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1. A Tool for Monitoring Genetic Selection Differentials in Dairy Herds in CanadaA software tool was developed to allow a dairy producer and/or agricultural advisor to monitor the genetic selection differentials (GSD) that a dairy farm is making. The objectives of this study were (i) to monitor GSD in individual farms, over years, so that producers can be advised as to whether or not they are achieving their selection objectives (and hence optimizing productivity and profitability); (ii) the development of a prototype software tool and visualization model to assist produc... B.A. Hagan, R.I. Cue |
2. A Pilot Study on Monitoring Drinking Behavior in Bucket Fed Dairy Calves Using an Ear-Attached Tri-Axial AccelerometerAccelerometers support the farmer with collecting information about animal behavior and thus allow a reduction in visual observation time. The milk intake of calves fed by teat-buckets has not been monitored automatically on commercial farms so far, although it is crucial for the calves’ development. This pilot study was based on bucket-fed dairy calves and intended (1) to evaluate the technical feasibility of using an ear-attached accelerometer (SMARTBOW, Smartbow GmbH, Weibern, Austri... L. Roland, L. Lidauer, G. Sattlecker, F. Kickinger, W. Auer, V. Sturm, D. Efrosinin, M. Drillich, M. Iwersen, A. Berger |
3. Evaluation of an Ear Tag Based Accelerometer for Monitoring Rumination Time, Chewing Cycles and Rumination Bouts in Dairy CowsThe objective of this study was to evaluate the ear tag based accelerometer SMARTBOW (Smartbow, Weibern, Austria) for detecting rumination time, chewing cycles and rumination bouts in dairy cows. For this, the parameters were determined by analyses of video recordings as reference and compared with the results of the accelerometer system. Additionally, the intra- and inter-observer reliability as well as the agreement of direct cow observations and video recordings was tested. Ten Simmental c... M. Iwersen, S. Reiter, V. Schweinzer, F. Kickinger, M. Öhlschuster, L. Lidauer, W. Auer, M. Drillich, A. Berger |
4. Ear-Attached Accelerometer as an On-Farm Device to Predict the Onset of Calving in Dairy CowsThe objective of this study on an ear-attached accelerometer in dairy cows was (1) to determine activity, rumination and lying time of the dams prior to calving, and include group level of measured variables (2) use the data to develop an algorithm to predict calving and (3) to test the performance of this algorithm. Video observations (24h/d) were used as reference for these events. Four weeks before expected calving, an ear-tag integrated tri-axial accelerometer (SMARTBOW system) was attach... S. Krieger, M. Oczak, L. Lidauer, F. Kickinger, M. Öhlschuster, W. Auer, M. Drillich, M. Iwersen, A. Berger |
5. Evaluation of the Ear-Tag Sensor System SMARTBOW for Detecting Estrus Events in Indoor Housed Dairy CowsLivestock farming technologies have a tremendous potential to improve and support farmers in herd management decisions, in particular in reproductive management. Nowadays, estrus detection in cows is challenging and many detection tools are available. The company Smartbow (Weibern, Austria) developed a novel ear-tag sensor, which consists of a 3D-accelerometer that records head and ear movements of cows as basis for algorithm development and further analyses. Estrus detection by the SMARTBOW ... V. Schweinzer, L. Lidauer, F. Kickinger, M. Öhlschuster, W. Auer, M. Drillich, M. Iwersen, A. Berger |
6. Exploring Relationships Between Dairy Herd Improvement Metrics in Minas Gerais – Brazil Dairy HerdsThe objective of the present study was to apply principal component analysis (PCA) on Brazilian Dairy Herd Improvement (DHI) data to discover the subset of most meaningful variables to describe complete lactations. The Holstein Livestock Breeders Association of Minas Gerais provided data collected between 2005 and 2016 from 122 dairy farms located in the State of Minas Gerais – Brazil. Twelve numerical variables were selected from the original dataset and four additional variables were ... G.M. Dallago, D. Figueiredo, R. Santos, P. Andrade, D. Santos |
7. Evaluation of a Wireless Pulse Oximeter to Measure Arterial Oxygen Saturation and Pulse Rate in Newborn Holstein Friesian CalvesPulse oximetry is a well-established technique in nowadays human and veterinarian medicine. Also in the farm animal sector, it could be a useful tool to detect critical conditions of the oxygen supply and the cardiovascular system of the patient. However, its use in ruminant medicine is still limited to experimental application. The objective of this study was to evaluate the accuracy of a Radius-7 Wearable Pulse Oximeter (Masimo Corporation, Irvine, CA) for monitoring the vital parameters of... P. Kanz, S. Krieger, M. Drillich, M. Iwersen |
