Case Studies which Demonstrate the Financial Viability of Precision Dairy Farming

A number of case studies are used to demonstrate the financial viability of precision farming methods for intensively managed pastures. Precision farming has sometimes been criticized as being technology-led where the management goals and desired outcomes are sometimes poorly defined. Case studies presented in this paper demonstrate a strong management approach where appropriate technologies are selected to contribute to the financial success of the farm. The first case study farm has increased milk production by 70% in four years, increased pasture production by 43%, reduced fertilizer costs to 43% of previous levels and has successfully predicted annual production to within 2 to 3% of actual. A strong emphasis on performance measurement is used to support a four stage management approach which consists of Planning, Measurement, Management and Review. The measurement systems in place inform the management at both strategic and operational levels and include twice daily recording of individual milk production and cow weight. The electronic identification (EID) system has been in place since 1996. The second case study farm has demonstrated similar savings in base fertiliser utilisation but has utilised other additional precision agriculture technologies such as the use of crop sensors and variable rate application of nutrients. Again a strong management focus is given, this time expressed as measure, manage, mitigate. This farming partnership also has a very strong environmental sustainability focus and recently received national recognition as the Supreme Winner of the 2013 New Zealand Ballance Farm Environmental Awards, giving further validation to the idea that precision agriculture is profitable as well as environmentally sustainable. Craige Mackenzie has also invested in precision irrigation, and there is a growing body of evidence to suggest that this method can give significant economic and environmental benefits on intensively managed pastures. Further case studies presenting the advantage of variable rate irrigation are also presented

Remote Sensing of Pasture Quality

Worldwide, farming systems are undergoing significant changes due to economic, environmental and social drivers. Agribusinesses must increasingly deliver products specified in terms of safety, health and quality. Increasing constraints are being placed on them by the market, the community and by government to achieve a financial benefit within social and environmental limits (Dynes et al. 2003). In order to meet these goals, producers must know the quantity and quality of the inputs into their feeding systems, be able to reliably predict the products and by-products being generated, and have the skills to be able to manage their business accordingly. Easy access to accurate and objective evaluation of forage is the first key component to meeting these objectives in livestock systems (Dynes et al. 2003) and remote sensing has considerable potential to be informative and cost-effective (Pullanagari et al. 2012b)

Equitability revisited: why the “equitable threat score” is not equitable

In the forecasting of binary events, verification measures that are “equitable” were defined by Gandin and Murphy to satisfy two requirements: 1) they award all random forecasting systems, including those that always issue the same forecast, the same expected score (typically zero), and 2) they are expressible as the linear weighted sum of the elements of the contingency table, where the weights are independent of the entries in the table, apart from the base rate. The authors demonstrate that the widely used “equitable threat score” (ETS), as well as numerous others, satisfies neither of these requirements and only satisfies the first requirement in the limit of an infinite sample size. Such measures are referred to as “asymptotically equitable.” In the case of ETS, the expected score of a random forecasting system is always positive and only falls below 0.01 when the number of samples is greater than around 30. Two other asymptotically equitable measures are the odds ratio skill score and the symmetric extreme dependency score, which are more strongly inequitable than ETS, particularly for rare events; for example, when the base rate is 2% and the sample size is 1000, random but unbiased forecasting systems yield an expected score of around −0.5, reducing in magnitude to −0.01 or smaller only for sample sizes exceeding 25 000. This presents a problem since these nonlinear measures have other desirable properties, in particular being reliable indicators of skill for rare events (provided that the sample size is large enough). A potential way to reconcile these properties with equitability is to recognize that Gandin and Murphy’s two requirements are independent, and the second can be safely discarded without losing the key advantages of equitability that are embodied in the first. This enables inequitable and asymptotically equitable measures to be scaled to make them equitable, while retaining their nonlinearity and other properties such as being reliable indicators of skill for rare events. It also opens up the possibility of designing new equitable verification measures

Identification of a suitable machine learning model for detection of asymptomatic Ganoderma boninense infection in oil palm seedlings using hyperspectral data

In Malaysia, oil palm industry has made an enormous contribution to economic and social prosperity. However, it has been affected by basal stem rot (BSR) disease caused by Ganoderma boninense (G. boninense) fungus. The conventional practice to detect the disease is through manual inspection by a human expert every two weeks. This study aimed to identify the most suitable machine learning model to classify the inoculated (I) and uninoculated (U) oil palm seedlings with G. boninense before the symptoms’ appearance using hyperspectral imaging. A total of 1122 sample points were collected from frond 1 and frond 2 of 28 oil palm seedlings at the age of 10 months old, with 540 and 582 reflectance spectra extracted from U and I seedlings, respectively. The significant bands were identified based on the high separation between U and I seedlings, where the differences were observed significantly in the NIR spectrum. The reflectance values of each selected band were later used as input parameters of the 23 machine learning models developed using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machine (SVM), k-nearest neighbor (kNN), and ensemble modelling with various types of kernels. The bands were optimized according to the classification accuracy achieved by the models. Based on the F-score and performance time, it was demonstrated that coarse Gaussian SVM with 9 bands performed better than the models with 35, 18, 14, and 11 bands. The coarse Gaussian SVM achieved an F-score of 95.21% with a performance time of 1.7124 s when run on a personal computer with an Intel® Core™ i7-8750H processor and 32 GB RAM. This early detection could lead to better management in the oil palm industry

