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Spatial Variability in Crop, Soil and Natural Resources
Global Proliferation of Precision Agriculture and its Applications
Modelling and Geo-Statistics
Decision Support Systems
Precision Horticulture
Workshop
ISPA Community: Nitrogen
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Authors
Abbas, F
Abdollahi, J.M
Abonyi, J
Adamchuk, V
Adamchuk, V.I
Adamchuk, V.I
Ahmad, H.N
Ahmed, M
Ahuja, L.R
Al-Busaidi, A
Ali, A
Allegro, G
Alomran, A.M
Alsheri, S.A
Alwabel, M.I
Amaral, L.R
Andales, A.A
Archontoulis, S
Arzani, H.P
Azimi, M.S
Baghernejad, M
Balakrishnan, P
Balakrishnan, P
Batbayar, B
Batchelor, W.D
Bazzi, C.L
Bazzi, C.L
Bazzi, C.L
Bean, G.M
Beeri, O
Benites, V.D
Benő, A
Bereuter, A
Bernardi, A.C
Betzek, N.M
Biswas, A
Blackmer, T.M
Borhani, M.M
Bosompem, M
Bouroubi, Y
Boyko, Y.I
Brinkhoff, J
Callegari, D
Camberato, J.J
Campos, L.B
Carter, P.R
Castrignanò, A
Chang, Y.K
Chen, L
Colley III, R
Corá, J
Cugnasca, C.E
Cui, B
Daroub, S.H
Denton, A.M
Diaz, O.A
Dong, R
Emadi, M
Erdenee, B
Esau, K
Esau, T.J
Esau, T.J
Fajardo, M
Farahpour, M.D
Farooque, A
Farooque, A.A
Farooque, A.A
Farooque, A.A
Farooque, A.A
Ferguson, R.B
Ferguson, R.B
Fernández, F.G
Filippetti, I
Francis, D
Franzen, D.W
Franzen, D.W
Franzen, D.W
Franzen, D.W
Fulton, J
G.M. Florax, R.J
Gavioli, A
Ghosheh, H
Gianello, E
Gobezie, T.B
Gowda, H.H
Griffin, T
Griffin, T
Griffin, T.W
Groulx, D
Guangwei, W
Hansen, N.C
Hendrickson, L
Huang, W
Inamassu, R.Y
Irmak, S
Jayasuriya, H
Jianjun, D
Jin, V
Johnson, R.M
Kaboli, S.D
Kanannavar, P
Kanannavar, P
Kaur, G
Khan, F
Khan, F.S
Khan, F.S
Khot, L
Kirkpatrick, T
Kitchen, N.R
Kocsis, M
Kross, A
Kumar, R
Kwarteng, J.A
Kyveryga, P.M
L, R.N
Laboski, C.A
Lacroix, R
Lamb, D.W
Lapen, D
Lee, J
Li, D
Liakos, V
Liang, X
Liping, C
Liu, Z
Lowenberg-DeBoer, J
Lowenberg-DeBoer, J
Ma, L
Mackenzie, C
Mackenzie, C
Madani, A
Madani, A
Magalhaes, P.G
Martelli, R
Martello, M
May-tal, S
Mazzoleni, R
McLendon, A
McNairn, H
Miao, Y
Miao, Y
Michelon, G.K
Mijatovic, B
Mirdavodi, H.M
Monfort, S
Mosmen, E.W
Mostaço, G.M
Mueller, T
Mueller, T
Mulla, D.J
Nafziger, E.D
Nielsen, D.C
Ntifo-Siaw, E
Pagani, A
Pantoja, J.L
Pastore, C
Patil, M
Patil, M
Patil, M.B
Percival, D
Percival, D.C
Perry, C
Pezzi, F
Port, K
Porter, W
Pujari, B
Pujari, B
Puntel, L
Quirós, J.J
Rabello, L.M
Rahman, M.M
Ransom, C.J
Raz, J
Reddy, K.A
Reeg, P.R
Rienzi, E
Rienzi, E
Robson, A
Rodrigues, M
Rodrigues, M
Rojo, F
Rud, R
Rudnick, D
Rudy, H
Söderström, M
Saleem, S.R
Sanches, G.M
Saseendran, S.A
Saurette, D
Sawyer, J.E
Schenatto, K
Schenatto, K
Schmer, M
Schneider, D.A
Schumann, A
Schumann, A
Schumann, A.W
Schumann, A.W
Shanahan, J.F
Shanwad, U
Shaver, T
Shinde, S
Sisák, I
Slaeem, S
Song, X
Souza, E.G
Souza, I.R
Stanley, J.N
Sunohara, M
Szabó, K
TORGBOR, B.A
Tateishi, R
Tremblay, N
Trout, T.J
Tucker, M
Upadhyaya, S
Upadhyaya, S
Upadhyaya, S
Valente, I.Q
Valentini, G
Van Donk, S
Vellidis, G
Viator, R.P
Wang, X
Ward, N
Weiqiang, F
Whelan, B
Wienhold, B
Xu, J.X
Zaman, Q
Zaman, Q.U
Zaman, Q.U
Zaman, Q.U
Zhao, C
Zhijun, M
de Menezes, P.L
de Oliveira, R.P
van Vliet, L
Topics
Spatial Variability in Crop, Soil and Natural Resources
Precision Horticulture
Decision Support Systems
Modelling and Geo-Statistics
Global Proliferation of Precision Agriculture and its Applications
ISPA Community: Nitrogen
Type
Poster
Oral
Year
2012
2022
2018
2008
2010
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Topics

