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Spatial Variability in Crop, Soil and Natural Resources
Precision A to Z for Practitioners
Precision Agriculture and Global Food Security
Smart Weather for Precision Agriculture
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Authors
Adamchuk, V.I
Akune, V.S
Al-Gaadi, K
Alarcon, V.J
Amado, T.J
Amado, T.J
Anselmi, A.A
Arnall, B
Arnall, D.B
Ayral, J
Baklouti, I
Balkcom, K
Bazzi, C.L
Benez, S.H
Berry, P
Betzek, N.M
Bisognin, M.B
Bobryk, C.W
Bodnár, K.B
Bonomi, A
Bridges, R.W
Bruggeman, S
Canata, T.F
Cardoso, T.F
Chagas, M.F
Citon, L.C
Clay, D.E
Clay, S
Colaço, A
Colaço, A.F
Cong, Y
Cong, Y
Conley, S
Conway, L
Corassa, G.M
Corassa, G.M
Cox, D
Dalla Nora, D
Dempsey, D
Destain, M
Dong, J
Dong, J
Dr., N
Duft, D.G
Eitelwein, M.T
Ferguson, R.B
Franco, H.C
Franzen, D.W
Fu, W
Fu, W
Fulton, J.P
Gao, N
Gavioli, A
Gaviraghi, R
Gombos, B
Griffin, T
Griffin, T
Grisham, M.P
Hama Rash, S
Hamida, A
Hanumanthappa, D
Hassaballa, A.A
Herrmann, I
Horbe, T.D
Johnson, R.M
Jones, B
Joshi, D
K, S
Kayad, A.G
Khakbazan, M
Kholikulov, S
Kindred, D
Kiran, A
Kitchen, N
Kitchen, N
Knapp, M
Knappenberger, T
Krol, C
Kulesza, S.E
Li, Y
Lu, J
Luciano, A.C
Ma, Y
Mackenzie, C
Madugundu, R
Magalhães, P.S
Maggi, M.F
Maharjan, B
Maldaner, L
Mansouri, M
Mayer, W
McBeath, T
McDonald, T.P
Meng, Z
Meng, Z
Miao, Y
Miller, J
Molin, J.P
Molin, J.P
Molin, J.P
Moulin, A
Mueller, T
Murdoch, A.J
Myers, B
Nagy, J
Nakazawa, P.H
Ortiz, B
Pan, L
Pan, R
Pardaev, S
Penn, C
Phillips, S
Piikki, K
Pires, J.L
Poncet, A.M
Ravindran, P
Redmond, C
Reimche, G.B
Rutter, B
Sanches, G.M
Sanderson, J
Santana Neto, A.J
Santi, A.L
Sassenrath, G.F
Schelling, K
Schenatto, K
Schulthess, U
Schwalbert, R.A
Schwiesow, D
Shang, Y
Shaw, J
Shoup, D
Singh, A
Song, X
Souza, E.G
Souza, W.J
Stelford, M
Sudduth, K
Sylvester-Bradley, R
Söderström, M
T, S
Tabaldi, F.M
Thies, S
Tola, E
Townsend, P
Trevisan, R
Trevisan, R.G
Vosberg, S
Walsh, M
Wang, H
Wang, R
Warren, J
Watkins, P
Webber, H
Wilhelm, N
Wiseman, L
Yang, C
Yang, G
Yang, Q
Yogananda, S
Yost, M
Yost, M
Zhang, A
eitelwein, M.T
giriyappa, M
Topics
Spatial Variability in Crop, Soil and Natural Resources
Precision A to Z for Practitioners
Precision Agriculture and Global Food Security
Smart Weather for Precision Agriculture
Type
Poster
Oral
Year
2016
2012
2018
Home » Topics » Results

Topics

Filter results48 paper(s) found.