8. Time Series Analysis of Somatic Cell Count from Dairy Herds in Minas Gerais - BrazilThe objective of this study was to analyze the temporal variation of somatic cell count (SCC) in milk of dairy cows from the state of Minas Gerais, Brazil. The Holstein Livestock Breeders Association of Minas Gerais provided data collected from 128 dairy farms located in the state of Minas Gerais between the years of 2000 and 2016. The database contains the SCC average of a total of 91,851 305-day lactations of Holstein animals. The annual SCC average was calculated as well as the percentage ... G.M. Dallago, D. Figueiredo, R. Santos, D. Santos, L. Guimarães, C. Santos, T. Castro, A. Santos, L. Otoni, J. Andrade |
9. The Influence of Calf’s Sex on Total Milk Yield and Its Constituents of Dairy CowsThe objective of the present work was to evaluate the influence of the sex of the calf on total milk yield and its constituents of Holstein-Friesian dairy cows. The Holstein Livestock Breeders Association of Minas Gerais provided data collected over the years from 2000 to 2016 from 127 dairy farms located in the state of Minas Gerais – Brazil. The data set analyzed contained 61747 observations of Holstein-Friesian animals that calved female (n = 28903) or male (n = 32844) calf. Fat, pro... G.M. Dallago, D. Figueiredo, R. Santos, D. Santos, L. Barroso, G. Alves, J. Vieira, L. Guimarães, C. Santos , L. Maciel |
10. Economic Evaluation of Automatic Heat Detection Systems in Dairy FarmingAlthough heat detection makes a relevant contribution to good reproduction performance of dairy cattle, available studies on the economic evaluations of automatic heat detection systems are limited. Therefore, the objective of this article is to provide an economic evaluation of using automatic heat detection. The effect of different heat detection rates on gross margin is modelled with SimHerd (SimHerd A/S, Denmark). The analysis considers all additional investment costs in automatic heat de... J. Pfeiffer, M. Gandorfer, J.F. Ettema |
11. The Spread of Precision Livestock Farming Technology at Dairy Farms in East HungaryDuring the survey, 25 dairy farms were examined in East Hungary in Hajdú-Bihar (H-B) County between 2017 and 2018 by methodical observation and oral interviews with the farm managers, about the spread of Precision Livestock Farming (PLF) technologies. Among Holstein Friesian dairy farms in the County 60% were questioned, and the representativity was above 47 percent ins each size category. Nine precision farming equipment were examined on the farms: milking robot or robotic carousel mi... C. Nándor, T. Rátonyi, E. Harsányi, P. Ragán, Z. Hagymássy, J. Nagy, A. Vántus |
12. Usage of Milk Revenue Per Minute of Boxtime to Assess Cows Selection and Farm Profitability in Automatic Milking SystemsThe number of farms implementing robotic milking systems, usually referred as automatic milking systems (AMS), is increasing rapidly. AMS efficiency is a priority to achieve high milk production and higher incomes from dairy herds. Recent studies suggested that milkability (i.e., amount of milk produced per total time spent in the AMS [kg milk/ minute of boxtime]) could be used for as a criteria for genetic evaluations. Therefore, an indicator of milkability was developed, which combines econ... L. Fadul-pacheco, G. Bisson, R. Lacroix, M. Séguin, R. Roy, E. Vasseur, D. Lefebvre |
13. Relationships Between First Test Day Metrics of First Lactation Cows to Evaluate Transition PeriodThe objective of this study was to apply principal component analysis (PCA) and multiple correspondence analysis (MCA) on Dairy Herd Improvement (DHI) data of animals on their first lactation to discover the most meaningful set of variables that describe the outcome on the first test day. Data collected over 4 years were obtained from 13 dairy herds located in Québec – Canada. The data set was filtered to contain only information from first test day of animals on their first lact... G.M. Dallago, D. Figueiredo, R. Santos, P. Andrade, D.E. Santschi, R. Lacroix, D.M. Lefebvre |
14. Feature Extraction from Radial Descriptor Lines for Body Condition Scoring of CowsBody condition score (BCS) is considered as one of the most important indices for managing dairy cows, which is used to evaluate fat cover and changes in body condition. Dairy farmers should be aware of their cows BCS to be able to identify the patient cows on time and manage diets when needed. In this study, we have introduced a new index which uses Radial Descriptor Lines (RDL) for BC scoring. Based on the fact that the fatter the cow the smoother the back surface, we hypothesised that the ... A. Jafari, F. Karimi, A. Werner, S. Ghoreishi, S. Kargar |
