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Factors Driving Adoption
On Farm Experimentation with Site-Specific Technologies
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Authors
Anaba, C.I
Badua, S
Bazzi, C.L
Bekkerman, A
Betzek, N.M
Canavari, M
Carter, E
Ciampitti, I
Clarke, S
Colley III, R
Cook, S
Csatári, N
Davis, P
Edge, B
Ekanayake, D.C
Emmi, L
Erickson, B.J
Evans, F
Fulton, J.P
Gavioli, A
Gibberd, M
Gong, A
Griffin, T.W
Harsányi, E
Hatfield, G
Hatley, D
Hawkins, E
Hegedus, P.B
Holmes, A
Izurieta, C
Jones, A
Kindred, D
Klopfenstein, A
Kovács, A.J
Kulmány, I
Lacoste, M
Lattanzi, P
Lowenberg-DeBoer, J
Magalhães, P.S
Marchant, B
Maxwell, B
Maxwell, B.D
Meeks, C
Milics, G
Morris, T
Nagy, J
Neményi, M
Nyéki, A
Oberthur, T
Owens, J
Payn, R
Peerlinck, A
Port, K
Ragán, P
Reicks, G
Ridout, M
Roques, S
Rátonyi, T
Schenatto, K
Sharda, A
Shearer, S
Sheppard, J
Sheppard, J
Silverman, N
Souza, E.G
Stevens, G
Strasser, R
Sulyok, D
Sylvester-Bradley, R
Tremblay, N
Vitali, G
Vories, E
Vántus, A
Werner, A
Zsebő, S
Topics
Factors Driving Adoption
On Farm Experimentation with Site-Specific Technologies
Type
Oral
Poster
Year
2022
2018
Home » Topics » Results

Topics

Filter results18 paper(s) found.

1. Use of Farmer’s Experience for Management Zones Delineation

In the management of spatial variability of the fields, the management zone approach (MZs) divides the area into sub-regions of minimal soil and plant variability, which have maximum homogeneity of topography and soil conditions, so that these MZs must lead to the same potential yield. Farmers have experience of which areas of a field have high and low yields, and the use of this knowledge base can allow the identification of MZs in a field based on production history. The objective of this s... K. Schenatto, E.G. Souza, C.L. Bazzi, A. Gavioli, N.M. Betzek, P.S. Magalhães

2. Supporting and Analysing On-Farm Nitrogen Tramline Trials So Farmers, Industry, Agronomists and Scientists Can LearN Together

Nitrogen fertilizer decisions are considered important for the agronomic, economic and environmental performance of cereal crop production. Despite good recommendation systems large unpredicted variation exists in measured N requirements. There may be fields and farms that are consistently receiving too much or too little N fertilizer, therefore losing substantial profit from wasted fertilizer or lost yield. Precision farming technologies can enable farmers (& researchers) to test appropr... D. Kindred, R. Sylvester-bradley, S. Clarke, S. Roques, D. Hatley, B. Marchant

3. An On-farm Experimental Philosophy for Farmer-centric Digital Innovation

In this paper, we review learnings gained from early On-Farm Experiments (OFE) conducted in the broadacre Australian grain industry from the 1990s to the present day. Although the initiative was originally centered around the possibilities of new data and analytics in precision agriculture, we discovered that OFEs could represent a platform for engaging farmers around digital technologies and innovation. Insight from interacting closely with farmers and advisors leads us to argue for a change... S. Cook, M. Lacoste, F. Evans, M. Ridout, M. Gibberd, T. Oberthur

4. Evaluation of Strip Tillage Systems in Maize Production in Hungary

Strip tillage is a form of conservation tillage system. It combines the benefits of conventional tillage systems with the soil-protecting advantages of no-tillage. The tillage zone is typically 0.25 to 0.3 m wide and 0.25 to 0.30 m deep. The soil surface between these strips is left undisturbed and the residue from the previous crop remain on the soil surface. The residue-covered area reaches 60-70%. Keeping residue on the surface helps prevent soil structure and reduce water loss from the so... T. Rátonyi, P. Ragán, D. Sulyok, J. Nagy, E. Harsányi, A. Vántus, N. Csatári

