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1. A Crop And Soil Strategy For Sensor-based Variable-rate Nitrogen ManagementCrop-based active canopy sensors and soil-based management zones (MZ) are currently being studied as tools to direct in-season variable-rate N application. Some have suggested the integration of these tools as a more robust decision tool for guiding spatially variable N rates. The objectives of this study were to identify (1) soil variables useful for MZ delineation and (2) determine if MZ could be useful in identifying field areas wi... D.F. Roberts, J.F. Shanahan, R.B. Fergugson, V.I. Adamchuk, N.R. Kitchen |
2. Comparative Analysis Of Different ApproachesThe efficiency of variable rate seeding (VRS) was confirmed in various crops. It is proven that corn requires increasing seeding rates in high-yielding zones, whereas soybeans need lower rates. However, the data for wheat appeared to be controversial. The aim of our experiment was to determine the most efficient strategy for variable rate fertilization and seeding in spring wheat in the conditions of Canadian Prairies. Two approaches were tested: based on Normalize Difference Vegetation Index... A. Melnitchouck |
3. Quantifying Spatial Variability Of Indigenous Nitrogen Supply For Precision Nitrogen Management In North China Plain... Y. Miao, Q. Cao, Z. Cui, F. Li, T.H. Dao, R. Khosla, X. Chen |
4. Precision Manure Management: It Matters Where You Put Your Manure“Precision fertilizer management” has been around for more than a decade and is practiced widely in Colorado and elsewhere. By precision, we mean application of fertilizer at the right time, in the right place, and in the right amount. However, “Precision Manure Management” is a relatively new concept that converge the best manure management practices with precision nutrient management practices, such as variable rate nutrient application across site-specific managemen... M.E. Moshia, R. Khosla, J. Davis, D. Westfall |
5. Variability In Wheat Crop Production Based On Management Zones In Humid Pampas Region, ArgentinaCrop productivity within fields is heterogeneous and it responds to the variation in crop management patterns, and in previous, random, and natural crop management factors. The methodologies for the delimitation of management zones (MZ) within production fields differ based on their application objectives. The ... M. L, M. Diaz-zorita, P. Mercuri |
6. Evaluation Of Different N Management Strategies Using A Tool For Fuzzy Multi Attributive Comparison Of AlternativesApplication of precision agriculture is related with choosing of optimal agrotechnilogy and, in particular, with definition of the best alternative of N management strategy. A potential satisfactory solution of this decision analysis problem could be the uses of multi attribute decision-making analysis based on fuzzy set theory and fuzzy logic (FMADA). This technique provides a means to achieve an optimal decision for real world problems which involve multiple alternatives and criteri... E. Krueger, D. Kurtener, D. Kurtener, R. Khosla |
7. Mepiquat Chloride Application On Cotton At Variable RateMepiquat chloride (1,1-dimethylpiperidinium chloride) are used to control excessive vegetative growth in cotton (Gossypium hirsutum L.) broadcast sprayed by ground or air. As proven by previous researches the variability of the cotton plants height in the field is large enough to justify the application of Mepiquat at variable rate. The major advantages of it are: (i) yield increase; (ii) economy of the applied input; (iii) reducing the potential of environmental pollution. The main objective... P.S. Magalhaes, , |
8. Site-specific Fertilization Management: Influence Of The Past History Of The Addition Of Fertilizers On The Intra Field Variability Of The Rate Of P And K In The Soil.Site specific crop management adapts the fertilizer amount applied in relation to the intra field crop needs. In this context, tries were carried out under field conditions. The aim of the trials was to develop technico-economic baseline data and methodology of soil sampling for precision agriculture in Upper-Normandy. ... C. Duval, J. Llorens, C. Duval, C. Duval, S. Ta |
9. Cotton NDVIResponse To Applied N At Different Soil EC LevelsSpatial variability for crop productivity in the southeastern US Coastal Plain is often due to differences in soil water holding capacity. An experiment was conducted to investigate the use of soil EC as an aid in the site-specific application of sidedress N to cotton. Treatments in the study consisted of three N rates (0, 34, and 112 kg N ha-1). Each treatment was replicated four times in plots that were 4 m wide (four cotton rows) and 350 m long. Soil EC was meas... P.J. Bauer |
10. Spatial Variability Of Crop And Soil Properties In A Crop-livestock Integrated SystemThe knowledge of spatial variability soil properties is useful in the rational use of inputs, as in the site specific application of lime and fertilizer. The objective of this work was to map and evaluate the spatial variability of the crop, soil chemical and physical properties. The study was conducted in 2 areas of 6.9 and 11.7 ha of a Typic Haplustox in Sao Carlos, SP, Brazil. The summer crops corn and sorghum were sowed together to the forage crop Brachiaria brizantha in the system of cro... A.C. Bernardi, C.R. Grego, R.G. Andrade, C.M. Vaz, L.M. Rabello, R.Y. Inamasu |
11. The Effect Of Variable-Rate Fertilizer Nitrogen Decision-Making On Winter Wheat... J. Guo, L. Chen, X. Wang, R. Zhang, L. Zotarelli |