15. Dynamic Feeding Intake Monitoring in Growing-Finishing Pigs Reared Under Precision Feeding StrategiesPigs exposed to challenges with no prior experience change their daily feeding intake pattern. A method identifying deviations from normal feeding patterns could be used to develop a model framework to estimate individual nutrient requirements of challenged pigs fed with precision feeding systems. The objective of this study was to develop a tool for early identification of feed intake deviations in precision fed growing-finishing pigs. Feed intake measurements collected during 84 d in 126 gr... L. Hauschild, A.R. Kristensen, I. Andretta, C. Pomar, A. Remus |
16. Precision Feeding Can Significantly Reduce Lysine Intake and Nitrogen Excretion Without Compromising the Performance of Growing PigsThe impact of using a mathematical model estimating real-time daily lysine requirements in a sustainable precision feeding program for growing pigs was investigated in two performance trials. Three treatments were tested in the first trial (60 pigs of 41.2±0.5 kg): a three-phase feeding program (3P) obtained by blending fixed proportions of feeds A (high nutrient concentration) and B (low nutrient concentration); and two daily-phase feeding programs in which the blended proportions of ... C. Pomar, I. Andretta, J. Rivest, L. Hauschild, J. Pomar |
17. Environmental Impacts of Precision Feeding Programs Applied in Brazilian Pig ProductionThis study was undertaken to evaluate the effect that switching from conventional to precision feeding systems during the growing-finishing phase would have on the potential environmental impact of Brazilian pig production. Standard life-cycle assessment procedures were used, with a cradle-to-farm gate boundary. The inputs and outputs of each interface of the life cycle were organized in a model. Grain production was independently characterized in the Central-West and South regions of Brazil,... C. Pomar, I. Andretta, L. Hauschild, M. Kipper, P.S. Pires |
18. Using Deep Learning - Convolutional Naural Networks (CNNS) for Real-Time Fruit Detection in the TreeImage/video processing for fruit detection in the tree using hard-coded feature extraction algorithms have shown high accuracy on fruit detection during recent years. While accurate, these approaches even with high-end hardware are still computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks architecture based on single-stage detectors. Using deep-learning techniques eliminates the need for hard-code specific features for s... K. Bresilla, L. Manfrini, A. Boini, G. Perulli, B. Morandi, L.C. Grappadelli |
19. Digital Transformation of Canadian Agri-FoodAgriculture in Canada is on the cusp of a dramatic revolution as a result of the digital transformation of the industry driven by the emergence of tools such as Precision Agri-Food Technologies and the Internet of Things (IoT, a network of interconnected physical devices capable of connecting to the internet). With the expected exponential growth of data from the application of innovative technologies such as IoT by the Canadian Agri-Food industry, Canada has the potential to gain valuable in... K.J. Hand |
20. Optimal Sensor Placement for Field-Wide Estimation of Soil MoistureSoil moisture is one of the most important parameters in precision agriculture. While techniques such as remote sensing seems appropriate for moisture monitoring over large areas, they generally do not offer sufficiently fine resolution for precision work, and there are time restrictions on when the data is available. Moreover, while it is possible to get high resolution-on demand data, but the costs are often prohibitive for most developing countries. Direct ground level measuremen... H. Pourshamsaei, A. Nobakhti |
21. A Case Study Comparing Machine Learning and Vegetation Indices for Assessing Corn Nitrogen Status in an Agricultural Field in MinnesotaCompact hyperspectral sensors compatible with UAV platforms are becoming more readily available. These sensors provide reflectance in narrow spectral bands while covering a wide range of the electromagnetic spectrum. However, because of the narrow spectral bands and wide spectral range, hyperspectral data analysis can benefit greatly from data mining and machine learning techniques to leverage its power. In this study, rainfed corn was grown during the 2017 growing season using four nitrogen ... A. Laacouri, T. Nigon, D. Mulla, C. Yang |