Filter results52 paper(s) found.

1. Precision Weed Management Research Advancement In The Near East

  Precision weed control research received considerable attention since the introduction of global positioning systems (GPS). GPS and geographic information systems (GIS) technologies may assist with field monitoring, particularly; in deciding what weed species to monitor? What weed densities are bypassing critical thresholds? and where?  While advancements in precision agricultural research could be detected through the intensive publications in the developed world,... H. Ghosheh

2. Road Map For Precision Agriculture In The Punjab, North-west India

Agricultural experimentation is both expensive and time consuming. It is necessary to reduce site-specific research and capitalize on the agricultural experience gained elsewhere by using soil maps and GIS-GPS (Geographic Information System - Global Positioning System) technology. Since in an agro-eco-subregion, soils in the same family require essentially the same management practices, maximum production results obtained in one soil family can be used as production targets for all soils belo... R. Kumar

3. Precision Agriculture In New Zealand’s Farming Systems

  To date New Zealand farmers do not realize how involved they are in Precision Agriculture (PA). As arable farmers we know how many kilograms of nitrogen (N) it takes to grow a tonne of wheat, how many kilograms of seed we can produce for every millimetre of water that is applied (through irrigation and/or rainfall) and yet we don’t believe we are involved in PA. As dairy farmers we are matching feed requirements to the specific production level of individual cows. We ar... C. Mackenzie, C. Mackenzie

4. Worldwide Adoption Of Precision Agriculture Technology: The 2010 Update

Precision agriculture technology has been on the market for nearly two decades; and the question remains regarding how and to what extent farmers are making the best use of the technology. Yield monitors, GPS-enabled guidance technology, farm-level mapping and GIS software, on-the-go variable rate applications, and other spatial technologies are being used by thousands of farmers worldwide. The USDA Agricultural Resource Management Survey (ARMS) and the annual CropLife/Purdue University Preci... T. Griffin, J. Lowenberg-deboer

5. Land Information System Of Precision Farming In Mongolia Using Remote Sensing And Geographical Information System

    Remote sensing (RS) and geographic information system (GIS) technologies have been of great use to planners in planning for efficient use of natural resources at national, sub region and rural levels.   RS can be used for precision farming in a number of ways for providing input supplies and variability management through decision support system.   GIS is the principal technology used to integrate spatial data... B. Erdenee, B. Batbayar, R. Tateishi

6. Is Precision Agriculture Feasible In Cocoa Production In Ghana? : The Case Of “Cocoa High Technology Programme” In The Eastern Region Of Ghana

  Ghana is the second largest producer of cocoa in the world supplying 25% of the world’s cocoa, thus cocoa production contributes significantly to the economy of ... M. Bosompem, J.A. Kwarteng, E. Ntifo-siaw

7. Local And Regional Soil Clay Mapping Using Gamma Ray Spectrometry

... M. Söderström

8. Impact Of Precision Leveling On Spatial Variability Of Moisture Conservation In Arid Zones Of Karnataka

... S. Upadhyaya, P. Balakrishnan, B. Pujari, M. Patil, P. Kanannavar

9. Laser Leveling Holds a Lot Of Promise in Water Conservation and Saving in Dry Zones (Drought Prone Areas) of Karnataka

... S. Upadhyaya, P. Balakrishnan, B. Pujari, M. Patil, P. Kanannavar

10. Long Term Effects of Irrigation with Sewage Effluent on Some Soil Properties

In the arid and semiarid regions, the use of treated sewage water increases as an alternative for non-renewable resources in irrigation. The objective of this research is to identify the effect of irrigation with sewage effluent and well water for lo... M.I. Alwabel, S.A. Alsheri, A.M. Alomran