12. Matching Nitrogen To Plant Available Water For Malting Barley On Highly Constrained Vertosol SoilCrop yield monitoring, high resolution aerial imagery and electromagnetic induction (EMI) soil sensing are three widely used techniques in precision agriculture (PA). Yield maps provide an indication of the crop’s response to a particular management regime in light of spatially-variable constraints. Aerial imagery provides timely and accurate information about photosynthetically-active biomass during crop growth and EMI indicates spatial variability in soil texture, salinity and/o... B. Sauer, C.N. Guppy, M.G. Trotter, D.W. Lamb, J.A. Delgado |
13. Spatial And Vertical Distribution Of Soil P, K, And Mg Content In A Vineyard Of The Do Ca Rioja Using Grid And Target Sampling MethodsKnowledge of spatial variability of soil nutrient contents is very important to design a fertilization strategy based on the needs of the vine. Matching fertilization and nutritional plant needs is very important due to the influence of nutritional status of vineyards on productive and qualitative factors. The aim of this work was to study the spatial and vertical variability of P, K and Mg in a vineyard soil by two methods: (i) the grid sampling at three depth ranges (... O. Unamunzaga, A. Castell, G. Besga, R. Perez-parmo, A. Aizpurua |
14. Adoption And Perceived Usefulness Of Precision Soil Sampling Information In Cotton ProductionSoil testing assists farmers in identifying nutrient variability to optimize input placement and timing. Anecdotal evidence suggests that soil test information has a useful life of 3–4 years. However, perceived usefulness may depend on a variety of factors, including field variability, farmer experience and education, farm size, Extension, and factors indirectly related to farming. In 2009, a survey of cotton farmers in 12 Southeastern states collected information... D.C. Harper, D.M. Lambert, B.C. English, J.A. Larson, R.K. Roberts, M. Velandia, D.F. Mooney, S.L. Larkin |
15. Evaluation Of A Controlled Release N-P Fertilizer Using A Modified Drill For Variable Rate FertilizationBase NP or NPK fertilization is a common practice in cereal production in Chile. Usually, a physical NPK blend is band applied with the seed at planting with the drill. Normal fertilizer rates vary from 400 to 500 kg ha-1; however, there is a tendency in the market to move from physical blend towards chemical blends (monogranule) and, more recently, to controlled release fertilizers (CRF). The CRF are usually recommended at very low rates, varying from 70 to 120 kg ha-1, however this rates ar... R.A. Ortega, J.F. Reyes, W. Esquivel, J. Orellana |
16. Yield Limiting Factors In The Conditions Of Southern AlbertaThe main goal of our experiment was to determine the main factors determining yield of green biomass of spring barley in the conditions of Southern Alberta. To analyze soil properties in the field, grid sampling was conducted at 1-ha grid. Soil samples were collected from the depths of 0…15 and 15…60 cm and analyzed for over 20 different characteristics including soil organic matter content, pH, cation exchange capacity (CEC), and the concentrations of macro- and micronutrients.... A. Melnitchouck |
17. Study Of Nitrogen Fixation And Nodulation In Annual Medic(medicago Rigidula) In Inoculation With Foreign And Inside Root Symbiotic BacteriaAnnual species of Medicago are important pasture legumes in western parts of iran. Their productions are affected by suitable soil Rhizobium meliloti strains and environmental conditions. The principle objective of this study was to find a suitable Rhizobium meliloti strain(s) for Medicago rigidula. Two experiments: one in the greenhouse and the other one on the field were conducted in 2006 to determine nodulation, and ni... E. Nabizadeh |
18. Site Specific Management Of An Oxisol Cultivated With Corn For Application Of Lime And GypsumDue to the necessity to improve soil fertility diagnostic, the researchers have been searched for more efficient technologies on agronomic, economic and environmental aspects. One of these technologies is the use of the concept of site-specific for soil fertility management. This research was conducted in a farm field (100 ha) located in Corinto, Minas Gerais state. The soil is classified as clayey Oxisol, cropped with corn (Zea mays L.) and irrigated with a center-pivot sprinkler irrigation ... A. Coelho, T.F. Cunha, T.F. Cunha |
19. Laboratory Evaluation Of Ion-selective Electrodes For Simultaneous Analysis Of Macronutrients In Hydroponic Solution... H. Kim, , , , K.A. Sudduth |
20. Effect Of Nitrogen Application Rate On Soil Residual N And Cotton YieldA long-term study was conducted on nitrogen application rate and its impact on soil residual nitrogen and cotton (FM960B2RF) lint yield under a drip irrigation production system near Plainview, Texas. The experiment was a randomized complete block design with five nitrogen application rates (0, 56, 112, 168 and 224 kg per ha) and five replications. The soil nitrogen treatment was applied as side dressing. Cotton yield, leaf N, seed N, soil residual nitrate, amount of irrigation, and rainfall ... M. Parajulee, D. Neupane, C. Wang, S. Carroll, R. Shrestha |