22. Weed Detection Among Crops by Convolutional Neural Networks with Sliding WindowsOne of the primary objectives in the field of precision agriculture is weed detection. Detecting and expunging weeds in the initial stages of crop growth with deep learning technique can minimize the usage of herbicides and maximize the crop yield for the farmers. This paper proposes a sliding window approach for the detection of weed regions using convolutional neural networks. The proposed approach involves two processes: (1) Image extraction and labelling, (2) building and training our neu... K. Kantipudi, C. Lai, C. Min, R.C. Chiang |
23. Changing the Cost of Farming: New Tools for Precision FarmingAccurate prescription maps are essential for effective variable rate fertilizer application. Grid soil sampling has most frequently been used to develop these prescription maps. Past research has indicated several technical and economic limitations associated with this approach. There is a need to keep the number of samples to a minimum while still allowing a reasonable level of map quality. As can be seen, precision agriculture managemen... P. Nagel, K. Fleming |
24. On-Farm Digital Solutions and Their Associated Value to North American FarmersDigital tools and data collection have become standard in a wide variety of present day agricultural operations. An array of digital tools, such as high resolution operational mapping, remote sensing, and farm management software offer solutions to many of the problems in modern agriculture. These technologies and services can, if implemented correctly, provide both immediate and long term agronomic value. A growing number of producers in Ohio and around North America question the proper meth... R. Colley iii, J. Fulton, N. Douridas, K. Port |
25. An Efficient Data Warehouse for Crop Yield PredictionNowadays, precision agriculture combined with modern information and communications technologies, is becoming more common in agricultural activities such as automated irrigation systems, precision planting, variable rate applications of nutrients and pesticides, and agricultural decision support systems. In the latter, crop management data analysis, based on machine learning and data mining, focuses mainly on how to efficiently forecast and improve crop yield. In recent years, raw and semi-pr... V.M. Ngo, N. Le-khac, M. Kechadi |
26. AgDataBox – API (Application Programming Interface)E-agricultural is an emerging field focusing in the enhancement of agriculture and rural development through improve in information and data processing. The data-intensive characteristic of these domains is evidenced by the great variety of data to be processed and analyzed. Countrywide estimates rely on maps, spectral images from satellites, and tables with rows for states, regions, municipalities, or farmers. Precision agriculture (PA) relies on maps of within field variability of soil and ... C.L. Bazzi, E.P. Jasse, E.G. Souza, P.S. Magalhães, G.K. Michelon, K. Schenatto, A. Gavioli |
27. Accelerating Precision Agriculture to Decision Agriculture: Enabling Digital Agriculture in AustraliaFor more than two decades, the success of Australia’s agricultural and rural sectors has been supported by the work of the Rural Research and Development Corporations (RDCs). The RDCs are funded by industry and government. For the first time, all fifteen of Australia’s RDC’s have joined forces with the Australian government to design a solution for the use of big data in Australian agriculture. This is the first known example of a nationwide approach for the digital transfor... J. Trindall, R. Rainbow |
28. Pest Detection on UAV Imagery Using a Deep Convolutional Neural NetworkPresently, precision agriculture uses remote sensing for the mapping of crop biophysical parameters with vegetation indices in order to detect problematic areas, and then send a human specialist for a targeted field investigation. The same principle is applied for the use of UAVs in precision agriculture, but with finer spatial resolutions. Vegetation mapping with UAVs requires the mosaicking of several images, which results in significant geometric and radiometric problems. Furthermore, even... Y. Bouroubi, P. Bugnet, T. Nguyen-xuan, C. Bélec, L. Longchamps, P. Vigneault, C. Gosselin |
29. Forecasting Crop Yield Using Multi-Layered, Whole-Farm Data Sets and Machine LearningThe ultimate goal of Precision Agriculture is to improve decision making in the business of farming. Many broadacre farmers now have a number of years of crop yield data for their fields which are often augmented with additional spatial data, such as apparent soil electrical conductivity (ECa), soil gamma radiometrics, terrain attributes and soil sample information. In addition there are now freely available public datasets, such as rainfall, digital soil maps and archives of satellite remote... P. Filippi, E.J. Jones, M. Fajardo, B.M. Whelan, T.F. Bishop |
