Proceedings
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| Filter results10 paper(s) found. |
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1. A Comprehensive Model for Farmland Quality Evaluation with Multi-source Spatial InformationFarmland quality represents various properties, including two parts of natural influencing factors and social influencing factors. The natural factors and social factors are interrelated and interaction, which determine the developing direction of farmland system. In order to overcome the limitation of subjective factors and fuzzy incompatible information, a more scientific evaluation method of farmland quality should be developed to reflect the essential characteristic of farmland.... Y. Dong, Y. Wang, X. Song, X. Gu |
2. Exploiting The Variability In Pasture Production On New Zealand Hill Country.New Zealand has about four million hectares in medium to steep hill country pasture to which granular solid fertiliser is applied by airplane. On most New Zealand hill country properties where cultivation is not possible the only means of influencing pasture production yield is through the addition of fertilizers and paddock subdivision to control grazing and pasture growth rates. Pasture response to fertilizer varies in production zones within the farm which can be modelled... M.Q. Grafton, P.J. Mcveagh, R.R. Pullanagari, I.J. Yule |
3. Evaluating Decision Systems For Using Variable Rates In Planting SoybeanIncreased interest in managing seeding rates within soybean fields is being driven by the advances in technologies and the need to increase productivity and economic returns. A wealth of previous research was focused on studying how different seeding rates affect soybean yields at small-plot scales. However, little is known how different site-specific factors influence the responsiveness of soybean to higher or lower plant population densities at field levels, especially across geographic... P. Reeg, P.M. Kyveryga, T.A. Mueller |
4. Precision Nutrient Management System Based on Ion and Crop Growth SensingAutomated sensing and variable-rate supply of nutrients in hydroponic solutions according to the status of crop growth would allow more efficient nutrient management for crop growth in closed systems. The Structure from Motion (SfM) method has risen as a new image sensing method to obtain 3D images of plants that can be used to estimate their growth, such as leaf cover area (LCA), plant height, and fresh weight. In this sense, sensor fusion technology combining ion-selective electrodes (ISEs)... W. Cho, D. Kim, C. Kang, H. Kim, J. Son, S. Chung, J. Jiang, H. Yun |
5. Use of a Cropping System Model for Soil-specific Optimization of Limited WaterIn 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 irrigation... L.R. Ahuja, S.A. Saseendran, L. Ma, D.C. Nielsen, T.J. Trout, A.A. Andales, N.C. Hansen |
6. Learn, Share, Connect and Be Inspired: How One Farming Group in Australia is Driving PA AdoptionThe use of Precision Agriculture (PA) technologies and techniques continues to expand in Australia. The Society of Precision Agriculture Australia (SPAA) has been instrumental in driving the adoption and development of these techniques to support industry and Australian farming communities. SPAA supports innovation, and innovation includes people. Founded in 2002, SPAA, a not for profit extension body, is Australia’s only dedicated farming group communicating and advocating for... N.F. Dimos, J.K. Koch |
7. Mapping Leaf Area Index of Maize in Tasseling Stage Based on Beer-Lambert Law and Landsat-8 ImageLeaf area index (LAI) is one of the important structural parameters of crop population, which could be used to monitor the variety of crop canopy structure and analyze photosynthesis rate. Mapping leaf area index of maize in a large scale by using remote sensing technology is very important for management of fertilizer and water, monitoring growth change and predicting yield. The Beer-Lambert law has been preliminarily applied to develop inversion model of crop LAI, and has achieved good application... X. Gu, S. Wang, G. Yang, X. Xu |
8. Corn Nitrogen Fertilizer Recommendation Models Based on Soil Hydrologic Groups Aid in Predicting Economically Optimal Nitrogen RatesNitrogen (N) fertilizer recommendations that match corn (Zea mays L.) N needs maximize grower profits and minimize water quality consequences. However, spatial and temporal variability makes determining future N requirements difficult. Studies have shown no single soil or weather measurement is consistently increases accuracy, especially when applied over a regional scale, in predicting economically optimal N rate (EONR). Basing site N response on soil hydrological group could help account for... G.M. Bean, N.R. Kitchen, J.J. Camberato, R.B. Ferguson, F.G. Fernandez, D.W. Franzen, C.A. Laboski, E.D. Nafziger, J.E. Sawyer, P.C. Scharf |
9. Using Canopy Hyperspectral Measurements to Evaluate Nitrogen Status in Different Leaf Layers of Winter WheatNitrogen (N) is one of the most important nutrient matters for crop growth and has the marked influence on the ultimate formation of yield and quality in crop production. As the most mobile nutrient constituent, N always transfers from the bottom to top leaves under N stress condition. Vertical gradient changes of leaf N concentration are a general feature in canopies of crops. Hence, it is significant to effectively acquire vertical N information for optimizing N fertilization managements.... X. Xu, Z. Li, G. Yang, X. Gu, X. Song, X. Yang, H. Feng |
10. Driving Growth Through Precision Agriculture: the Evolution of the Nebraska On-farm Research NetworkThe Nebraska On-Farm Research Network (NOFRN), allows farmers to answer production, profitability and sustainability questions in their own field. The University of Nebraska (USA) sponsors the NOFRN and provides technical support in the experimental design, execution, data analysis and results dissemination. In recent years, precision agriculture technologies have expanded network capabilities through an increasing number of experiments and provided new avenues for data analyses. The goal is... G. Balboa, B. Tobaldo, T. Lexow, J.D. Luck |