1. Beyond NDVI - Additional Benefits of RapidEye Image Products

... U. Schulthess, K. Schelling

2. The Map - Supported by New NPK-Sensors - is Intelligent, Not the Tractor

DI Walter H. Mayer   PROGIS Software GmbH   Postgasse 6, A-9500 Villach www.progis.com office@progis.com +43 4242 26332 WinGIS®-AGROffice® and BING®-maps: Since years PROGIS has been developing an object oriented GIS (WinGIS®), agriculture and forestry applications for single enterprises, for advisors, for the chain management including logistics and communication implementation with mobile GIS (mobG... W. Mayer

3. An Approach to Selection of Soil Water Content Monitoring Locations within Fields

Increased input efficiency is one of the main challenges for a modern agricultural enterprise. One way to optimize production cycles is to rationalize crop residue utilization. In conditions where there is limited use of mineral fertilizers and without applying manure, plant residues may be used as an organic fertilizer ... V.I. Adamchuk, L. Pan, R.B. Ferguson

4. The Use of Crop Sensors Beyond Nitrogen and Improving the Right to Farm

... C. Mackenzie

5. John Deere FarmSight™

Agriculture has had several revolutions in the past century, and it currently faces what may be its greatest challenge to date – population growth and the increased need for food, fiber, and fuel in the future.  To meet this challenge the agricultural industry will have to drive efficiencies to a level never seen before, within a context of several macro trends (e.g., farm sizes increasing, environmental sustainability requirements evolving).  John Deere FarmSightTM... M. Stelford

6. AMMO Ag: Agricultural Marketing & Merchandising Optimizer

EHedger provides an integrated risk management solution for farm operations utilizing our proprietary AMMO platform combined with proven hedging strategies, first-hand market insight, effective trade execution and farming expertise. AMMO software enables real-time analysis of crop/livestock production. Farmers can set profit margins, evaluate variable profit scenarios, understand production costs and risks, and create sustainable marketing programs to maximize their... C. Krol, D. Dempsey

7. Real-Time Fluorescence Sensors for Precision Agriculture

... J. Ayral

8. Raven Sponsor Presentation: Slingshot Overview

Slingshot, a suite of products and services centered around high-speed wireless connectivity in the cab ... D. Schwiesow

9. Precision Agriculture and Springer

Maryse Walsh will be presenting Precision Agriculture, the Springer journal, but also the discipline and its place in the Springer publications overall. The community attending the ICPA has a major role in ensuring the positive development of these publications and the affiliation of the journal to the ISPA will only help. ... M. Walsh

10. Raising Awareness of the Potential of Crop Sensing Technologies to Improve Environmental Stewardship

Extensive research and on-farm work using active crop sensors for input management have been conducted in the Midwest and Great Plain USA with favorable results. Contrasting is the situation in the Southeast where the adoption by farmers is still limited and current on-going research is focused on the main southeastern crops. This presentation will provide an overview of the multiple extension activities related to crop sensing involving farmers, extension agents and crop consultants in ... B. Ortiz

11. Making the Most of Precision Ag Data: Big Data in Farm Management

na ... T. Griffin

12. Davco's Journey Into Precision Sugarcane Farming

Davco's Journey Into Precision Sugarcane Farming ... D. Cox

13. Sensor Algorithms 101

This presentation will break down the algorithms used for Optical Sensor Based Nitrogen rate recommendations. The group will walk through the mechanics and agronomics behind the most commonly used equations, in order to turn the black boxes into slightly muddied waters. ... B. Arnall

14. Use of Zone or Grid Soil Nutrient Management as Part of an Integrated Site-specific Nutrient Strategy

Zone and grid sampling are used as a basis for fertilizing with nutrients site-specifically. Use of sensors to assist in-season management of nitrogen is also gaining momentum. The presentation will suggest when grid or zone sampling for preplant nutrients might be utilized and how these recommendations would be used in an integrated approach of preplant plus in-season nutrient management. ... D. Franzen

15. Statistical Variability of Crop Yield, Soil Test N and P Within and Between Producer’s Fields

Soil test N and P significantly affect crop production in the Canadian Prairies, but vary considerably within and between producer's fields.  This study describes the variability of crop yield, soil test N and P within and between producer's fields in the context of variable fertilizer rates.  Yield, terrain attribute, soil test N and P data were collected for 10 fields in Alberta, Saskatchewan and Manitoba Canada in 2014 and 2015.  The influence of ... A. Moulin, M. Khakbazan

16. Understanding Complex Soil Variability: the Application of Archaeological Knowledge to Precision Agriculture Systems in the UK.