30. Shared Protocols and Data Template in Agronomic TrialsDue to the overlap of many disciplines and the availability of novel technologies, modern agriculture has become a wide, interdisciplinary endeavor, especially in Precision Agriculture. The adoption of a standard format for reporting field experiments can help researchers to focus on the data rather than on re-formatting and understanding the structure of the data. This paper describes how a European consortium plans to: i) create a “handbook” of protocols for reporting definition... D. Cammarano, D. Drexler, P. Hinsinger, P. Martre, X. Draye, A. Sessitsch, N. Pecchioni, J. Cooper, W. Helga, A. Voicu |
31. Improving the Use of Artificial Neural Networks for Site-Specific Nitrogen FertilizationFor the planning of site-specific nitrogen fertilization, adequate decision rules are needed. Prerequisite for site specific nitrogen fertilization is the site specific forecast of yield. For this the use of artificial neural networks (ANN) has proven particularly interesting. Therefore, ANN based small-scale yield forecasts are realized in order to deviate the economic optimum of fertilization. The basis of yield forecasts with ANN are different site-specific input variables that have presum... J.S. Hauser, P. Wagner |
32. Data Clustering Tools for Understanding Spatial Heterogeneity in Crop Production by Integrating Proximal Soil Sensing and Remote Sensing DataRemote sensing (RS) and proximal soil sensing (PSS) technologies offer an advanced array of methods for obtaining soil property information and determining soil variability for precision agriculture. A large amount of data collected using these sensors may provide essential information for precision or site-specific management in a production field. In this paper, we introduced a new clustering technique was introduced and compared with existing clustering tools for determining relatively hom... M. Saifuzzaman, V.I. Adamchuk, H. Huang, W. Ji, N. Rabe, A. Biswas |
33. Data-Driven Agricultural Machinery Activity Anomaly Detection and ClassificationIn modern agriculture, machinery has become the one of the necessities in providing safe, effective and economical farming operations and logistics. In a typical farming operation, different machines perform different tasks, and sometimes are used together for collaborative work. In such cases, different machines are associated with representative activity patterns, for example, in a harvest scenario, combines move through a field following regular swaths while grain carts follow irregular pa... Y. Wang, A. Balmos, J. Krogmeier, D. Buckmaster |
34. ADAPT: A Rosetta Stone for Agricultural DataModern farming requires increasing amounts of data exchange among hardware and software systems. Precision agriculture technologies were meant to enable growers to have information at their fingertips to keep accurate farm records (and calculate production costs), improve decision-making and promote efficiencies in crop management, enable greater traceability, and so forth. The attainment of these goals has been limited by the plethora of proprietary, incompatible data formats among... D.D. Danford, K.J. Nelson, S.T. Rhea, M.W. Stelford, R. Ferreyra, J.A. Wilson, B.E. Craker |
35. Analyzing Trends for Agricultural Decision Support System Using Twitter DataThe trends and reactions of the general public towards global events can be analyzed using data from social platforms, including Twitter. The number of tweets has been reported to help detect variations in communication traffic within subsets like countries, age groups and industries. Similarly, publicly accessible data and (in particular) data from social media about agricultural issues provide a great opportunity for obtaining instantaneous snapshots of farmers’ opinions and a method ... S. Jha, D. Saraswat, M.D. Ward |
36. Effective Use of a Debris Cleaning Brush for Mechanical Wild Blueberry HarvestingWild blueberries are an important horticultural crop native to northeastern North America. Management of wild blueberry fields has improved over the past decade causing increased plant density and leaf foliage. The majority of wild blueberry fields are picked mechanically using tractor mounted harvesters with 16 rotating rakes that gently comb through the plants. The extra foliage has made it more difficult for the cleaning brush to remove unwanted debris (leaf, stems, weeds, etc.) from the p... K. Esau, Q. Zaman, A. Farooque, A. Schumann |