11. Application of RS, GPS & GIS in a National Monitoring System for Accurate Range Assessment

Sustainable use of rangelands requires information on vegetation cover and its changes through time, condition trend and the effect of climate as well as management practices. The main objective of this research was showing variation of vegetation para... H.P. Arzani, M.S. Azimi, S.D. kaboli, H.M. mirdavodi, M.M. Borhani, J.M. Abdollahi, M.D. farahpour

12. Natural Resources Management through Frontier Technologies - A Case Study from India

The social and economic development of the state is interlaced with our natural resources, and the manner in which they are managed and exploited.  The unplanned development and overexploitation of resources are exerting various... H.H. Gowda, K.A. Reddy, M.B. Patil, R.N. L, U. Shanwad

13. Spatial Variability Index Based On Soil Properties for Notill and Pasture Site-Specific Management in Brazil.

 Quantitative characterization of soil properties spatial variation has first been appl... R.P. De oliveira, A.C. Bernardi, V.D. Benites, L.M. Rabello, R.Y. Inamassu

14. Evaluation of Application Effect of the Laser Land Leveling Technology in Typical Areas of China

The technology of laser land leveling can improve the accuracy of land leveling and it is the important measure of improving irrigation efficiency and facilitating more uniform distribution of irrigation water. The technology is more widely used in China ... W. Guangwei, M. Zhijun, C. Liping, F. Weiqiang, D. Jianjun

15. Winter Wheat Growth Uniformity Monitoring Through Remote Sensed Images

  ... X. Song, C. Zhao, L. Chen, W. Huang, B. Cui

16. Soil Spatial Variability in the Everglades Agricultural Area in South Florida

The Everglades agricultural area is composed by histosols laying on hard limestone bedrock in south Florida. Despite the common assumption of homogeneity of these soils, agricultural practices could result in the increase of soil variability. Therefore, soil spatial variability was studied on three fields (5.5 ha each) at the Everglades Research and Education Center to compare the c... J.L. Pantoja, S.H. Daroub, O.A. Diaz

17. Spatial Econometric Approaches to Develop Site-Specific Nematode Management Strategies in Cotton Production

Root-knot nematode infestations tend to be spatially clustered within agricultural... Z. Liu, T. Griffin, T. Kirkpatrick, S. Monfort

18. Precision Tools to Evaluate Benefits of Tile Drainage in a Corn and Soybean Rotation in Iowa

... P.R. Reeg, T.M. Blackmer, P.M. Kyveryga

19. Analysis of Spatial Variability of Key Soil Attributes In North-Central Ukraine

As Ukrainian agricultural production undergoes major changes, a better understanding of the diversity of land resources is needed to optimize management.  Dealing with large fields (over 100 ha in size) with non-uniform growing conditions presents an opportunity for site-specific management of agricultural inputs. This publication describes our 2010 pilot study on the implementation of integrated mapping of apparent soil electrical conductivity and field topography to guide soil sampling... Y.I. Boyko, V.I. Adamchuk

20. Relationship of Soil Properties to Apparent Ground Conductivity in Wild Blueberry Fields

  One of the fundamental deficiencies in high value crops is the lack of detailed, up-to-date and pertinent geo-referenced soil information for site-specific crop management to improve productivity. This experiment was designed to estimate and map soil properties rapidly and reliably using an electromagnetic induction (EMI) method. Two wild bl... F.S. Khan, Q.U. Zaman, A.W. Schumann, A. Madani, D.C. Percival, A.A. Farooque, S.R. Saleem, F.S. Khan

21. Spatial Variability of Sugarcane Yields in Relation to Soil Salinity in Louisiana

High soil salinity levels have been documented to negatively impact sugarcane yields.  Tests were conducted in commercial sugarcane fields in South Louisiana in 2009-2010 to determine if elevated soil salinity ... R.P. Viator, R.M. Johnson

22. Landscape Influences on Soil Nitrogen Supply and Water Holding Capacity for Irrigated Corn

... T. Shaver, M. Schmer, S. Irmak, S. Van donk, B. Wienhold, V. Jin, A. Bereuter, D. Francis, D. Rudnick, N. Ward, L. Hendrickson, R. Ferguson, V.I. Adamchuk

23. Impact of Variable Rate Fertilization on Nutrients Losses in Surface Runoff for Wild Blueberry Fields

Wild blueberry producers apply agrochemicals uniformly without considering substantial variation in soil properties, topographic features that may affect fruit yield within field. A wild blueberry field was selected to evaluate the impact of variable rate (VR) fertilization on nutrient losses in surface runoff from steep slope to low lying areas to improve cr... S. Slaeem, Q.U. Zaman, A. Madani, A. Schumann, D. Percival, H.N. Ahmad, A.A. Farooque, F. Khan

