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The importance of accounting-integrated information systems for realising productivity and sustainability in the agricultural sector
اهمیت سیستم های اطلاعاتی حسابداری یکپارچه برای تحقق بهره وری و پایداری در بخش کشاورزی-2021 Agricultural information systems are an integral part of modern farming and are helping to
make a significant contribution to improved farm productivity and profitability. To date,
however, there has been a failure to integrate accounting information systems with onfarm data, despite today’s farmers facing unprecedented and interconnected economic
and resource pressures. This study explores this problem in more detail, defines the objectives of the solution and develops a model of integrated accounting and agricultural information systems, drawing on a ‘fads and fashions’ framework and advancing our
understanding of bundled innovations. Using data from a participatory case study in
Australian potato farming, the study integrates accounting data with soil moisture and climate data to track, alert and inform irrigation decisions. Development of preliminary digital software based on the model demonstrates how cost-informed tracking, alerts and
forecasting can be supported by bundling accounting information systems and sensing
technology. In doing so, the model extends the fads and fashions framework for agricultural information systems and demonstrates how accounting information can be the key
for improved water productivity, profitability and agricultural sustainability.
keywords: تصمیم گیری کشاورزی | سیستم های حسابداری یکپارچه | نوآوری های همراه | سنسور | اطلاعات دیجیتال | ایستگاه های آب و هوا | تصویربرداری ماهواره ای | Agricultural decision-making | Integrated accounting systems | Bundled innovations | Sensors | Digital information | Weather stations | Satellite imagery |
مقاله انگلیسی |
2 |
Estimating crime scene temperatures from nearby meteorological station data
تخمین دمای صحنه جرم از داده های ایستگاه هواشناسی مجاور-2020 The importance of temperature data in minimum postmortem interval (minPMI) estimations in criminal
investigations is well known. To maximise the accuracy of minPMI estimations, it is imperative to
investigate the different components involved in temperature modelling, such as the duration of
temperature data logger placement at the crime scene and choice of nearest weather station to compare
the crime scene data to. Currently, there is no standardised practice on how long to leave the temperature
data logger at the crime scene and the effects of varying logger duration are little known. The choice of the
nearest weather station is usually made based on availability and accessibility of data from weather
stations in the crime scene vicinity. However, there are no guidelines on what to look for to maximise the
comparability of weather station and crime scene temperatures.
Linear regression analysis of scene data with data from weather stations with varying time intervals,
distances, altitudes and microclimates showed the greatest goodness of
fit (R2), i.e. the highest compatibility between datasets, after 4–10 days. However, there was no significant improvement in
estimation of crime scene temperatures beyond a 5-day regression period. The smaller the distance
between scene and weather station and the higher the similarity in environment, such as altitude and
geographical area, resulted in greater compatibility between datasets.
Overall, the study demonstrated the complexity of choosing the most comparable weather station to
the crime scene, especially because of a high variation in seasonal temperature and numerous influencing
factors such as geographical location, urban ‘heat island effect’ and microclimates. Despite subtle
differences, for both urban and rural areas an optimal data fit was generally reached after about
five consecutive days within a radius of up to 30 km of the ‘crime scene’. With increasing distance and
differing altitudes, a lower overall data fit was observed, and a diminishing increase in R2 values was
reached after 4–10 consecutive days. These results demonstrate the need for caution regarding distances
and climate differences when using weather station data for retrospective regression analyses for
estimating temperatures at crime scenes. However, the estimates of scene temperatures from regression
analysis were better than simply using the temperatures from the nearest weather station. This study
provides recommendations for data logging duration of operation, and a baseline for further research into
producing standard guidelines for increasing the accuracy of minPMI estimations and, ultimately, greater
robustness of forensic entomology evidence in court. Keywords: Temperature modelling | Minimum post-mortem interval | Micro-climate | Forensic ecology | Temperature datalogger |
مقاله انگلیسی |
3 |
Prediction of the apple scab using machine learning and simple weather stations
پیش بینی apple scabبا استفاده از یادگیری ماشین و ایستگاه های ساده آب و هوا-2019 Apple scab is an economically important pest for apple production. It is controlled by applying fungicides when
conditions are ripe for the development of its spores. This occurs when leaves are wet for a long enough time at a
given temperature. However, leaf wetness is not a sufficiently well-defined agro-meteorological variable.
Moreover, the readings of leaf wetness sensors depend to a large extent on their location within the tree canopy.
Here we show that virtual wetness sensors, which are based on the easily obtained meteorological parameters
such as temperature, relative humidity and wind speed, can be used in place of physical sensors. To this end, we
have first collected data for two growing seasons from two types of wetness sensors planted in four locations in
the tree canopy. Then, for each sensor we have built a machine-learning model of leaf wetness using the
aforementioned meteorological variables. These models were further used as virtual sensors. Finally, Mills
models of apple scab infection were built using both real and virtual sensors and their results were compared.
The comparison of apple scab models based on real sensors shows significant variability. In particular, the
results of a model depend on the location of the sensor within the canopy. The models based on data obtained
from virtual sensors, are similar to the models based on physical sensors. Both types of models generate results
within the same range of variability. The outcome of the study shows that the control of apple scab can be based
on machine learning models based on standard meteorological variables. These variables can be readily obtained
using inexpensive meteorological stations equipped with basic sensors. These results open the way to a widespread
application of precise control of apple scab and consequently significant reduction of the use of pesticides
in apple production with benefits for environment, human health and economics of production. Keywords: Apple scab | Machine learning | Random fores |
مقاله انگلیسی |
4 |
Deep shared representation learning for weather elements forecasting
یادگیری نمایندگی اشتراکی عمیق برای پیش بینی عناصر آب و هوا-2019 The accuracy and reliability of weather forecasting are of importance for many economic, business
and management activities. This paper introduces novel data-driven predictive models based on
deep convolutional neural networks (CNN) architecture for temperature and wind speed prediction
in weather data. In particular, the proposed deep learning framework employs different upgrading
versions of the convolutional neural networks i.e. 1d-, 2d- and 3d-CNN. The introduced models exploit
the spatio-temporal multivariate weather data for learning shared representations using historical
data and forecasting weather elements for a number of user defined weather stations simultaneously
in an end-to-end fashion. The embedded feature learning component of the models as well as
coupling the learned features of different input layers have shown to have a significant impact on
the prediction task. The proposed models show promising results compared to the classical neural
networks architecture used for modeling nonlinear systems. Two experimental setups have been
considered based on a dataset collected from the Weather Underground website at six stations
located in Netherlands and Belgium as well as a larger dataset with higher temporal resolution from
the National Climatic Data Center (NCDC) at five stations located in Denmark. First, we focus on
simultaneously predicting the temperature of two main stations of Amsterdam and Brussels for 1–10
days ahead. The second experiment concerns wind speed prediction at three weather stations located
in Denmark for 6 and 12 h ahead. The obtained numerical results show that learning new shared
representations of the weather data by means of convolutional operations improves the prediction
performance. Keywords: Deep learning | Weather forecasting | Convolutional neural networks | Dimensionality reduction | Representation learning |
مقاله انگلیسی |