As higher resolution datasets have become more available and more accessible within commercial agriculture, there has been an increasing expectation that more data will bring more answers to questions surrounding soil, crop and yield variability. When this does not happen, trust and confidence in data can be lost, affecting the uptake and use of precision agriculture. This research presents a novel approach for understanding complex soil variability at a variety of different scales.... H. Webber

17. Estimating Environmental Systems Using Iterated Sigma Point Techniques: a Biomass Substrate Hypothetical System

This paper addresses the problem of biomass substrate hypothetical system estimation using sigma points kalman filter (SPKF) methods. Various conventional and state-of-theart state estimation methods are compared for the estimation performance, namely the unscented Kalman filter(UKF), the central difference Kalman filter (CDKF), the square-root unscented Kalman filter (SRUKF), the square-root central difference Kalman filter (SRCDKF), the iterated unscented Kalman filter (IUKF), the iterated ... I. Baklouti, M. Mansouri, M. Destain, A. Hamida

18. Spatial and Temporal Variation of Soil Nitrogen Within Winter Wheat Growth Season

This study aims to explore the spatial and temporal variation characteristics of soil ammonium nitrogen and nitrate nitrogen within winter wheat growth season. A nitrogen-rich strip fertilizer experiment with eight different treatments was conducted in 2014. Soil nitrogen samples of 20-30cm depth near wheat root were collected by in-situ Macro Rhizon soil solution collector then soil ammonium nitrogen and nitrate nitrogen content determined by SEAL AutoAnalyzer3 instrument. Classical statisti... X. Song, G. Yang, Y. Ma, R. Wang, C. Yang

19. Rectification of Management Zones Considering Moda and Median As a Criterion for Reclassification of Pixels

Management zones (MZ) make economically viable the application of precision agriculture techniques by dividing the production areas according to the homogeneity of its productive characteristics. The divisions are conducted through empirical techniques or cluster analysis, and, in some cases, the MZ are difficult to be delimited due to isolated cells or patches within sub-regions. The objective of this study was to apply computational techniques that provide smoothing of MZ, so as to become v... N.M. Betzek, E.G. Souza, C.L. Bazzi, K. Schenatto, A. Gavioli, M.F. Maggi

20. Positioning Strategy of Maize Hybrids Adjusting Plant Population by Management Zones

Choice of hybrid and accurate amount of plants per area determines grain yield and consequently net incomes. Local field adjustment in plant population is a strategy to manage spatial variability and optimize environmental resources that are not under farmer control (like soil type and water availability). This study aims to evaluate the response of hybrids by levels of plant population across management zones (MZ). Six different hybrids and five rates of plant populations were analyzed start... A.A. Anselmi, J.P. Molin, M.T. Eitelwein, R. Trevisan, A. Colaço

21. Should One Phosphorus Extraction Method Be Used for VRT Phosphorus Recommendation in the Southern Great Plains?

Winter Wheat has been produced throughout the southern Great Plains for over 100 years.  In most cases this continuous production of mono-culture lower value wheat crop has led to the neglect of the soils, one such soil property is soil pH. In an area dominated by eroded soils and short term leases, Land-Grant University wheat breeders have created lines of winter wheat which are aluminum tolerant to increase production in low productive soils.  Now the fields in this region can hav... D.B. Arnall, S. Phillips, C. Penn, P. Watkins, B. Rutter, J. Warren

