Session 2: \u3cem\u3eDatasets for Precision Agriculture Practices\u3c/em\u3e

Precision agriculture practices as a data-based agriculture require many types of data. In data side of view, precision agriculture practices can be explained in three steps which are 1) data acquisition, 2) data processing, and 3) implementation of final decisions. Producers and agronomists collect data from fields and apply the final decisions to farming. Data scientists need to analyze the big data to make a best decision for maximum benefits. However many different types and formats of data cause difficulties of data analysis. This presentation will talk about types and formats of data that are actually using in precision agriculture practices

UAV for Precision Agriculture

Readers will find answers to the following questions: What is Precision Ag Practice? Why is Remote Sensing used in Agriculture? What and Why is NDVI? UAV for Precision Agriculture? How to use UAV for Precision Agriculture? What are the main steps involved in Drone Work (Planned Flying)

Using Drones for Precision Agriculture

In this teaching module, students will learn what Precision Agriculture is and how to apply drone into Precision Agriculture practices. To use data (images) taken by drone, students will learn the basic theory of Remote Sensing. Using images, students learn how to make NDVI (Normalized Difference Vegetation Index) maps and how to apply drone (remote sensing technique) in agriculture

Introduction to Soil Health for High School Students

Growing food from soil is a basic and important work to continue supporting the growing human population. Producing more foods and producing healthy food are challenging tasks, because producing foods use soil nutrients but can deplete the soil. This can adversely affect the natural balance. If the soil is healthy, we will not only increase production but also grow healthy food. This class is a starting point for learning about Soil Health – where students will learn about how the soil functions and how it directly influences the food we grow. In summary, it is all about striking the right balance and understanding the magic that happens beneath our feet in the world of soil and food production

Soil Health Class-2: Soil pH

This lesson on soil pH explores the crucial role of soil acidity or alkalinity in plant health and productivity. Soil pH, or potential of Hydrogen ions in soil, is a measure of how acidic or basic soil is. Appropriate soil pH condition is important to grow plants. If soil pH is not appropriate, plants cannot absorb nutrients from soil well, then plants cannot grow well and cannot produce vegetables. This class will show what an appropriate soil pH condition is and how to measure soil pH of garden using simple test kit. Covering the pH scale from acidic to alkaline, students learn the impact of pH on nutrient availability, microbial activity, and overall soil fertility. Through hands-on activities, such as recording pH readings and observing soil properties, students gain practical insights into soil variability. The lesson also highlights the importance of indigenous practices in promoting soil health, fostering an awareness of the connection between human practices and soil conditions. This holistic approach aims to empower students to make informed decisions in environmental stewardship and food sovereignty by understanding the intricate relationship between soil pH and successful agriculture

Precision Farming Protocols. Part 2. Comparison of Sampling Approaches for Precision Phosphorus Management

Research is needed to compare the different techniques for developing site‐specific phosphorus (P) recommendations on a field‐wide basis. The objective of this study was to determine the impact different techniques for developing site‐specific P recommendation maps on yield and profitability. Enterprise analysis combined with a crop simulation model and detailed field characterization was used to estimate the value of spatial P information in a system where N was not limiting. The systems evaluated were continuous corn (Zea mays) and corn and soybean (Gfycine max) rotations where sampling and fertilizer applications were applied annually and semi‐annually, respectively. The sampling techniques tested were: (i) an unfertilized P control; (ii) whole field; (iii) whole field plus historic information (feedlot); (iv) landscape positions; (v) soil type; (vi) soil type plus historic information (feedlot); and (vii) 90‐m grid sampling. The finding of this study were based on soil samples collected from a 30 by 30‐m grid. The value of the spatial information was dependent on the crops response to P, the accuracy of the different sampling techniques, crop rotation, and the length of time between sampling dates. All of the sampling techniques produced different application maps. The recommendation map based on a single composite sample under fertilized 56.5% of the field. Increasing the sampling density reduced the percentage of under‐fertilized land. If corn had a low P response, then simulation/enterprise analysis indicated that applying P did not increased profits. For all scenarios tested: (i) the soil type + historic sampling approach had higher potential profits than the 90 m grid sampling approach; and (ii) there was no economic benefit associated with the 90‐m grid sampling. However, if research shows that amortization of sampling and analysis costs over 3 or 4 years is appropriate, then it may be possible to derive economic benefit from a 90‐m grid sampling. For a corn/soybean rotation, where fertilizer was applied when corn was planted and N and P was not applied to soybeans, enterprise/ simulation analysis (2.8 Mg ha‐1 soybean yield goal and a moderate P model) showed that soil + historic sampling approach increased profitability $3.74 ha‐1 when compared to the uniform P treatment

A Rapid Method for Measuring Feces Ammonia-Nitrogen and Carbon Dioxide-Carbon Emissions and Decomposition Rate Constants

A rapid approach is needed for determining the eff ectiveness of precision conservation on soil health as evaluated using CO2 and NH3 emissions. Th is study demonstrated an approach for calculating CO2–C and NH3–N emissions and associated rate constants when feces were applied to bare soil or soil + vegetation. In addition, point CO2–C emission measurements were compared with near continuous measurements. The CO2–C emissions were measured at 2 h intervals over 20 d, whereas ammonia volatilization was measured three times daily for 7 d. Total CO2–C emissions over 20 d were 5% lower [186 g CO2–C (m2 × 20 d) –1] than point measurement collected at 1100 h every day (197 g CO2–C (m2 × 20 d) –1), and about 10% lower than if collected every 2 d [206 g CO2–C (m2 × 20 d) –1]. A Fast Fourier transformation (FFT) showed that temperature and NH3–N and CO2–C emissions followed diurnal cycles and that they were in-phase with each other. Over 7 d, 20% of feces NH4–N was volatilized and that this loss was similar when feces were applied over vegetation or mixed into the soil. Feces additions increased the amplitude of the CO2–C diurnal cycle, and the fecal-C first-order rate degradation constants were higher when mixed with soil [0.0109 ± 0.0043 g(g×d) –1, p = 0.1] than applied over vegetation [0.00454 ± 0.00336 g(g×d) –1, p = 0.1]

