcane rotations, and assistance with the integration of those crops into viable and sustainable cropping systems with sugarcane will be critical to the futuer development of these industries.

Description of the pig production systems, biosecurity practices and herd health providers in two provinces with high swine density in the Philippines

A cross-sectional study was conducted between October 2011 and March 2012 in two major pig producing provinces in the Philippines. Four hundred and seventy one pig farms slaughtering finisher pigs at government operated abattoirs participated in this study. The objectives of this study were to group: (a) smallholder (S) and commercial (C) production systems into patterns according to their herd health providers (HHPs), and obtain descriptive information about the grouped S and C production systems; and (b) identify key HHPs within each production system using social network analysis. On-farm veterinarians, private consultants, pharmaceutical company representatives, government veterinarians, livestock and agricultural technicians, and agricultural supply stores were found to be actively interacting with pig farmers. Four clusters were identified based on production system and their choice of HHPs. Differences in management and biosecurity practices were found between S and C clusters. Private HHPs provided a service to larger C and some larger S farms, and have little or no interaction with the other HHPs. Government HHPs provided herd health service mainly to S farms and small C farms. Agricultural supply stores were identified as a dominant solitary HHP and provided herd health services to the majority of farmers. Increased knowledge of the routine management and biosecurity practices of S and C farmers and the key HHPs that are likely to be associated with those practices would be of value as this information could be used to inform a risk-based approach to disease surveillance and control. © 2014 Elsevier B.V.