22. Consequences of Spatial Variability in the Field on the Uniformity of Seed Quality in Barley Seed Crops

Spatial variation is known to affect cereal growth and yield but consequences for seed quality are less well-known. Intra-field spatial variation occurs in soil and environmental variables and these are expected to affect the crop. The objective of this paper was to identify the spatial variation in barley seed quality and to investigate its association with environmental factors and the spatial scale over which this correlation occurs. Two uniformly-managed, commercial fields of wi... S. Hama rash, A.J. Murdoch

23. Processing Yield Data from Two or More Combines

Erroneous data affect the quality of yield map. Data from combines working close to each other may differ widely if one of the monitors is not properly calibrated and this difference has to be adjusted before generating the map. The objective of this work was to develop a method to correct the yield data when running two or more combines in which at least one has the monitor not properly calibrated. The passes of each combine were initially identified and three methods to correct yield data w... L. Maldaner, J.P. Molin, T.F. Canata

24. The New Digital Soil Map of Sweden -Derived for Free Use in Precision Agriculture

The Digital Soil Map of Sweden (DSMS) was finalized in 2015. The present paper describes the mapping strategy, the estimated uncertainty of the primary map layers and its potential use in precision agriculture. The DSMS is a geodatabase with information on the topsoil of the arable land in Sweden. The spatial resolution is 50 m × 50 m and it covers > 90% of the arable land of the country (~2.5 million ha). Non-agriculture land and areas with organic soil are excluded. Access to a num... K. Piikki, M. Söderström

25. Shifting Fertiliser Response Zones in a Four Year, Whole-paddock Cereal Cropping Experiment.

Precision agriculture in cropping areas of dryland Australia has focused on managing within production zones. These are ideally stable, possibly soil- and topography-based areas within fields. There are many different ideas on how to delimit and implement zones, and a four year whole-field experiment, with low, medium and high treatment philosophies applied per 9m seeder/harvester width across the entire field, was established to explore how zones might best be established and used. The treat... B. Jones, T. Mcbeath, N. Wilhelm

26. Spatial Variability of Soil Nutrients and Site Specific Nutrient Management in Maize

A field study was conducted during kharif 2014 and rabi 2014-15 at Southern Transition Zone of Karnataka under the jurisdiction of University of Agricultural Sciences, GKVK, Bangalore, India to know the spatial variability for available nutrient content in cultivator’s field and effect of site specific nutrient management in maize. The farmer’s fields have been delineated with each grid size of 50 m x 50 m using geospatial technology. Soil samples from 0-15 cm we... S. T, M. Giriyappa, D. Hanumanthappa, N. Dr., S. K, S. Yogananda, A. Kiran

27. Sources of Information to Delineate Management Zones for Cotton

Cotton in Brazil is an input-intensive crop. Due to its cultivation in large fields, the spatial variability takes an important role in the management actions. Yield maps are a prime information to guide site-specific practices including delineation of management zones (MZ), but its adoption still faces big challenges. Other information such as historical satellite imagery or soil electrical conductivity might help delineating MZ as well as predicting crop performance. The objective of this w... R.G. Trevisan, M.T. Eitelwein, A.F. Colaço, J.P. Molin

28. Measurement of In-field Variability for Active Seeding Depth Applications in Southeastern US

Proper seeding depth control is essential to optimize row-crop planter performance, and adjustment of planter settings to within field spatial variability is required to maximize crop yield potential. The objectives of this study were to characterize planting depth response to varying soil conditions within fields, and to discuss implementation of active seeding depth technologies in Southeastern US. This study was conducted in 2014 and 2015 in central Alabama for non-irrigated maize (Zea may... A.M. Poncet, J.P. Fulton, T.P. Mcdonald, T. Knappenberger, R.W. Bridges, J. Shaw, K. Balkcom