24. Sensor Fusion on a Wild Blueberry Harvester for Fruit Yield, Plant Height and Topographic Features Mapping to Improve Crop Productivity

  Site-specific crop management can improve profitability and environmental risks of wild blueberry crop having large spatial variation in soil/plant characteristics, topographic features which may affect fruit yield. An integrated automated sensor fusion system including an ultrasonic sensor, a digital color camera, a slope sens... A.A. Farooque, Q.U. Zaman, D. Groulx, A.W. Schumann, T.J. Esau, Y.K. Chang

25. Spatial Apparent Electrical Conductivity (ECa), Soil Moisture and Water Use Efficiency in Vertosol Soils

Producing high resolution maps of water use efficiency (crop yield per unit of water consumption; WUE) for precision crop management is limited by our ability to readily produce maps of soil moi... J.N. Stanley, D.A. Schneider, D.W. Lamb

26. Spatial and Temporal Variability of Corn Grain Yield as a Function of Soil Parameters, and Climate Factors

Effective site-specific management requires an understanding the influence of soil and weather on yield variability. Our objective was to examine the influence of soil, precipitation, and temperature on spatial and temporal corn grain yield variability.  The study site (10 by 250 -m in size) was located in Jaboticabal, São Paulo State, on a Rhodic Hapludox. Corn yield (planted with 0.9-m spacing) was measure... T. Mueller, J. Corá, A. Castrignanò, M. Rodrigues, E. Rienzi

27. On-The-Go pH Sensor: An Evaluation in a Kentucky Field

A commercially available on-the-go soil pH sensor measures and maps subsurface soil pH at high spatial intensities across managed landscapes.  The overall purpose of this project was to evaluate the potential for this sensor to be used in agricultural fields. The specific goals were to determine and evaluate 1) the accuracy with which this instrument can be calibrated, 2) the geospatial structure of soil pH measure... T. Mueller, E. Gianello, B. Mijatovic, E. Rienzi, M. Rodrigues

28. Measurement of Systematic Errors in Crop Prediction

Precision agriculture typically attempts to answer grower questions using an increasingly more fine-grained analysis.  However, some entities, such as cooperatives, can have an interest in answers that are spatially course-grained, such as obtaining an estimate of the overall crop production within a season.  Errors in factors that most influence fine-grained predictions, such as soil quality, may have a smaller impact on overall yield forecasts since their effect is likely to ... A.M. Denton, E.W. Mosmen, J.X. Xu

29. Evaluating Spatial Effects Induced by Alternative On- Farm Trial Experimental Designs with Cross-regressive Variables Using Monte Carlo Methods

The goal of this research was to adapt spatial regression methods to on-farm trials in a farm management context. Different experimental designs and statistical analysis methods are tested with site-specific data under a range of spatial autocorrelation levels using Monte Carlo simulation techniques. Simulations indicated that data usable for farm management decision making could be gathered from limited replication experimental designs if that data were analyzed with the appropriate spatial ... T.W. Griffin, R.J. G.m. florax, J. Lowenberg-deboer

30. A New Approach for Quantitative Land Suitability Evaluation Using Geostatistics, Remote Sensing (Rs) and Geographic Information System (Gis)

The objective of this study was to incorporate geostatistics, remote sensing and geographic information system methods due to improving the quantitative land suitability assessment in Arsanjan plain, southern Iran. The primary data was collected from 85 soil samples from tree depths (0­30, 30­60 and 60­90 cm) and the secondary information from remotely sensed data “LISS­III receiver from IRS­P6 satellite”. In order to identify the spatial dependence of soil imp... M. Baghernejad, M. Emadi

31. Use of a Cropping System Model for Soil-specific Optimization of Limited Water

In the arena of modern agriculture, system models capable of simulating the complex interactions of all the relevant processes in the soil-water-plant- atmosphere continuum are widely accepted as potential tools for decision support to optimize crop inputs of water to achieve location specific yield potential while minimizing environmental (soil and water resources) impacts. In a recent study, we calibrated, validated, and applied the CERES-Maize v4.0 model for simulating limited-water irriga... L.R. Ahuja, S.A. Saseendran, L. Ma, D.C. Nielsen, T.J. Trout, A.A. Andales, N.C. Hansen