Precision Farming Protocols: Part 1. Grid Distance and Soil Nutrient Impact on the Reproducibility of Spatial Variability Measurements

To determine temporal changes in soil nutrient status, reproducible results must be obtained at each time step. The objective of this paper was to determine the impact of grid distance on the reproducibility of spatial variability measurements. Soil samples from the 0 to 15 cm depth were collected from a 30 by 30 m grid in May 1995 in a 65 ha notill corn (Zea mays L.) field. Each bulk sample contained 15 individual cores, collected at sample points located every 11.4 cm along a transect that transversed 3 corn rows (57 cm). At each sampling point latitude, longitude, elevation, landscape position, and soil series were determined. The 30 m grid was used to develop 4 and 9 independent data sets having a 60 and 90 m, grids, respectively. Semivariograms, nugget to sill ratios, and mean squared errors were calculated for each data set. At 60 m: (i) the total N, total C, and pH semivariograms, of different start points, were similar, while semivariograms for Olsen P, K, and Zn were different; (ii) the spatial dependence ratings, based on the nugget to sill ratio, for total N, total C, and pH semivariograms were consistent and suggested moderate spatial dependence; (iii) the spatial dependence rating for Olsen P, K, and Zn for the 4 semivariograms were not consistent and ranged from weak to moderate spatial dependence. At 90 m all soil nutrients had different semivariograms for each start point, while the spatial dependence rating for each total N, total C, and pH start point were consistent and showed moderate spatial dependence. The total C, P, K, Zn, and pH MSE values at 60 m, were 30, 30, 41, 28, and 72% lower than the variance, respectively. This study showed that semivariogram, semivariance, MSE, and nugget to sill ratios reproducibility were dependent on soil nutrient and grid distance

Defining Yield Goals and Management Zones to Minimize Yield and Nitrogen and Phosphorus Fertilizer Recommendation Errors

Three general approaches (minimize soil nutrient variability, yield, and fertilizer recommendation errors) have been used to assess nutrient management zone boundaries. The objective of this study was to determine the influence of different approaches to define management zones and yield goals on minimizing yield variability and fertilizer recommendation errors. This study used soil nutrient and yield information collected from two east-central South Dakota fields between 1995 and 2000. The crop rotation was corn (Zea mays L.) followed by soybean [Glycine max (L.) Merr.]. The four management zone delineation approaches tested were to: (i) sample areas impacted by old homesteads separately from the rest of the field; (ii) separate the field into grid cells; (iii) use geographic information systems or cluster analysis of apparent electrical conductivity, elevation, aspect, and connectedness to identify zones; and (iv) use the Order 1 soil survey. South Dakota fertilizer N and P recommendations were used to calculate fertilizer requirements. This study showed that management zones based on a 4-ha grid cell and an Order 1 soil survey had lower within-zone yield variability than the other methods tested. The best approaches for minimizing recommendation errors were nutrient specific. Nitrogen and P recommendations were improved using multiple years of yield monitor data to develop landscape-specific yield goals, sampling old homesteads separately from the rest of the field, and grid cell soil sampling to fine-tune N and P recommendations

Identifying Management Zones Using Soil, Crop, and Remote Sensing Information

Management zones based on field history, yield maps, topography, remote sensing, and producer preferences have the potential to reduce sampling costs and improve fertilizer recommendations. The objectives of this study were: (i) to determine the influence of different approaches to define nutrient management zones based on soil nutrient and crop yield variability; (ii) to evaluate fertilizer recommendation errors; and (iii) to determine if remote sensing data combined with readily available soil attribute information can be used to predict crop yield. This research was conducted in three eastern South Dakota fields. Soil samples taken in grid sampling were analyzed for Olsen P and N03-N. An AgLeader 2000 yield monitor was used to measure com (Zea mays L.) and soybean (Glycine max L.) yields between 1995 and 2001. Remote sensing was collected in the spring, summer, and late summer in 1999, 2000, and 2001. Over 20 different approaches for identifying management zone boundaries were tested and principal component analysis was used to develop yield prediction models. Soil nitrate and Olsen P concentrations were spatially variable. Yields in summit/shoulder areas were limited by too little water, while in wet years yields in the footslope areas were limited by too much water. For all the methods tested to identify management zone boundaries, splitting the fields into 4-ha blocks had the lowest nutrient, yield, and fertilizer recommendations pooled variances. The impact of block sampling on fertilizer recommendations was attributed to field management and soil forming processes. These results suggest that if areas are not physically connected, then they should not be composited into a single sample, and that both intrinsic and prior management must be considered in developing nutrient management zones. Yield models based on remote sensing data explained the most yield variability when the models used several dates of information. Yield models based on only remote sensing data collected in the summer explained the least amount of yield variability. Adding soil attribute and plant information to the models had a small impact on the ability of the model to exp lam yield variability. Findings from this study can be used by land managers to improve fertilizer recommendations and to estimate crop yields prior to harvest