29. Response of Soybean Cultivars According to Management Zones in Southern Brazil

The positioning of soybean cultivars on fields according your environmental response is new strategy to obtain high soybean yields. The aim of this study was to investigate the agronomic response of six soybean cultivars according management zones in Southern Brazil. The study was conducted in 2013/2014 and in two fields located in Boa Vista das Missões, Rio Grande do Sul, Brazil. The experimental design was a randomized complete block in a factorial arrangement (3x6), with three manag... T.J. Amado, A.L. Santi, G.M. Corassa, M.B. Bisognin, R. Gaviraghi, J.L. Pires

30. High-resolution Mapping with On-the-go Soil Sensor and Its Relation with Corn Yield and Soil Acidity in a Dystrophic Red Oxisol

Spatial representations of soil attributes with low resolution can lead to gross errors of recommendation and compromise the efficiency of soil corrections and consequently the grain yield. However, obtaining the spatial variability of soil attributes with high resolution by soil sampling is not recommended because of its large time spent and high cost of laboratory analysis what makes difficult their large-scale application. This way, the on-the-go soil sensing has been used in precision agr... G.M. Corassa, T.J. Amado, R.A. Schwalbert, G.B. reimche, D. Dalla nora, T. . horbe, F.M. tabaldi

31. Spatial Variability and Correlations Between Soil Attributes and Productivity of Green Corn Crop

In Brazil, the progressive development in the cultivation of the corn for consumption in the green stadium stands by the relevant socio-economic role that this related to multiple applications, the attractive market price and continuous demand for the product in nature. Therefore, this study was to analyze the correlations and spatial variability of the productivity of the culture of the green corn in winter, in alluvial soil of the type Cambisols eutrophic in the amount areas and Hydromorphi... W.J. Souza, S.H. Benez, P.H. Nakazawa, A.J. Santana neto, L.C. Citon, V.S. Akune

32. Claypan Depth Effect on Soil Phosphorus and Potassium Dynamics

Understanding the effects of fertilizer addition and crop removal on long-term change in spatially-variable soil test P (STP) and soil test K (STK) is crucial for maximizing the use of grower inputs on claypan soils. Using apparent electrical conductivity (ECa) to estimate topsoil depth (or depth to claypan, DTC) within fields could help capture the variability and guide site-specific applications of P and K. The objective of this study was to determine if DTC derived from ECa... L. Conway, M. Yost, N. Kitchen, K. Sudduth, B. Myers

33. In-field Variability of Terrain and Soils in Southeast Kansas: Challenges for Effective Conservation

A particular challenge for crop production in southeast Kansas is the shallow topsoil, underlain with a dense, unproductive clay layer. Concerns for topsoil loss have shifted production systems to reduced tillage or conservation management practices. However, historical erosion events and continued nutrient and sediment loss still limit the productive capacity of fields. To improve crop production and further adoption of conservation practices, identification of vulnerable areas of fields was... G.F. Sassenrath, T. Mueller, V.J. Alarcon, S.E. Kulesza, D. Shoup

34. Field Potential Soil Variability Index to Identify Precision Agriculture Opportunity

Precision agriculture (PA) technologies used for identifying and managing within-field variability are not widely used despite decades of advancement. Technological innovations in agronomic tools, such as canopy reflectance or electrical conductivity sensors, have created opportunities to achieve a greater understanding of within-field variability. However, many are hesitant to adopt PA because uncertainty exists about field-specific performance or the potential return on investment. These co... C.W. Bobryk, M. Yost, N. Kitchen

35. Assessing the Variability of Red Stripe Disease in Louisiana Sugarcane Using Precision Agriculture Methods

Symptoms of red stripe disease caused by Acidovorax avenae subsp. avenae in Louisiana between 1985 and 2010 were limited to the leaf stripe form which caused no apparent yield loss.  During 2010, the more severe top rot form was observed, and a study was initiated to investigate the distribution of red stripe in the field and determine its effects on cane and sugar yields. Two fields of cultivar HoCP 00-950, one plant-cane (PC) crop and one first-ratoon (FR) crop, affected by top rot wer... R.M. Johnson, M.P. Grisham

