Precision Agriculture and Remote Sensing Technologies in Central Queensland agricultural production systems

Demonstrate potential benefits of various Precision Agricultural technologies to Central Queensland farming community.

Influence of different nitrogen rates and DMPP nitrification inhibitor on annual N2/O emissions from a subtropical wheat–maize cropping system

Global cereal production will need to increase by 50% to 70% to feed a world population of about 9 billion by 2050. This intensification is forecast to occur mostly in subtropical regions, where warm and humid conditions can promote high N2O losses from cropped soils. To secure high crop production without exacerbating N2O emissions, new nitrogen (N) fertiliser management strategies are necessary. This one-year study evaluated the efficacy of a nitrification inhibitor (3,4-dimethylpyrazole phosphate—DMPP) and different N fertiliser rates to reduce N2O emissions in a wheat–maize rotation in subtropical Australia. Annual N2O emissions were monitored using a fully automated greenhouse gas measuring system. Four treatments were fertilized with different rates of urea, including a control (40 kg-N ha−1 year−1), a conventional N fertiliser rate adjusted on estimated residual soil N (120 kg-N ha−1 year−1), a conventional N fertiliser rate (240 kg-N ha−1 year−1) and a conventional N fertiliser rate (240 kg-N ha−1 year−1) with nitrification inhibitor (DMPP) applied at top dressing. The maize season was by far the main contributor to annual N2O emissions due to the high soil moisture and temperature conditions, as well as the elevated N rates applied. Annual N2O emissions in the four treatments amounted to 0.49, 0.84, 2.02 and 0.74 kg N2O–N ha−1 year−1, respectively, and corresponded to emission factors of 0.29%, 0.39%, 0.69% and 0.16% of total N applied. Halving the annual conventional N fertiliser rate in the adjusted N treatment led to N2O emissions comparable to the DMPP treatment but extensively penalised maize yield. The application of DMPP produced a significant reduction in N2O emissions only in the maize season. The use of DMPP with urea at the conventional N rate reduced annual N2O emissions by more than 60% but did not affect crop yields. The results of this study indicate that: (i) future strategies aimed at securing subtropical cereal production without increasing N2O emissions should focus on the fertilisation of the summer crop; (ii) adjusting conventional N fertiliser rates on estimated residual soil N is an effective practice to reduce N2O emissions but can lead to substantial yield losses if the residual soil N is not assessed correctly; (iii) the application of DMPP is a feasible strategy to reduce annual N2O emissions from sub-tropical wheat–maize rotations. However, at the N rates tested in this study DMPP urea did not increase crop yields, making it impossible to recoup extra costs associated with this fertiliser. The findings of this study will support farmers and policy makers to define effective fertilisation strategies to reduce N2O emissions from subtropical cereal cropping systems while maintaining high crop productivity. More research is needed to assess the use of DMPP urea in terms of reducing conventional N fertiliser rates and subsequently enable a decrease of fertilisation costs and a further abatement of fertiliser-induced N2O emissions.

Strategic tillage in no-till farming systems in Australia’s northern grains-growing regions: II. Implications for agronomy, soil and environment

In semi-arid sub-tropical areas, a number of studies concerning no-till (NT) farming systems have demonstrated advantages in economic, environmental and soil quality aspects over conventional tillage (CT). However, adoption of continuous NT has contributed to the build-up of herbicide resistant weed populations, increased incidence of soil- and stubble-borne diseases, and stratification of nutrients and organic carbon near the soil surface. Some farmers often resort to an occasional strategic tillage (ST) to manage these problems of NT systems. However, farmers who practice strict NT systems are concerned that even one-time tillage may undo positive soil condition benefits of NT farming systems. We reviewed the pros and cons of the use of occasional ST in NT farming systems. Impacts of occasional ST on agronomy, soil and environment are site-specific and depend on many interacting soil, climatic and management conditions. Most studies conducted in North America and Europe suggest that introducing occasional ST in continuous NT farming systems could improve productivity and profitability in the short term; however in the long-term, the impact is negligible or may be negative. The short term impacts immediately following occasional ST on soil and environment include reduced protective cover, soil loss by erosion, increased runoff, loss of C and water, and reduced microbial activity with little or no detrimental impact in the long-term. A potential negative effect immediately following ST would be reduced plant available water which may result in unreliability of crop sowing in variable seasons. The occurrence of rainfall between the ST and sowing or immediately after the sowing is necessary to replenish soil water lost from the seed zone. Timing of ST is likely to be critical and must be balanced with optimising soil water prior to seeding. The impact of occasional ST varies with the tillage implement used; for example, inversion tillage using mouldboard tillage results in greater impacts as compared to chisel or disc. Opportunities for future research on occasional ST with the most commonly used implements such as tine and/or disc in Australia’s northern grains-growing region are presented in the context of agronomy, soil and the environment.

