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1. Pesticide Drift Control with Wireless Sensor NetworksPrecision Agriculture is an agricultural practice that uses technology based on the principle of variability. The geographically referenced data implement the process of agricultural automation so as to dose fertilizers and pesticides. The efficient application of low cost pesticides without contamination the environment is an agricultural production challenge. The main effect to be avoided during application is pesticide drift. To minimize it is important to know the environmental conditions... C.E. Cugnasca, I.M. Santos |
2. The Ultimate Soil Survey in One Pass: Soil Texture, Organic Matter, pH, Elevation, Slope, and CurvatureThe goal of accurately mapping soil variability preceded GPS-aided agriculture, and has been a challenging aspect of precision agriculture since its inception. Many studies have found the range of spatial dependence is shorter than the distances used in most grid sampling. Other studies have examined variability within government soil surveys and concluded that they have limited utility in many precision applications. Proximal soil sensing has long been envisioned as a metho... E. Lund, C. Maxton, G. Kweon |
3. Use of Active Crop Canopy Reflectance Sensor for Nitrogen Sugarcane FertilizationResearches about the use of ground-based canopy reflectance sensors aiming the nitrogen management fertilization on variable-rate over the sugarcane crop have been conducted in São Paulo, Brazil since 2007. Sugarcane response to nitrogen is variable, making difficult the development of models to estimate its d... L.R. Amaral, G. Portz, H. Rosa, J. Molin |
4. Mapping the Leaf Area Index In Vineyard Using a Ground-Based LIDAR ScannerThe leaf area index (LAI) is defined as the one-sided leaf area per unit ground area and is probably the most widely used index to characterize grapevine vigour. However, direct LAI measurement requires the use of destructive leaves sampling methods which are costly and time-consuming and so are other indirect methods. Faced with these techniques, vineyard leaf area can be indirectly estimated using ground-based LIDAR sensors that scan the vines and get information about the geometry and/or s... J. Arno, I. Del moral, A. Escolà, J. Company, J.A. MartÍnez-casasnovas, J. Masip, R. Sanz, J.R. Rosell |
5. Improvement of the Quality of “On-The-Go” Recorded Soil pHAn important basis for lime fertilisation is the recording of pH values. Many studies have shown that the pH value can vary greatly within a small area. Only through the development of a sensor by VERIS has it become possible to determine the pH value cheaply in a much higher sampling density than with the time and cost intensive laboratory method. With respect to their measurement principles, both methods differ fundamentally in that in the laboratory method an extraction medium is used. Thi... M. Schneider, T. Leithold, P. Wagner |
6. Vegetation Indices from Active Crop Canopy Sensor and Their Potential Interference Factors on SugarcaneAmong the inputs usually used in the sugarcane production the nitrogen (N) is the most significant. With the use of ground-based canopy sensors to obtain vegetation indexes (VI), it is possible to obtain recommendations of nutrient supply i... L.R. Amaral, J.P. Molin, L. Taubinger |
7. Nineteen-Soil-Parameter Calibration Models and Mapping for Upland Fields Using the Real-Time Soil SensorIn precision agriculture, rapid, non-destructive, cost-effective and convenient soil analysis techniques are needed for soil management, crop quality control using fertilizer, manure and compost, and variable-rate input for s... S. Shibusawa, K. Ninomiya, M. Kodaira |
8. Impact of Nitrogen (N) Fertilization on the Reflectance of Cotton Plants at Different Spatial ScalesThis study was conducted to examine the reflectance of cotton plants measured at three different spatial scales: individual leaf, canopy, and scene, in relation to N treatment effects, and consequently to select the best spatial scale(s) for estimating chlorophyll or N contents. At the leaf scale, N treatments effects were most apparent at 550... S. Maas, F. Muharam |
9. Temporal N Status Evaluation Using Hyperspectral Vegetation Indices in a Potato CropThe amount and timing of nitrogen (N) fertilization represents a leading issue in precision agriculture, especially for potato (Solanum tuberosum L.) crop since N is an essential element for plant growth and tuber yield. Therefore, the ability to assess in-season crop N status from non-destructive methods such as proximal sensing is a promising alternative to optimize N f... A. Cambouris, K. Chokmani, T. Morier |