5. Delineation of 'Management Classes' Within Non-Irrigated Maize Fields Using Readily Available Reflectance Data and Their Correspondence to Spatial Yield Variation

Maize is grown predominantly for silage or gain in North Island, New Zealand. Precision agriculture allows management of spatially variable paddocks by variably applying crop inputs tailored to distinctive potential-yield limiting areas of the paddock, known as management zones. However, uptake of precision agriculture among in New Zealand maize growers is slow and limited, largely due to lack of data, technical expertise and evidence of financial benefits. Reflectance data of satellite and a... D.C. Ekanayake, J. Owens, A. Werner, A. Holmes

6. Improving Yield Prediction Accuracy Using Energy Balance Trial, On-the-Go and Remote Sensing Procedure

 Our long term experience in the ~23.5 ha research field since 2001 shows that decision support requires complex databases from each management zone within that field (eg. soil physical and chemical parameters, technological, phenological and meteorological data). In the absence of PA sustainable biomass production cannot be achieved. The size of management zones will be ever smaller. Consequently, the on the go and remote sensing data collection should be preferred.  ... A. Nyéki , G. Milics, A.J. Kovács, M. Neményi, I. Kulmány, S. Zsebő

7. Variety Effects on Cotton Yield Monitor Calibration

While modern grain yield monitors are able to harvest variety and hybrid trials without imposing bias, cotton yield monitors are affected by varietal properties. With planters capable of site-specific planting of multiple varieties, it is essential to better understand cotton yield monitor calibration. Large-plot field experiments were conducted with two southeast Missouri cotton producers to compare yield monitor-estimated weights and observed weights in replicated variety trials. Two replic... E. Vories, A. Jones, G. Stevens, C. Meeks

8. Can Optimization Associated with On-Farm Experimentation Using Site-Specific Technologies Improve Producer Management Decisions?

Crop production input decisions have become increasingly difficult due to uncertainty in global markets, input costs, commodity prices, and price premiums. We hypothesize that if producers had better knowledge of market prices, spatial variability in crop response, and weather conditions that drive crop response to inputs, they could more cost-effectively make profit-maximizing input decisions. Understanding the drivers of variability in crop response and designing accompanying management str... B.D. Maxwell, A. Bekkerman, N. Silverman, R. Payn, J. Sheppard, C. Izurieta, P. Davis, P.B. Hegedus

9. Draft Privacy Guidelines and Proposal Outline to Create a Field-Scale Trial Data Repository for Data Collected by On-Farm Networks

Implementing better management practices in corn and soybeans that increase profitability and reduce pollution caused by the practices requires large numbers of field-scale, replicated trials. Numerous complex and often unmeasurable interactions among the environment, genetics and management at the field scale require large numbers of trials completed at the field scale in a systematic and uniform manner to enable calculation of probabilities that a practice will be an improvement compared wi... T. Morris, N. Tremblay

10. An Economic-Theory-Based Approach to Management Zone Delineation

In both the academic and popular literatures on precision agriculture technology, a management zoneis generally defined as an area in a field within which the optimal input application strategy is spatially uniform.  The characteristics commonly chosen to delineate management zones, both in the literature and in commercial practice, are yield and variables associated with yield.  But microeconomic theory makes clear that economically optimal input application strategi... B. Edge

11. Influence of Planter Downforce Setting and Ground Speed on Seeding Depth and Plant Spacing Uniformity of Corn

Uniform seed placement improves seed-to-soil contact and requires proper selection of downforce control across varying field conditions. At faster ground speeds, downforce changes and it becomes critical to select the level of planter downforce settings to achieve the desired consistency of seed placement during planting. The objective of this study was to assess the effect of ground speed and downforce setting on seeding depth and plant spacing and to evaluate the relationship of ground spee... A. Sharda, S. Badua, I. Ciampitti, R. Strasser, T.W. Griffin