Differences between healthy and Ganoderma boninense infected oil palm seedlings using spectral reflectance of young leaf data

Ganoderma boninense (G.boninense) is the causal agent of basal stem rot (BSR) which significantly reduced the productivity of oil palm plantations in Southeast Asia. At early stage, the disease did not show any physical symptoms that could be seen with naked eyes resulted in detection difficulties. To date, there was no effective detection for this disease, and conventional methods such as manual and laboratory-based required trained specialists as well as time-consuming. Therefore, this study was conducted using hyperspectral remote sensing to investigate the differences in spectral reflectance of young leaf (frond one (F1) of healthy and G. boninense infected oil palm seedlings. The seedlings were inoculated with G. boninense pathogen at five months old. At five months after inoculation, 558 spectral signatures of F1 were extracted from acquired hyperspectral images. Noise removal was done to the extracted spectral signatures to remove outliers in the data. Then, the spectral signatures were averaged and plotted to observe the differences. Differences in reflectance of healthy and G. boninense infected seedlings were seen evidently in the near-infrared (NIR) region. Thus, this study showed evidence that F1 spectral reflectance has the ability to detect early stage of G. boninense infection at oil palm seedlings

Iron is a ligand of SecA-like metal-binding domains in vivo

Funding: JEL thanks the Royal Society for a University Research Fellowship and the Wellcome Trust for the Q-band EPR spectrometer (099149/Z/12/Z).The ATPase SecA is an essential component of the bacterial Sec machinery, which transports proteins across the cytoplasmic membrane. Most SecA proteins contain a long C-terminal tail (CTT). In Escherichia coli, the CTT contains a structurally flexible linker domain and a small metal-binding domain (MBD). The MBD coordinates zinc via a conserved cysteine-containing motif and binds to SecB and ribosomes. In this study, we screened a high-density transposon library for mutants that affect the susceptibility of E. coli to sodium azide, which inhibits SecA-mediated translocation. Results from sequencing this library suggested that mutations removing the CTT make E. coli less susceptible to sodium azide at subinhibitory concentrations. Copurification experiments suggested that the MBD binds to iron and that azide disrupts iron binding. Azide also disrupted binding of SecA to membranes. Two other E. coli proteins that contain SecA-like MBDs, YecA and YchJ, also copurified with iron, and NMR spectroscopy experiments indicated that YecA binds iron via its MBD. Competition experiments and equilibrium binding measurements indicated that the SecA MBD binds preferentially to iron and that a conserved serine is required for this specificity. Finally, structural modelling suggested a plausible model for the octahedral coordination of iron. Taken together, our results suggest that SecA-like MBDs likely bind to iron in vivo.PostprintPeer reviewe

The development and validation of the major life changing decision profile (MLCDP)

Background Chronic diseases may influence patients taking major life changing decisions (MLCDs) concerning for example education, career, relationships, having children and retirement. A validated measure is needed to evaluate the impact of chronic diseases on MLCDs, improving assessment of their life-long burden. The aims of this study were to develop a validated questionnaire, the “Major Life Changing Decision Profile” (MLCDP) and to evaluate its psychometric properties. Methods 50 interviews with dermatology patients and 258 questionnaires, completed by cardiology, rheumatology, nephrology, diabetes and respiratory disorder patients, were analysed for qualitative data using Nvivo8 software. Content validation was carried out by a panel of experts. The first version of the MLCDP was completed by 210 patients and an iterative process of multiple Exploratory Factor Analyses and item prevalence was used to guide item reduction. Face validity and practicability was assessed by patients. Results 48 MLCDs were selected from analysis of the transcripts and questionnaires for the first version of the MLCDP, and reduced to 45 by combination of similar themes. There was a high intraclass correlation coefficient (0.7) between the 13 members of the content validation panel. Four more items were deleted leaving a 41-item MLCDP that was completed by 210 patients. The most frequently recorded MLCDs were decisions to change eating habits (71.4%), to change smoking/drinking alcohol habits (58.5%) and not to travel or go for holidays abroad (50.9%). Factor analysis suggested item number reduction from 41 to 34, to 29, then 23 items. However after taking into account item prevalence data as well as factor analysis results, 32 items were retained. The 32-item MLCDP has five domains education (3 items), job/career (9), family/relationships (5), social (10) and physical (5). The MLCDP score is expressed as the absolute number of decisions that have been affected. Conclusions The 32-item (5 domains) MLCDP has been developed as an easy to complete generic tool for use in clinical practice and for quality of life and epidemiological research. Further validation is required