10. Integrated Crop Canopy Sensing System for Spatial Analysis of In-Season Crop PerformanceOver the past decade, the relationships between leaf color, chlorophyll content, nitrogen supply, biomass and grain yield of agronomic crops have been studied wi... L. Shiratsuchi, C.C. Lutz, R.B. Ferguson, V.I. Adamchuk |
11. Estimating Soil Quality Indicators with Diffuse Reflectance SpectroscopyKnowledge of within-field spatial variability in soil quality indicators is important to assess the impact of site-specific management on the soil. Standard methods for measuring these properties require considerable time and expense, so sensor-based approaches would b... R.J. Kremer, N.R. Kitchen, K.A. Sudduth, D.B. Myers |
12. Evaluation of the Sensor Suite for Detection of Plant Water Stress in Orchard and Vineyard CropsA mobile sensor suite was developed and evaluated to predict plant water status by measuring the leaf temperature of nut trees and grapevines. It consists of an infrared thermometer to measure leaf temperature along with relevant ambient condition sensors to measure microclimatic variables in the vicinity of the leaf. Sensor suite was successfully evaluated in three crops (almonds, walnuts and grapevines) for both sunlit and shaded leaves. Stepwise linear regression models developed for ... R. Dhillon, V. Udompetaikul, F. Rojo, S. Upadhyaya, D. Slaughter, B. lampinen, K. Shackel |
13. Proximal Sensing Tools to Estimate Pasture Quality Parameters.To date systems for estimating pasture quality have relied on destructive sampling with measurement completed in a laboratory which was very time consuming and expensive. Results were often not received until after the pasture was grazed which defeated the point of the measurement, as farmers required the information to make decisions about grazing strategies to e... R. Pullanagari, I. Yule, M. Tuohy, M. Hedley, W. King, . Dynes |
14. Performance of Two Active Canopy Sensors for Estimating Winter Wheat Nitrogen Status in North China Plain... Q. Cao, Y. Miao, G. Feng, X. Gao, B. Liu, R. Khosla |
15. Different Leaf Sensing Approaches for the Estimation of Winter Wheat Nitrogen StatusNondestructive real time diagnosis of crop N status is crucial to the development of precision nitrogen (N) management strategies. Chlorophyll meter has been a popular sensor for such purposes and different approaches to use this sensor has been developed using a threshold value, nitrogen sufficiency index (NSI) or ratio ... B. Liu, Y. Miao, G. Feng, S. Yue, F. Li, X. Gao |
16. Assessing Water Status in Wheat under Field Conditions Using Laser-Induced Chlorophyll Fluorescence and Hyperspectral MeasurementsClassical measurements for estimating water status in plants using oven drying or pressure chambers are tedious and time-consuming. In the field, changes in radiation conditions may further influence the measurements and thus requir... S. El-sayed, U. Schmidhalter, B. Mistele |
17. Using Informative Bayesian Priors and On-farm Experimentation to Predict Optimal Site-specific Nitrogen RatesMost U.S. Corn Belt states now recommend the Maximum Return to Nitrogen (MRTN) method for determining optimal nitrogen rates, which is based on 15 years of on-farm yield response to nitrogen trials. The MRTN method recommends a uniform rate for a region of a state. This study combines Illinois MRTN data, Bayesian methods, and on-farm experimentation from the Data Intensive Farm Management (DIFM) project to provide site-specific nitrogen recommendations. On-farm trials are now being used to pr... W. Brorsen, D. Poursina, C. Patterson, T. Mieno, B. Edge, E.D. Nafziger |
18. Site-specific Evaluation of Sensor-based Winter Wheat Nitrogen Tools Via On-farm ResearchCrop producers face the challenge of optimizing high yields and nitrogen use efficiency (NUE) in their agricultural practices. Enhancing NUE has been demonstrated by adopting digital agricultural technologies for site-specific nitrogen (N) management, such as remote-sensing based N recommendations for winter wheat. However, winter wheat fields are often uniformly fertilized, disregarding the inherent variability within the fields. Thus, an on-farm evaluation of sensor-based N tools is needed ... J. Cesario pinto, L. Thompson, N. Mueller, T. Mieno, L. Puntel, P. Paccioretti, G. Balboa |
19. The Impact of Row Unit Position on Planter Toolbar on Corn Crop Development: an Experimental StudyPrecision planting techniques are essential to grow corn successfully. Monitoring planter speed, row-unit bounce, and gauge-wheel load ensures high-quality seeding. Vertical vibration during planting can impede seed metering and delivery, causing planting variability. Row unit vibration increases with planting speed and can lead to spatial variability in planting. Therefore, the goals of this study were to 1) understand the influence of row unit location on its vertical vibration; and 2) comp... J. Peiretti, A. Sharda, S. Badua |
