Agronomy

Research

35 pages, 7287 KiB

Open AccessArticle

Implementation of Proximal and Remote Soil Sensing, Data Fusion and Machine Learning to Improve Phosphorus Spatial Prediction for Farms in Ontario, Canada

by Abdelkrim Lachgar, David J. Mulla and Viacheslav Adamchuk

Agronomy 2024, 14(4), 693; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy14040693 - 27 Mar 2024

Viewed by 564

Abstract

One of the challenges in site-specific phosphorus (P) management is the substantial spatial variability in plant available P across fields. To overcome this barrier, emerging sensing, data fusion, and spatial predictive modeling approaches are needed to accurately reveal the spatial heterogeneity of P. [...] Read more.

One of the challenges in site-specific phosphorus (P) management is the substantial spatial variability in plant available P across fields. To overcome this barrier, emerging sensing, data fusion, and spatial predictive modeling approaches are needed to accurately reveal the spatial heterogeneity of P. Seven spatially variable fields located in Ontario, Canada are clustered into two zones; four fields are located in eastern Ontario and three others are located in western Ontario. This study compares Bayesian Additive Regression Trees (BART), Support Vector Machine regressor (SVM), and Ordinary Kriging (OK), along with novel data fusion concepts, to analyze integrated high-density spatial data layers related to spatial variability in soil available P. Feature selection and interaction detection using BART variable selection and Recursive Feature Elimination (RFE) for SVM were applied to 42 predictors, including soil-vegetation indices derived from PlanetScope multispectral imagery, high-density apparent soil electrical conductivity (EC_a), and high-resolution topographic attributes derived from DUALEM-21S and a Real-Time Kinematic (RTK) global navigation satellite systems (GNSS) receiver, respectively. Modeling spatial heterogeneity of soil available P with BART showed higher accuracy than SVM and OK in both zones of this study when trained and tested on ground truth data from clusters of farms. A BART variable selection approach resulted in six auxiliary predictors of soil available P in the eastern zone, while only four predictors were selected to predict P in the western zone. RFE for SVM resulted in models with 15 and 12 auxiliary predictors in the eastern and western Ontario zones. Topographic elevation was the most influential predictor of soil available P in both zones. Compared with the SVM and OK methods, BART exhibited lower average RMSE values for individual fields of 1.86 ppm and 3.58 ppm across the eastern and western Ontario zones, respectively, along with higher R² values of 0.85 and 0.83, respectively. In contrast, SVM had RMSE values for individual fields in the eastern and western Ontario zones, respectively, averaging 5.04 ppm and 7.51 ppm and R² values of 0.27 and 0.43. RMSE values for soil available P in individual fields across the eastern and western Ontario zones averaged 4.77 ppm and 7.81 ppm, respectively, with the OK method, while R² values averaged 0.19 and 0.44. The selection of suitable auxiliary predictors and data fusion, combined with BART spatial machine learning algorithms, have potential to be a useful tool to accurately estimate spatial patterns in soil available P for agricultural fields in Ontario, Canada. Full article

(This article belongs to the Special Issue The Importance of Soil Spatial Variability in Precision Agriculture)

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13 pages, 1801 KiB

Open AccessArticle

Unravelling the Complexities of Genotype-Soil-Management Interaction for Precision Agriculture

by Svend Christensen and Signe M. Jensen

Agronomy 2023, 13(11), 2727; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13112727 - 29 Oct 2023

Viewed by 1059

Abstract

The knowledge of interactions among crop genotypes, soil types, and crop management is essential for precision agriculture. This paper explores these interactions through the analysis of 27 years of winter wheat trials, with 276 unique varieties tested across seven distinct soil types and [...] Read more.

The knowledge of interactions among crop genotypes, soil types, and crop management is essential for precision agriculture. This paper explores these interactions through the analysis of 27 years of winter wheat trials, with 276 unique varieties tested across seven distinct soil types and more than 8000 plots. The study investigates how different winter wheat crop varieties respond to varying soil types and preceding crops. The findings revealed a significant interaction between variety, soil type, and preceding crop. With only a few exceptions, the highest-yielding varieties were predominantly the most recently developed. The ranking of the varieties exhibited inconsistency across the various soil types, implying that a variety yields differently when cultivated in different soil types. Furthermore, the influence of preceding crops on yield varied with soil type. This suggests that taking field-specific soil variation and the preceding crop into account during variety selection may improve the yield potential. Furthermore, the study highlights consistent yield increases due to advancements in breeding programs, with yearly increases ranging from 0.05 to 0.1 t/ha per year across all soil types. Integration of insights from genetics, soil attributes, and management practices demonstrates how farmers can increase productivity. Full article

(This article belongs to the Special Issue The Importance of Soil Spatial Variability in Precision Agriculture)

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17 pages, 9714 KiB

Open AccessArticle

Dryland Winter Wheat Production and Its Relationship to Fine-Scale Soil Carbon Heterogeneity—A Case Study in the US Central High Plains

by Paulina B. Ramírez, Francisco J. Calderón, Merle F. Vigil, Kyle R. Mankin, David Poss and Steven J. Fonte

Agronomy 2023, 13(10), 2600; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13102600 - 12 Oct 2023

Viewed by 1029

Abstract

Soil carbon plays a key role in maintaining soil quality, but its direct impact on crop yields depends on the interplay of different factors. This study aims to study fine–spatial variation soil properties and their effect on grain productivity in fallow–wheat cropping systems [...] Read more.