Strategic tillage in no-till farming systems in Australia’s northern grains-growing regions: I. Drivers and implementation

Development of no-tillage (NT) farming has revolutionized agricultural systems by allowing growers to manage greater areas of land with reduced energy, labour and machinery inputs to control erosion, improve soil health and reduce greenhouse gas emission. However, NT farming systems have resulted in a build-up of herbicide-resistant weeds, an increased incidence of soil- and stubble-borne diseases and enrichment of nutrients and carbon near the soil surface. Consequently, there is an increased interest in the use of an occasional tillage (termed strategic tillage, ST) to address such emerging constraints in otherwise-NT farming systems. Decisions around ST uses will depend upon the specific issues present on the individual field or farm, and profitability and effectiveness of available options for management. This paper explores some of the issues with the implementation of ST in NT farming systems. The impact of contrasting soil properties, the timing of the tillage and the prevailing climate exert a strong influence on the success of ST. Decisions around timing of tillage are very complex and depend on the interactions between soil water content and the purpose for which the ST is intended. The soil needs to be at the right water content before executing any tillage, while the objective of the ST will influence the frequency and type of tillage implement used. The use of ST in long-term NT systems will depend on factors associated with system costs and profitability, soil health and environmental impacts. For many farmers maintaining farm profitability is a priority, so economic considerations are likely to be a primary factor dictating adoption. However, impacts on soil health and environment, especially the risk of erosion and the loss of soil carbon, will also influence a grower’s choice to adopt ST, as will the impact on soil moisture reserves in rainfed cropping systems.

Legumes or nitrification inhibitors to reduce N2O emissions from subtropical cereal cropping systems in Oxisols?

The DAYCENT biogeochemical model was used to investigate how the use of fertilizers coated with nitrification inhibitors and the introduction of legumes in the crop rotation can affect subtropical cereal production and N2O emissions. The model was validated using comprehensive multi-seasonal, high-frequency dataset from two field investigations conducted on an Oxisol, which is the most common soil type in subtropical regions. Different N fertilizer rates were tested for each N management strategy and simulated under varying weather conditions. DAYCENT was able to reliably predict soil N dynamics, seasonal N2O emissions and crop production, although some discrepancies were observed in the treatments with low or no added N inputs and in the simulation of daily N2O fluxes. Simulations highlighted that the high clay content and the relatively low C levels of the Oxisol analyzed in this study limit the chances for significant amounts of N to be lost via deep leaching or denitrification. The application of urea coated with a nitrification inhibitor was the most effective strategy to minimize N2O emissions. This strategy however did not increase yields since the nitrification inhibitor did not substantially decrease overall N losses compared to conventional urea. Simulations indicated that replacing part of crop N requirements with N mineralized by legume residues is the most effective strategy to reduce N2O emissions and support cereal productivity. The results of this study show that legumes have significant potential to enhance the sustainable and profitable intensification of subtropical cereal cropping systems in Oxisols.

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.

Development and Application of Embedding Methods for the Simulation of Large Chemical Systems

The high computational cost of correlated wavefunction theory (WFT) calculations has motivated the development of numerous methods to partition the description of large chemical systems into smaller subsystem calculations. For example, WFT-in-DFT embedding methods facilitate the partitioning of a system into two subsystems: a subsystem A that is treated using an accurate WFT method, and a subsystem B that is treated using a more efficient Kohn-Sham density functional theory (KS-DFT) method. Representation of the interactions between subsystems is non-trivial, and often requires the use of approximate kinetic energy functionals or computationally challenging optimized effective potential calculations; however, it has recently been shown that these challenges can be eliminated through the use of a projection operator. This dissertation describes the development and application of embedding methods that enable accurate and efficient calculation of the properties of large chemical systems.