20. Enhancing On-farm Rice Yields, Water Productivity, and Profitability Through Alternate Wetting and Drying Technology in Dry Zones of West AfricaIrrigated rice farming is crucial for meeting the growing rice demand and ensuring global food security. Yet, its substantial water demand poses a significant challenge in light of increasing water scarcity. Alternate wetting and drying irrigation (AWD), one of the most widely advocated water-saving technologies, was recently introduced as a prospective solution in the semi-arid zones of West Africa. However, it remains debatable whether AWD can achieve the multiple goals of saving water whil... Y.J. Johnson, M. Becker, E.R. Dossou-yovo, K. Saito |
21. Analysis of Yield Gaps in Sub-Saharan African Cereal Production SystemsFood production in sub-Saharan Africa (SSA) is one of the lowest and keeps declining across farmers’ fields season after season (Assefa et al., 2020; F Affholder, 2013). Yield gaps in cereal cropping systems have been reported by many researchers, attesting to the existence of huge variability in production levels of cereals such as corn, wheat, sorghum, rice and millet. across SSA. It is still unclear whether the yield gaps are similar in size or driven by similar factors across differ... E. Odoom, K.A. Frimpong, S. Phillips |
22. Optimizing Experimental Design for Determining Economic Nitrogen Levels: Insights on the Use of Monte Carlo SimulationsThe determination of economic nitrogen levels is a pivotal element in the quest for sustainable agricultural practices. Designing experiments to accurately identify these levels, especially in contexts constrained by limited plot availability, poses a significant challenge. In response to these challenges, this study endeavors to demonstrate an approach to optimize the experimental design for identifying economic nitrogen levels, even under such constraints. We employed statistical... C. Matavel, A. Meyer-aurich, H. Piepho |
23. Effective Furrow Closing Systems for Consistent Corn Seed PlacementFarmers face a constant challenge when choosing the appropriate planter setup due to the variability of cropping systems under no-till. Effective performance of the planter's closing wheels can reduce errors from previous components that affect seedbed formation in the furrow. Effective seed-to-soil contact during planting is essential for optimal seed emergence and overall crop stand, with the closing wheels playing a pivotal role in this process. Producers have a range of closing wheels... J. Peiretti, B. Gigena, S. Badua, A. Sharda |
24. Assessment of Soil Spatial Properties and Variability Using a Portable VIS-NIRS Soil Probe for On-farm Precision ExperimentationAssessing the spatial variability of soil properties represents an important issue for on-farm sustainable management owing to high cost of sampling densities. Actual methods of soil properties measurement are based on conventional soil sampling of one sample per ha, followed by laboratory analysis, requiring many soil extraction processes and harmful chemicals. This conventional laboratory analysis does not allow exploring spatial variation of soil properties at desired fine spatial scale. T... A. Cambouris, M. Duchemin, E. Lord, N. Ziadi, B. Javed, J.D. Nze memiaghe, D.A. Ramirez-gonzalez |
25. Operationalization of On-farm Experimentation in African Cereal Smallholder Farming SystemsPast efforts have concentrated on linear or top-down approaches in delivering precision nutrient management (PNM) practices to smallholder farmers. These deliberate attempts at increasing adoption of PNM practices have not yielded the expected outcomes, that is, increased productivity and nutrient use efficiency, at scale. This is because technologies generated by scientists with minimal farmer involvement often are not well tailored to the attendant agro-ecological, socio-economic, and cultu... I. Adolwa, S. Phillips, B.A. Akorede, A.A. Suleiman, T. Murrell, S. Zingore |
26. Harnessing Farmers’, Researchers’ and Other Stakeholders’ Knowledge and Experiences to Create Shared Value from On-farm Experimentation: Lessons from KenyaAchieving greater sustainability in farm productivity is a major challenge facing smallholder farmers in Kenya. Existing technologies have not solved the challenges around declining productivity because they are one-size-fits-all that doesn’t account for the diverse smallholder contexts. A study was carried out in Kenya by a multi-disciplinary team to assess the value of On-Farm Experimentation (OFE) to tailor technologies to local conditions. The OFE process begun with identification o... J. Muthamia, I. Adolwa, J. Mutegi, S. Zingore, S. Phillips |
