Impact of a change in tillage and crop residue management practice on soil chemical and microbiological properties in a cereal-producing red duplex soil in NSW, Australia

The effect of a change of tillage and crop residue management practice on the chemical and micro-biological properties of a cereal-producing red duplex soil was investigated by superimposing each of three management practices (CC: conventional cultivation, stubble burnt, crop conventionally sown; DD: direct-drilling, stubble retained, no cultivation, crop direct-drilled; SI: stubble incorporated with a single cultivation, crop conventionally sown), for a 3-year period on plots previously managed with each of the same three practices for 14 years. A change from DD to CC or SI practice resulted in a significant decline, in the top 0-5 cm of soil, in organic C, total N, electrical conductivity, NH4-N, NO3-N, soil moisture holding capacity, microbial biomass and CO2 respiration as well as a decline in the microbial quotient (the ratio of microbial biomass C to organic C; P <0.05). In contrast, a change from SI to DD or CC practice or a change from CC to DD or SI practice had only negligible impact on soil chemical properties (P >0.05). However, there was a significant increase in microbial biomass and the microbial quotient in the top 0-5 cm of soil following the change from CC to DD or SI practice and with the change from SI to DD practice (P <0.05). Analysis of ester-linked fatty acid methyl esters (EL-FAMEs) extracted from the 0- to 5-cm and 5- to 10-cm layers of the soils of the various treatments detected changes in the FAME profiles following a change in tillage practice. A change from DD practice to SI or CC practice was associated with a significant decline in the ratio of fungal to bacterial fatty acids in the 0- to 5-cm soil (P <0.05). The results show that a change in tillage practice, particularly the cultivation of a previously minimum-tilled (direct-drilled) soil, will result in significant changes in soil chemical and microbiological properties within a 3-year period. They also show that soil microbiological properties are sensitive indicators of a change in tillage practice.

A comparative analysis of relevant crop carbon footprint calculators, with reference to cotton production in Australia

An increasing concern over the sustainability credentials of food and fiber crops require that farmers and their supply chain partners have access to appropriate and industry-friendly tools to be able to measure and improve the outcomes. This article focuses on one of the sustainability indicators, namely, greenhouse gas (GHG) emissions, and nine internationally accredited carbon footprint calculators were identified and compared on an outcomes basis against the same cropping data from a case study cotton farm. The purpose of this article is to identify the most “appropriate” methodology to be applied by cotton suppliers in this regard. From the analysis of the results, we subsequently propose a new integrated model as the basis for an internationally accredited carbon footprint tool for cotton and show how the model can be applied to evaluate the emission outcomes of different farming practices.

Calcium phosphate flocs and the clarification of sugar cane juice from whole of crop harvesting

Sugar cane biomass is one of the most viable feedstocks for the production of renewable fuels and chemicals. Therefore, processing the whole of crop (WC) (i.e., stalk and trash, instead of stalk only) will increase the amount of available biomass for this purpose. However, effective clarification of juice expressed from WC for raw sugar manufacture is a major challenge because of the amounts and types of non-sucrose impurities (e.g., polysaccharides, inorganics, proteins, etc.) present. Calcium phosphate flocs are important during sugar cane juice clarification because they are responsible for the removal of impurities. Therefore, to gain a better understanding of the role of calcium phosphate flocs during the juice clarification process,the effects of impurities on the physicochemical properties of calcium phosphate flocs were examined using small-angle laser light scattering technique, attenuated total reflectance Fourier transformed infrared spectroscopy, and X-ray powder diffraction. Results on synthetic sugar juice solutions showed that the presence of SiO2 and Na+ ions affected floc size and floc structure. Starch and phosphate ions did not affect the floc structure; however, the former reduced the floc size, whereas the latter increased the floc size. The study revealed that high levels of Na+ ions would negatively affect the clarification process the most, as they would reduce the amount of suspended particles trapped by the flocs. A complementary study on prepared WC juice using cold and cold/intermediate liming techniques was conducted. The study demonstrated that, in comparison to the one-stage (i.e., conventional) clarification process, a two-stage clarification process using cold liming removed more polysaccharides (≤19%),proteins (≤82%), phosphorus (≤53%), and SiO2 (≤23%) in WC juice but increased Ca2+ (≤136%) and sulfur (≤200%)

Rice husk biochar and crop residue amendment in subtropical cropping soils: Effect on biomass production, nitrogen use efficiency and greenhouse gas emissions

We investigated the effect of maize residues and rice husk biochar on biomass production, fertiliser nitrogen recovery (FNR) and nitrous oxide (N2O) emissions for three different subtropical cropping soils. Maize residues at two rates (0 and 10 t ha−1) combined with three rates (0, 15 and 30 t ha-1) of rice husk biochar were added to three soil types in a pot trial with maize plants. Soil N2O emissions were monitored with static chambers for 91 days. Isotopic 15N-labelled urea was applied to the treatments without added crop residues to measure the FNR. Crop residue incorporation significantly reduced N uptake in all treatments but did not affect overall FNR. Rice husk biochar amendment had no effect on plant growth and N uptake but significantly reduced N2O and carbon dioxide (CO2) emissions in two of the three soils. The incorporation of crop residues had a contrasting effect on soil N2O emissions depending on the mineral N status of the soil. The study shows that effects of crop residues depend on soil properties at the time of application. Adding crop residues with a high C/N ratio to soil can immobilise N in the soil profile and hence reduce N uptake and/or total biomass production. Crop residue incorporation can either stimulate or reduce N2O emissions depending on the mineral N content of the soil. Crop residues pyrolysed to biochar can potentially stabilise native soil C (negative priming) and reduce N2O emissions from cropping soils thus providing climate change mitigation potential beyond the biochar C storage in soils. Incorporation of crop residues as an approach to recycle organic materials and reduce synthetic N fertiliser use in agricultural production requires a thorough evaluation, both in terms of biomass production and greenhouse gas emissions.

Visual detection of occluded crop: For automated harvesting

This paper presents a novel crop detection system applied to the challenging task of field sweet pepper (capsicum) detection. The field-grown sweet pepper crop presents several challenges for robotic systems such as the high degree of occlusion and the fact that the crop can have a similar colour to the background (green on green). To overcome these issues, we propose a two-stage system that performs per-pixel segmentation followed by region detection. The output of the segmentation is used to search for highly probable regions and declares these to be sweet pepper. We propose the novel use of the local binary pattern (LBP) to perform crop segmentation. This feature improves the accuracy of crop segmentation from an AUC of 0.10, for previously proposed features, to 0.56. Using the LBP feature as the basis for our two-stage algorithm, we are able to detect 69.2% of field grown sweet peppers in three sites. This is an impressive result given that the average detection accuracy of people viewing the same colour imagery is 66.8%.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

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 technology 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 the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling 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. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.