32. Effective Use of a Debris Cleaning Brush for Mechanical Wild Blueberry Harvesting

Wild 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

33. 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

34. Reverse Modelling of Yield-Influencing Soil Variables in Case of Few Soil Data

Our 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

35. Optimized Soil Sampling Location in Management Zones Based on Apparent Electrical Conductivity and Landscape Attributes

One 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

36. Optimal Placement of Proximal Sensors for Precision Irrigation in Tree Crops

In 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

37. Prediction of Corn Economic Optimum Nitrogen Rate in Argentina

Static (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

38. Field Test of a Satellite-Based Model for Irrigation Scheduling in Cotton

Cotton 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

39. Variable Selection and Data Clustering Methods for Agricultural Management Zones Delineation

Delineation 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

40. Field Grown Apple Nursery Tree Plant Counting Based on Small UAS Imagery Derived Elevation Maps

In 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

41. Optimising Nitrogen Use in Cereal Crops Using Site-Specific Management Classes and Crop Reflectance Sensors

The 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

42. AgronomoBot: A Smart Answering Chatbot Applied to Agricultural Sensor Networks

Mobile 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

43. Improving the Precision of Maize Nitrogen Management Using Crop Growth Model in Northeast China

The 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

44. Spatial Decision Support System: Controlled Tile Drainage – Calculate Your Benefits

Climate 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

45. Precision Irrigation Management Through Conjunctive Use of Treated Wastewater and Groundwater in Oman

Agriculture 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

46. Overview and Value of Digital Technologies for North American Soybean Producers

In 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

47. Development of an Online Decision-Support Infrastructure for Optimized Fertilizer Management

Determination 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

48. Temperature Effect on Wild Blueberry Fruit Quality During Mechanical Harvest

Mechanical harvesters, utilizing a range of technologies, have been developed for timely operations and remain the most cost-effective means of picking the wild blueberry crop. Approximately 95% of wild blueberries in Atlantic Canada are immediately frozen and processed, while only a small percentage is sold in the fresh market. However, the producers can benefit by increasing the value of their harvested crop through fresh market sales. The objective of this study was to determine the optimu... T.J. Esau, A.A. Farooque, F. Abbas

49. Variable Rate Fertilization in a High-yielding Vineyard of Cv. Trebbiano Romagnolo May Reduce Nitrogen Application and Vigour Variability Without Loss of Crop Load

The site-specific management of vineyard cultural practices may reduce the spatial variability of vine vigor, contributing to achieve the desired yield and grape composition. In this framework, variable rate fertilization may effectively contribute to reduce the different availability of mineral nutrients between different areas of the vineyard, and so achieving the vine’s aforementioned performances. The present study was aimed to apply a variable rate fertilization in a high... G. Allegro, R. Martelli, G. Valentini, C. Pastore, R. Mazzoleni, F. Pezzi, I. Filippetti, A. Ali

50. Developing a Machine Learning and Proximal Sensing-based In-season Site-specific Nitrogen Management Strategy for Corn in the US Midwest

Effective in-season site-specific nitrogen (N) management strategies are urgently needed to ensure both food security and sustainable agricultural development. Different active canopy sensor-based precision N management strategies have been developed and evaluated in different parts of the world. Recent studies evaluating several sensor-based N recommendation algorithms across the US Midwest indicated that these locally developed algorithms generally did not perform well when used broadly acr... D. Li, Y. Miao, .G. Fernández, N.R. Kitchen, C. . Ransom, G.M. Bean, .E. Sawyer, J.J. Camberato, .R. Carter, R.B. Ferguson, D.W. Franzen, D.W. Franzen, D.W. Franzen, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.F. Shanahan

51. Digital Soil Sensing and Mapping for Crop Suitability

Soil, central to any land-based production system, determines the success of any crops. While soil for a farm or field is fixed, the crops can be selected to best fit the soil’s capability and production. Traditionally crops are selected based on farm history, knowledge, and years of trial and error to tailor the right crop to the right soil. Inherent challenges associated with this make the whole process unsustainable. Due to the consistent nature of the information collected, soil sen... D. Saurette, A. Biswas, T.B. Gobezie

52. Assessing the Potential of Sentinel-1 in Retrieving Mango Phenology and Investigating Its Relation to Weather in Southern Ghana

The rise in global production of horticultural tree crops over the past few decades is driving technology-based innovation and research to promote productivity and efficiency. Although mango production is on the rise, application of the remote sensing technology is generally limited and the available study on retrieving mango phenology stages specifically, was focused on the application of optical data. We therefore sought to answer the questions; (1) can key phenology stages of mango be retr... B.A. Torgbor, M.M. Rahman, A. Robson, J. Brinkhoff