Chapter 1 introduces a method for efficiently performing projection-based WFT-in-DFT embedding calculations on large systems. This is accomplished by using a truncated basis set representation of the subsystem A wavefunction. We show that naive truncation of the basis set associated with subsystem A can lead to large numerical artifacts, and present an approach for systematically controlling these artifacts.

Chapter 2 describes the application of the projection-based embedding method to investigate the oxidative stability of lithium-ion batteries. We study the oxidation potentials of mixtures of ethylene carbonate (EC) and dimethyl carbonate (DMC) by using the projection-based embedding method to calculate the vertical ionization energy (IE) of individual molecules at the CCSD(T) level of theory, while explicitly accounting for the solvent using DFT. Interestingly, we reveal that large contributions to the solvation properties of DMC originate from quadrupolar interactions, resulting in a much larger solvent reorganization energy than that predicted using simple dielectric continuum models. Demonstration that the solvation properties of EC and DMC are governed by fundamentally different intermolecular interactions provides insight into key aspects of lithium-ion batteries, with relevance to electrolyte decomposition processes, solid-electrolyte interphase formation, and the local solvation environment of lithium cations.

Comparing impacts of new cropping systems on biodiversity in traditional rural China

This study selected six geographically-similar villages with traditional and alternative cultivation methods (two groups of three, one traditional and two alternatives) in two counties of Henan Province, China—a representative area of the Huang-huai-hai Plain representing traditional rural China. Soil heavy metal concentrations, floral and faunal biodiversity, and socio-economic data were recorded. Heavy metal concentrations of surface soils from three sites in each village were analysed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS, chromium, nickel, copper, cadmium, and lead) and Atomic Absorption Spectrophotometer (AAS, zinc). The floral biodiversity of four land-use types was recorded following the Braun-Blanquet coverage-abundance method using 0.5×0.5m quadrats. The faunal biodiversity of two representative farmland plots was recorded using 0.3×0.3m quadrats at four 0.1m layers. The socio-economic data were recorded through face-to-face interviews of one hundred randomly selected households at each village. Results demonstrate different cultivation methods lead to different impact on above variables. Traditional cultivation led to lower heavy metal concentrations; both alternative managements were associated with massive agrochemical input causing heavy metal pollution in farmlands. Floral distribution was significantly affected by village factors. Diverse cultivation supported high floral biodiversity through multi-scale heterogeneous landscapes containing niches and habitats. Faunal distribution was also significantly affected by village factor nested within soil depth. Different faunal groups responded differently, with Acari being taxonomically diverse and Collembola high in densities. Increase in manual labour and crop number in villages using alternative cultivation may positively affect biodiversity. The results point to the conservation potential of diverse cultivation methods in traditional rural China and other regions under social and political reforms, where traditional agriculture is changing to unified, large-scale mechanized agriculture. This study serves as a baseline for conservation in small-holding agricultural areas of China, and points to the necessity of further studies at larger and longer scales.

A practical taxonomy of methods and literature for managing uncertain spatial data in geographic information systems

Perfect information is seldom available to man or machines due to uncertainties inherent in real world problems. Uncertainties in geographic information systems (GIS) stem from either vague/ambiguous or imprecise/inaccurate/incomplete information and it is necessary for GIS to develop tools and techniques to manage these uncertainties. There is a widespread agreement in the GIS community that although GIS has the potential to support a wide range of spatial data analysis problems, this potential is often hindered by the lack of consistency and uniformity. Uncertainties come in many shapes and forms, and processing uncertain spatial data requires a practical taxonomy to aid decision makers in choosing the most suitable data modeling and analysis method. In this paper, we: (1) review important developments in handling uncertainties when working with spatial data and GIS applications; (2) propose a taxonomy of models for dealing with uncertainties in GIS; and (3) identify current challenges and future research directions in spatial data analysis and GIS for managing uncertainties.