36. Economic and Environmental Impacts in Sugarcane Production to Meet the Brazilian Ethanol Demands by 2030: The Role of Precision Agriculture

The agreement signed at COP-21 reaffirms the vital compromise of Brazil with sugarcane and ethanol production. To meet the established targets, the ethanol production should be 54 billion liters in 2030. From the agronomic standpoint, two alternatives are possible; increase the planted area and/or agricultural yield. The present study aimed to evaluate the economic and environmental impacts in sugarcane production meeting the established targets in São Paulo state. In this context, wer... G.M. Sanches, T.F. Cardoso, M.F. Chagas, A.C. Luciano, D.G. Duft, P.S. Magalhães, H.C. Franco, A. Bonomi

37. Applying a Bivariate Frequency Ratio Technique for Potato High Yield Susceptibility Mapping

Spatial variation of soil characteristics and vegetation conditions are viewed as the most important indicators of crop yield status. Therefore, this study was designed to develop a crop yield prediction model through spatial autocorrelation between the actual yield of potato (Solanum tuberosum L.) crop and selected yield status indicators (soil N, EC, pH, texture and vegetation condition), where the vegetation condition was represented by the cumulative normalized difference vegetation index... K. Al-gaadi, A.A. Hassaballa, E. Tola, R. Madugundu, A.G. Kayad

38. Introducing Precision Ag Tools to Over-100 Year Old Historical Experiment

The historic Knorr-Holden experimental site near Scottsbluff, Nebraska, US, established in 1912 is the oldest irrigated maize plot in North America. Over years, the treatment has been revised a few times to reflect and address contemporary practices. The N fertilization is found to be capable of restoring most of production capacity of the soil. After a full century of the experiment, in 2014, N treatments were revised again. Now, the experiment is a split-plot randomized complete block desig... B. Maharjan

39. Agronōmics: Eliciting Food Security from Big Data, Big Ideas and Small Farms

Most farmers globally could make their farms more productive; few are limited by ambient availabilities of light energy and water. Similarly the sustainability of farming practices offers large scope for innovation and improvement. However, conventional ‘top-down’ Agricultural Knowledge and Innovation Systems (AKISs) are commonly failing to maintain significant progress in either productivity or sustainability because multifarious and complex agronomic interactions thwart accurate... R. Sylvester-bradley, D. Kindred, P. Berry

40. Realising the Full Potential of Precision Agriculture: Encouraging Farmer 'Buy-in' by Building Trust in Data Sharing

Uncertainty around the ownership, privacy and security of farm data are most commonly the reasons cited for farmer’s reluctance to “buy-in” to big data in agriculture. Evidence provided to the recent US Committee on Commerce, Science, and Transportation Subcommittee on Consumer Protections, Product Safety, Insurance, and Data Security, United States Senate Technology in Agriculture: Data Driven Farming (Nov 2017) highlighted that “data ownership, and rel... L. Wiseman, J. Sanderson

41. An Automatic Control Method Research for 9YG-1.2 Large Round Baler

When manual or semi-automatic round baler working, the tractor driver have to frequently manual the machine according to the bale process at the same time of driving. The driver easily feel fatigue in this operating mode for a long time, so the consistency of the bale’s density can not be guaranteed. And there may be wrong operation. In this article, we use the model 9YG-1.2 large round baler as a research prototype. We study the information collection and processing of the baler’... J. Dong, Z. Meng, Y. Cong, A. Zhang, W. Fu, R. Pan, Q. Yang, Y. Shang