27. Determining Site-Specific Soybean Optimal Seeding Rate Using On-Farm Precision ExperimentationTen on-farm precision experiments were conducted in Nebraska during 2018 – 2022 to address the following: i) determine the Economic Optimal Seeding Rates (EOSR), ii) identify the most important site-specific variables influencing the optimal seeding rates for soybeans. Seeding rates ranged from 200,000 to 440,000 seeds ha-1, and treatments were randomized and replicated in blocks across the entire field. The study was implemented using a variable rate prescription. ... M.M. Dalla betta, L. Puntel, L. Thompson, T. Mieno, J.D. Luck, N. Cafaro la menza, P. Paccioretti |
28. Creating Value from On-farm Research: Efields Data Workflow and Management Successes and ChallengesFarm operations today generate a large amount of data that can be difficult to properly manage. This challenge is further compounded when conducting on-farm research. The Ohio State University eFields program partners with farmers to conduct on-farm research and share results in a timely manner. Since 2017, the team has conducted and shared 987 trials across Ohio with the annual number of trials increasing from 45 to 292. This rapid increase has required development of a data workflow that st... J.P. Fulton, D. Wilson, R. Tietje, E. Hawkins |
29. Evaluating Different Strategies to Analyze On-farm Precision Nitrogen Trial DataOn-farm trials are being conducted by more and more researchers and farmers. On-farm trials are very different to traditional small plot experiments due to the existence of significant within-field variability in soil-landscape conditions. Traditional statistical techniques like analysis of variance (ANOVA) are commonly adopted for on-farm trial analysis to evaluate overall performance of different treatments, assuming uniform environmental and management factors within a field. As a result, ... K. Mizuta, Y. Miao, J. Lu, R.P. Negrini |
30. Influence of Potassium Variability on Soybean YieldDue to its role as a plant essential nutrient, Potassium (K) serves as a fundamental component for plant growth. Soybeans are heavily reliant upon this nutrient for root growth and the production of pods, so much so that after nitrogen, potassium is the second most in-demand nutrient. Much of the overall soybean crop grown in Oklahoma is not managed with the fertility of K directly in mind. However, as the potential and expectation for greater yield increases, so does interest from produ... J. Derrick, S. Akin, R. Sharry, B. Arnall |
31. All for One and One for All: a Simulation Assessment of the Economic Value of Large-scale On-farm Experiment NetworkWhile on-farm experiments offer invaluable insights for precision management decisions, their scope is usually confined to the specific conditions of individual farms and years, which limits the derivation of more broad and reliable decisions. To address this limitation, aggregating data from numerous farms of various crop growth conditions into a comprehensive dataset appears promising. However, the quantifiable value of this experiment network remains elusive, despite the common agreement o... X. Li |
32. Optimizing Chloride (Cl) Application for Enhanced Agricultural YieldThe optimization of chloride (Cl-) application rates is crucial for enhancing crop yields and reducing environmental impact in agricultural systems. This study investigates the relationship between chloride application rates and wheat yields, focusing on Club wheat cultivation in a 19.76-hectare field in Washington State. The target yield was set at 3765 kilograms per hectare, with seeding conducted at 67.24 kilograms per hectare using conservation tillage practices. Potassium chlo... F. Pereira de souza, R.P. Negrini, H. Tao |
33. On-farm Experimentation Case Study in Brazil: Evaluation of Soybean Seeding Rate Using Resources Available at the FarmIn order to maximize grain yield in soybean (Glycine max [L.] Merr.) it is necessary that the plant population is correctly defined. Production environments differ spatially, and cultivar holders suggest plant populations across macroregions and in broad ranges. Refinements of planting seasons and populations are carried out through tests on many properties, often costly and sometimes unrepresentative of most fields. Tools for managing spatial variability are ways to conduct mor... M. Rodrigues alves franchi, I. Molina cyrineu, F. Kagami taira, L. Hunhoff, L.M. Gimenez |
34. 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 ... G. Balboa, B. Tobaldo, T. Lexow, J.D. Luck |