Bioprocess Technology for Antagonistic Pseudomonas MCCB 102, 103,Micrococcus MCCB 104, and Evaluation of Synechocystis MCCB 114, 115 as Probiotics for the Management of Vibrio in Shrimp Culture Systems

National Centre for Aquatic Animal Health, School of Environmental Studies, Cochin University of Science and Technology

Socio-economic characterization of integrated cropping systems in urban and peri-urban agriculture of Faisalabad, Pakistan

Faisalabad city is surrounded by agricultural lands, where farmers are growing vegetables, grain crops, and fodder for auto-consumption and local marketing. To study the socioeconomic impact and resource use in these urban and peri-urban agricultural production (UPA) systems, a baseline survey was conducted during 2009–2010. A total of 140 households were selected using a stratified sampling method and interviewed with a structured questionnaire. The results revealed that 96 % of the households rely on agriculture as their main occupation. Thirty percent of the households were owners of the land and the rest cultivated either rented or sharecropped land. Most of the families (70 %) were headed by a member with primary education, and only 10 % of the household head had a secondary school certificate. Irrigationwater was obtained from waste water (37 %), canals (27 %), and mixed alternative sources (36 %). A total of 35 species were cultivated in the UPA systems of which were 65% vegetables, 15% grain and fodder crops, and 5% medicinal plants. Fifty-nine percent of the households cultivated wheat, mostly for auto-consumption. The 51 % of the respondents grew cauliflower (Brassica oleracea L.) and gourds (Cucurbitaceae) in the winter and summer seasons, respectively. Group marketing was uncommon and most of the farmers sold their produce at the farm gate (45 %) and on local markets (43 %). Seeds and fertilizers were available from commission agents and dealers on a credit basis with the obligation to pay by harvested produce. A major problem reported by the UPA farmers of Faisalabad was the scarcity of high quality irrigation water, especially during the hot dry summer months, in addition to lacking adequate quantities of mineral fertilizers and other inputs during sowing time. Half of the respondents estimated their daily income to be less than 1.25 US$ and spent almost half of it on food. Monthly average household income and expenses were 334 and 237 US$, respectively.

Pea-barley intercropping for efficient symbiotic N-2-fixation, soil N acquisition and use of other nutrients in European organic cropping systems

Complementarity in acquisition of nitrogen (N) from soil and N-2-fixation within pea and barley intercrops was studied in organic field experiments across Western Europe (Denmark, United Kingdom, France, Germany and Italy). Spring pea and barley were sown either as sole crops, at the recommended plant density (P100 and B100, respectively) or in replacement (P50B50) or additive (P100B50) intercropping designs, in each of three cropping seasons (2003-2005). Irrespective of site and intercrop design, Land Equivalent Ratios (LER) between 1.4 at flowering and 1.3 at maturity showed that total N recovery was greater in the pea-barley intercrops than in the sole Crops Suggesting a high degree of complementarity over a wide range of growing conditions. Complementarity was partly attributed to greater soil mineral N acquisition by barley, forcing pea to rely more on N-2-fixation. At all sites the proportion of total aboveground pea N that was derived from N-2-fixation was greater when intercropped with barley than when grown as a sole crop. No consistent differences were found between the two intercropping designs. Simultaneously, the accumulation Of Phosphorous (P), potassium (K) and sulphur (S) in Danish and German experiments was 20% higher in the intercrop (P50B50) than in the respective sole crops, possibly influencing general crop yields and thereby competitive ability for other resources. Comparing all sites and seasons, the benefits of organic pea-barley intercropping for N acquisition were highly resilient. It is concluded that pea-barley intercropping is a relevant cropping strategy to adopt when trying to optimize N-2-fixation inputs to the cropping system. (C) 2009 Elsevier B.V. All rights reserved.

A CASE tool for the modeling of methods and information systems

MODSI is a multi-models tool for information systems modeling. A modeling process in MODSI can be driven according to three different approaches: informal, semi-formal and formal. The MODSI tool is therefore based on the linked usage of these three modeling approaches. It can be employed at two different levels: the meta-modeling of a method and the modeling of an information system.In this paper we start presenting different types of modeling by making an analysis of their particular features. Then, we introduce the meta-model defined in our tool, as well as the tool functional architecture. Finally, we describe and illustrate the various usage levels of this tool.