42. Correlations Between Meteorological Parameters and the Water Loss of Maize from Silking to Harvesting

The University of Debrecen provides outstanding conditions for the development of “Smart Weather for Precision Agriculture” programs. The reliability of research is provided by the Polyfactoral Long-term Field Experiments of Debrecen (hybrid x fertilisation x plant density x tillage x irrigation) established in 1983. Within this research program, it is possible to examine various crop cultures, cultivars and hybrids under changing natural, environmental and weather circu... K.B. Bodnár, J. Nagy, B. Gombos

43. Exploring Tractor Mounted Hyperspectral System Ability to Detect Sudden Death Syndrome Infection and Assess Yield in Soybean

Pre-visual detection of crop disease is critical for both food and economic security. The sudden death syndrome (SDS) in soybeans, caused by Fusarium virguliforme (Fv), induces 100 million US$ crop loss, per year, in the US alone. Field-based spectroscopic remote sensing offers a method to enable timely detection, but still requires appropriate instrumentation and testing. Soybean plants were measured at canopy level over a course of a growing season to assess the capacity of spectral measure... I. Herrmann, S. Vosberg, P. Ravindran, A. Singh, P. Townsend, S. Conley

44. Development of Farmland-Terrain Simulation System for Consistency of Seeding Depth

A farmland-terrain simulation system suitable for rugged topography was designed to study the irregularities of farmland surface morphology led by both topographic fluctuation and terrain tilt. The system consists of terrain simulation mechanism, hydraulic system, control system, etc. The terrain simulation mechanism is connected to the rack through hydraulic cylinder to simulate farmland surface fluctuation. The hydraulic system controls the hydraulic cylinder to drive the terrain simulation... W. Fu, J. Dong, Y. Cong, N. Gao, Y. Li, Z. Meng

45. Active Canopy Sensor-Based Precision Rice Management Strategy for Improving Grain Yield, Nitrogen and Water Use

The objective of this research was to develop an active crop sensor-based precision rice (Oryza sativa L.) management (PRM) strategy to improve rice yield, N and water use efficiencies and evaluate it against farmer’s rice management in Northeast China. Two field experiments were conducted from 2011 to 2013 in Jiansanjiang, Heilongjiang Province, China, involving four treatments and two varieties (Kongyu 131 and Longjing 21). The results indicated that PRM system significantly increased... J. Lu, H. Wang, Y. Miao

46. Effect of Composts Prepared from Municipal Solid Waste in the Agrochemical Properties of Serosem Soils of Uzbekistan

Optimizing soil fertility and agro-chemical soil properties are currently of great importance, since the content of humus and nutrients from year to year decreases. The reason for decline of soil fertility is the lack of organic fertilizers and use of crop rotation involving leguminous perennial herb. On the other hand a source of organic fertilizer can be municipal solid waste. Currently in the cities of Uzbekistan accumulated huge amount of solid waste whose disposal is an environmental nec... S. Kholikulov, S. Pardaev

47. Managing the Kansas Mesonet for Site Specific Weather Information

Weather data has become one of the most widely discussed layers in precision agriculture especially in terms of agricultural ‘big data’. However, most farmers (and even other researchers outside of meteorology) are not likely aware of the complexities required to maintain weather stations that provide data. These stations are exposed to the elements 24/7 and provide unique challenges for sustainment during extreme weather conditions. Based upon decades of experience, this paper di... T. Griffin, C. Redmond, M. Knapp

48. Precision Fall Urea Fertilizer Applications: Timing Impact on Carbon Dioxide, Ammonia Volatilization and Nitrous Oxide Emissions

To minimize ammonia (NH3) volatilization and nitrous oxide (N2O) emissions from fall applied fertilizer, it is generally recommended to not apply the fertilizer until the soil temperature decreases below 10 C. However, this recommendation is not based on detailed measurements of NH3and N2O emissions. The objective of this study was to determine the influence of fertilizer application timing on nitrous oxide, carbon dioxide, and ammonia volatilization emissions.  Nitrogen fertilizer ... S. Thies, D.E. Clay, S. Bruggeman, D. Joshi, S. Clay, J. Miller