12. Investigate the Optimal Plot Length in On-Farm Trials

Agronomic researchers have recently begun running large-scale, on-farm field trials that employ new technologies that enable us to conduct hundreds of farm trials all over the world and, by extension, rigorous quantitative and data-centered analysis.  The large-scale, on-farm trials follow traditional small-plot trials where the fields are divided into plots, and different treatments are randomly assigned to each plot. Over the past two years, researchers have been designing trials with ... A. Gong

13. Using Deep Learning in Yield and Protein Prediction of Winter Wheat Based on Fertilization Prescriptions in Precision Agriculture

Precision Agriculture has been gaining interest due to the significant growth in the fields of engineering and computer science, hence leading to more sophisticated methods and tools to improve agricultural techniques. One approach to Precision Agriculture involves the application of mathematical models and machine learning to fertilization optimization and yield prediction, which is what this research focuses on. Specifically, in this work we report the results of predicting yield and protei... J. Sheppard, A. Peerlinck, B. Maxwell

14. Can Unreplicated Strip Trials Be Used in Precision On-Farm Experiments?

On-farm experiments are used to evaluate a wide variety of products ranging from pesticide and fertilizer rates to the installation of tile drainage. The experimental design for these experiments is usually replicated strip trials.  Replication of strip trials is used to estimate experimental error, which is the basis for judging statistical significance of treatment effects. Another consideration for using strip trials is greater within-field variability than smaller fields us... G. Hatfield, G. Reicks, E. Carter

15. eFields – An On-Farm Research Network to Inform Farm Recommendations

On-farm research has been traditionally used to provide local, field-scale information about agronomic practices. Farmers tend to have more confidence in on-farm research results because they are perceived to be more relevant to their farm operations compared to small plot research results. In recent years, more farmers have been conducting on-farm studies to help evaluate practices and input decisions.  Recent advances in precision agriculture technologies have stream-lined the on-... J.P. Fulton, E. Hawkins, R. Colley iii, K. Port, S. Shearer, A. Klopfenstein

16. Survey Shows Specialty and Commodity Crop Retailers Use Precision Agriculture Differently

The 2021 CropLife-Purdue Survey of precision agricultural practices by US agricultural input dealers serving the American grain and oilseed sector shows that most of them use GPS guidance and related technologies like sprayer boom control, most provide variable rate fertilizer services, and the majority say that fertilizer decisions are influenced by grower data. In contrast, dealers serving horticultural and specialty crop farms indicate comparatively modest adoption of many precision agricu... B.J. Erickson, J. Lowenberg-deboer

17. Farmers’ and Experts’ Perceptions of Precision Farming Impacts on Economic Efficiency, Food Security, Climate and Environmental Sustainability

“Global food security could be in jeopardy, due to mounting pressures on natural resources and to climate change, both of which threaten the sustainability of food systems at large. Excessive fertilizer use can contribute to problems of eutrophication, acidification, climate change and the toxic contamination of soil, water and air. Lack of fertilizer application may cause the degradation of soil fertility. Agricultural production systems need to focus more on the effective co... C.I. Anaba

18. Robot Safety Issues in Field Crops - EU Regulatory Issues and Technical Aspects

The use of robots in Precision Agriculture is becoming of great interest, but they introduce a new kind of risk in the field due to their self-acting and self-driving capability. Safety issues appear with respect to people working in the same field in human-robot collaboration (HRC) framework or to the accidental presence of humans or animals. A robot out of control may also invade other areas causing unpredictable harm and damage. Currently, the safety of highly automated agricultu... M. Canavari, P. Lattanzi, G. Vitali, L. Emmi