37. Three Years of On-Farm Evaluation of Dynamic Variable Rate Irrigation: What Have We Learned?This paper will present a dynamic Variable Rate Irrigation System developed by the University of Georgia. The system consists of the EZZone management zone delineation tool, the UGA Smart Sensor Array (UGA SSA) and an irrigation scheduling decision support tool. An experiment was conducted in 2015, 2016 and 2017 in two different peanut fields to evaluate the performance of using the UGA SSA to dynamically schedule Variable Rate Irrigation (VRI). For comparison reasons strips were designed wit... V. Liakos, W. Porter, X. Liang, M. Tucker, A. Mclendon, C. Perry, G. Vellidis |
38. Reverse Modelling of Yield-Influencing Soil Variables in Case of Few Soil DataOur hypothesis was that simple models can be applied to predict yield by using only those yield data which spatially coincide with the soil data and the remaining yield data and the models can be used to test different sampling and interpolation approaches commonly applied in precision agriculture and to better predict soil variables at not observed locations. Three strategies for composite sample collection were compared in our study. Point samples were taken 1.) along lines within homogenou... I. Sisák, A. Benő, K. Szabó, M. Kocsis, J. Abonyi |
39. Optimized Soil Sampling Location in Management Zones Based on Apparent Electrical Conductivity and Landscape AttributesOne of the limiting factors to characterize the soil spatial variability is the need for a dense soil sampling, which prevents the mapping due to the high demand of time and costs. A technique that minimizes the number of samples needed is the use of maps that have prior information on the spatial variability of the soil, allowing the identification of representative sampling points in the field. Management Zones (MZs), a sub-area delineated in the field, where there is relative homogeneity i... G.K. Michelon, G.M. Sanches, I.Q. Valente, C.L. Bazzi, P.L. De menezes, L.R. Amaral, P.G. Magalhaes |
40. Optimal Placement of Proximal Sensors for Precision Irrigation in Tree CropsIn agriculture, use of sensors and controllers to apply only the quantity of water required, where and when it is needed (i.e., precision irrigation), is growing in importance. The goal of this study was to generate relatively homogeneous management zones and determine optimal placement of just a few sensors within each management zone so that reliable estimation of plant water status could be obtained to implement precision irrigation in a 2.0 ha almond orchard located in California, USA. Fi... C.L. Bazzi, K. Schenatto, S. Upadhyaya, F. Rojo |
41. Prediction of Corn Economic Optimum Nitrogen Rate in ArgentinaStatic (i.e. texture and soil depth) and dynamic (i.e. soil water, temperature) factors play a role in determining field or subfield economically optimal N rates (EONR). We used 50 nitrogen (N) trials from Argentina at contrasting landscape positions and soil types, various soil-crop measurements from 2012 to 2017, and statistical techniques to address the following objectives: a) characterize corn yield and EONR variability across a multi-landscape-year study in central west Buenos Aire... L. Puntel, A. Pagani, S. Archontoulis |
42. Field Test of a Satellite-Based Model for Irrigation Scheduling in CottonCotton irrigation in Israel began in the mid-1950s. It is based on an irrigation protocol developed over dozens of years of cotton farming in Israel, and proved to provide among the world's best cotton yield results. In this experiment, we examined the use of an irrigation recommendation system that is based on satellite imagery and hyper-local meteorological data, "Manna treatment", compared to the common irrigation protocols in Israel, which use a crop coefficient (Kc) table a... O. Beeri, S. May-tal, J. Raz, R. Rud |
43. Variable Selection and Data Clustering Methods for Agricultural Management Zones DelineationDelineation of agricultural management zones (MZs) is the delimitation, within a field, of a number of sub-areas with high internal similarity in the topographic, soil and/or crop characteristics. This approach can contribute significantly to enable precision agriculture (PA) benefits for a larger number of producers, mainly due to the possibility of reducing costs related to the field management. Two fundamental tasks for the delineation of MZs are the variable selection and the cluster anal... A. Gavioli, E.G. Souza, C.L. Bazzi, N.M. Betzek, K. Schenatto |
44. Field Grown Apple Nursery Tree Plant Counting Based on Small UAS Imagery Derived Elevation MapsIn recent years, growers in the state are transitioning to new high yielding, pest and disease resistant cultivars. Such transition has created high demand for new tree fruit cultivars. Nursery growers have committed their incoming production of the next few years to meet such high demands. Though an opportunity, tree fruit nursery growers must grow and keep the pre-sold quantity of plants to supply the amount promised to the customers. Moreover, to keep the production economical amidst risin... M. Martello, J.J. Quirós, L. Khot |