21. Soil Quality Improvement Through Proper Combination Of Tillage, Nitrogen Fertilization And Cover Cropping SystemsNo-tillage, N fertilization and cover cropping affect physical, chemical and biological qualities of soil. We investigated the effect of 15-yr of tillage systems, N fertilization and cover crops on soil organic matter, aggregation, bulk density and on microbial community in the sandy loam soil of central Italy. The soil in no-tillage (NT) system had 50% more organic matter and 3 folds higher aggregate stability than the soil in conventional tillage (CT) system. The NT system significantly inc... T.B. Sapkota |
22. Variability in Yield Response of Maize to N, P and K Fertilization Towards Site-specific Nutrient Recommendations in Two Maize Belts in TogoSavannah and central regions are the major maize production zones in Togo, but with maize grain yields at a threshold of only 1.5 Mg ha-1. We use a participatory approach to assess the importance of the major three macro elements (N, P and K) for maize cropping in the two regions in order to further allow for site-specific and scalable fertilizer recommendations. Thirty farmers’ fields served as pilot sites, allocated within the two regions to account for spatial variability ... J.M. Sogbedji, M. Lare, A.K. Lotsi, K.A. Amouzou, T. Agneroh |
23. Suitability of ML Algorithms to Predict Wild Blueberry Harvesting LossesThe production of wild blueberries (Vaccinium angustifolium.) is contributing 112.2 million dollars to the Canada’s revenue which can be further increased through controlling harvest losses. A precise prediction of blueberry harvesting losses is necessary to mitigate such losses. In this study, the performance of three machine learning (ML) models was evaluated to predict the wild blueberry harvest losses on the ground. The data from four commercial fields in Atlantic Canada we... H. Khan, T. Esau, A. Farooque, F. Abbas |
24. Comparative Analysis of Light-weight Deep Learning Architectures for Soybean Yield Estimation Based on Pod Count from Proximal Sensing Data for Mobile and Embedded Vision ApplicationsCrop yield prediction is an important aspect of farming and food-production. Therefore, estimating yield is important for crop breeders, seed-companies, and farmers to make informed real-time financial decisions. In-field soybean (Glycine max L.(Merr.)) yield estimation can be of great value to plant breeders as they screen thousands of plots to identify better yielding genotypes that ultimately will strengthen national food security. Existing soybean yield estimation too... J.J. Mathew, P.J. Flores, J. Stenger, C. Miranda, Z. Zhang, A.K. Das |
25. Agriculture Machine Guidance Systems: Performance Analysis of Professional GNSS ReceiversGNSS (Global Navigation Satellite Systems) plays nowadays a major role in different civilian activities and is a key technology enabling innovation in different market sectors. For instance, GNSS-enabled solutions are widespread within the Precision Agriculture and, among them, applications in the field of machinery guidance are commonly employed to optimize typical agriculture practices. The scope of this paper is to present the outcomes of the agriculture testing campaign performe... J. Capolicchio, D. Mennuti, I. Milani, M. Fortunato, R. Petix, J. Reyes gonzalez, M. Sunkevic |
26. Enhancing PA Adoption Through Value ConnectionsDespite an increase in breadth of precision agriculture over time, and the attendant elements of digital agriculture that either support PA or integrates the outputs of PA, the pace of adoption of digital agriculture in our farming systems remains slow. In assessing impediments to adoption of digital agriculture, much work to date has focused on the value proposition as considered by individual producers or value chain actors. At this level, adoption remains constrained by perceptions o... D.W. Lamb, M.T. Schaefer |
27. Farmer Charlie - Low Cost Smart Local Data Available to Remote FarmersFarmer Charlie brings connectivity and information to farmers, who receive tailored agronomic data to improve their agricultural practice. Farmer Charlie is based on on-site sensors through which soil data can be detected, gathered, and processed by a dedicated server. Broadband communication allows farmers to receive real-time, localised information on tablet or mobile phone. Farmer Charlie is a low-cost solution, it can be adapted to various crops and to detect soil humidity, pH, temperatur... B. Bonnardel |
28. Smart Food Oases: Development of a Distributed Point-to-point Urban Food Ecosystem in Food Desert AreasUrban agriculture has been getting much attention in the past decade as a solution to overcome food insecurity and accessibility of food for urban residents and to have better green environments in cities. Urban agriculture is expected to provide better nutrients to residents, reduce transportation and environmental costs, and help urban dwellers access food efficiently. The present study is to build a collaborative ecosystem among urban growers/producers and create bridges from these farmers... J. Lee, S. Song, S. Oh, K. Krishnaswamy, C. Sun, Y. Adu-gyamfi |
29. The ISO Strategic Advisory Group for Smart Farming: a Multi-pronged Opportunity for Greater Global InteroperabilityAgriculture is becoming increasingly complex and producers must secure their profitability, sustainability, and freedom to operate under a progressively more challenging set of constraints such as climate change, regulatory pressure, changes in consumer preferences, increasing cost of inputs, and commodity price volatility. We have not, however, yet reached the level of data interoperability required for a truly "smart" farming that can tackle the aforementioned probl... R. Ferreyra, J. Lehmann |