45. Optimising Nitrogen Use in Cereal Crops Using Site-Specific Management Classes and Crop Reflectance SensorsThe relative cost of Nitrogen (N) fertilisers in a cropping input budget, the 33% Nitrogen use efficiency (NUE) seen in global cereal grain production and the potential environmental costs of over-application are leading to changes in the application rates and timing of N fertiliser. Precision agriculture (PA) provides tools for producers to achieve greater synchrony between N supply and crop N demand. To help achieve these goals this research has explored the use of management classes derive... B. Whelan, M. Fajardo |
46. AgronomoBot: A Smart Answering Chatbot Applied to Agricultural Sensor NetworksMobile devices advanced adoption has fostered the creation of various messaging applications providing convenience and practicality in general communication. In this sense, new technologies arise bringing automatic, continuous and intelligent features for communication through messaging applications by using web robots, also called Chatbots. Those are computer programs that simulate a real conversation between humans to answer questions or do tasks, giving the impression that the person is ta... G.M. Mostaço, L.B. Campos, C.E. Cugnasca, I.R. Souza |
47. Improving the Precision of Maize Nitrogen Management Using Crop Growth Model in Northeast ChinaThe objective of this project was to evaluate the ability of the CERES-Maize crop growth model to simulate grain yield response to plant density and N rate for two soil types in Northeast China, with the long-term goal of using the model to identify the optimum plant density and N fertilizer rate forspecific site-years. Nitrogen experiments with six N rates, three plant densities and two soil types were conducted from 2015 to 2017 in Lishu county, Jilin Province in Northeast China. The CERES-... X. Wang, Y. Miao, W.D. Batchelor, R. Dong, D.J. Mulla |
48. Spatial Decision Support System: Controlled Tile Drainage – Calculate Your BenefitsClimate projection studies suggest that extreme heat waves and floods will become more frequent, affecting future crop yields by 20%-30%, globally. Managing vulnerability and risk begins at the farm level where best management practices can reduce the impacts associated with extreme weather events. A practice that can assist in mitigating the impact of some extreme events is controlled tile drainage (CTD). With CTD, producers use water flow control structures to manage the drainage of water f... A. Kross, G. Kaur, D. Callegari, D. Lapen, M. Sunohara, H. Mcnairn, H. Rudy, L. Van vliet |
49. Precision Irrigation Management Through Conjunctive Use of Treated Wastewater and Groundwater in OmanAgriculture under arid environment is always become a challenge due to water scarcity and salinity problems. With average rainfall of 100 mm, agriculture in Oman is limited due to the arid climate and limited arable lands. More than 50 percent of the arable lands are located in the 300 km northern coastal belt of Al-Batinah region. In addition, country is facing severe problem of sea water intrusion into the groundwater aquifers due to undisciplined excessive groundwater (GW) abstractio... H. Jayasuriya, A. Al-busaidi, M. Ahmed |
50. Overview and Value of Digital Technologies for North American Soybean ProducersIn the current state of digital agriculture, many digital technologies and services are offered to assist North American soybean producers. Opportunities for capturing and analyzing information related to soybean production methods are made available through the adoption of these technologies. However, often it is difficult for producers to know which digital tools and services are available to them or understand the value they can provide. The objective of th... J. Lee, J. Fulton, K. Port, R. Colley iii |
51. Development of an Online Decision-Support Infrastructure for Optimized Fertilizer ManagementDetermination of an optimum fertilizer application rate involves various influential factors, such as past management, soil characteristics, weather, commodity prices, cost of input materials and risk preference. Spatial and temporal variations in these factors constitute sources of uncertainties in selecting the most profitableapplication rate. Therefore, a decision support system (DSS) that could help to minimize production risks in the context of uncertain crop performance is needed. ... S. Shinde, V. Adamchuk, R. Lacroix, N. Tremblay, Y. Bouroubi |