Soil carbon plays a key role in maintaining soil quality, but its direct impact on crop yields depends on the interplay of different factors. This study aims to study fine–spatial variation soil properties and their effect on grain productivity in fallow–wheat cropping systems in the US central High Plains. We evaluate wheat yields in relation to soil macro and micronutrients, total C (TC), and texture as well as subtle variations in field elevation. To document soil–yield relationships at a fine spatial scale, soil sampling (0–15 and 15–30 cm depths) was conducted using a regular 30 m grid spacing in eleven adjacent fields. Interpolated yield maps indicated that the availability of key nutrients and textures contributed to the spatial distribution of wheat productivity. Random forest (RF) showed that these soil attributes were able to explain slightly under 30% of the spatial variation in crop yields. Our findings demonstrate that TC can often serve as a reliable proxy for delineating yield-based management zones, even in inherently low C soils. In addition, Fe, Zn, SO₄-S, sand, and subtle topographic changes were also critical factors affecting wheat yield. Our results highlight that developing management zones in these soils relying exclusively on soil information is not straightforward. However, the high level of within-field spatial variability observed needs to be addressed. Full article

(This article belongs to the Special Issue The Importance of Soil Spatial Variability in Precision Agriculture)

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20 pages, 14341 KiB

Open AccessArticle

Drip Irrigation Soil-Adapted Sector Design and Optimal Location of Moisture Sensors: A Case Study in a Vineyard Plot

by Jaume Arnó, Asier Uribeetxebarria, Jordi Llorens, Alexandre Escolà, Joan R. Rosell-Polo, Eduard Gregorio and José A. Martínez-Casasnovas

Agronomy 2023, 13(9), 2369; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13092369 - 12 Sep 2023

Cited by 1 | Viewed by 1309

Abstract

To optimise sector design in drip irrigation systems, a two-stage procedure is presented and applied in a commercial vineyard plot. Soil apparent electrical conductivity (ECa) mapping and soil purposive sampling are the two stages on which the proposal is based. Briefly, ECa data [...] Read more.

To optimise sector design in drip irrigation systems, a two-stage procedure is presented and applied in a commercial vineyard plot. Soil apparent electrical conductivity (ECa) mapping and soil purposive sampling are the two stages on which the proposal is based. Briefly, ECa data to wet bulb depth provided by the VERIS 3100 soil sensor were mapped before planting using block ordinary kriging. Looking for simplicity and practicality, only two ECa classes were delineated from the ECa map (k-means algorithm) to delimit two potential soil classes within the plot with possible different properties in terms of potential soil water content and/or soil water regime. Contrasting the difference between ECa classes (through discriminant analysis of soil properties at different systematic sampling locations), irrigation sectors were then designed in size and shape to match the previous soil zoning. Taking advantage of the points used for soil sampling, two of these locations were finally selected as candidates to install moisture sensors according to the purposive soil sampling theory. As these two spatial points are expectedly the most representative of each soil class, moisture information in these areas can be taken as a basis for better decision-making for vineyard irrigation management. Full article

(This article belongs to the Special Issue The Importance of Soil Spatial Variability in Precision Agriculture)

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13 pages, 3663 KiB

Open AccessArticle

Evaluating Critical Nitrogen Dilution Curves for Assessing Maize Nitrogen Status across the US Midwest

by Hui Shao, Yuxin Miao, Fabián G. Fernández, Newell R. Kitchen, Curtis J. Ransom, James J. Camberato, Paul R. Carter, Richard B. Ferguson, David W. Franzen, Carrie A. M. Laboski, Emerson D. Nafziger, John E. Sawyer and John F. Shanahan

Agronomy 2023, 13(7), 1948; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13071948 - 23 Jul 2023

Cited by 1 | Viewed by 1169

Abstract

Plant N concentration (PNC) has been commonly used to guide farmers in assessing maize (Zea mays L.) N status and making in-season N fertilization decisions. However, PNC varies based on the development stage. Therefore, a relationship between biomass and N concentration is [...] Read more.

Plant N concentration (PNC) has been commonly used to guide farmers in assessing maize (Zea mays L.) N status and making in-season N fertilization decisions. However, PNC varies based on the development stage. Therefore, a relationship between biomass and N concentration is needed (i.e., critical N dilution curve; CNDC) to better understand when plants are N deficient. A few CNDCs have been developed and used for plant N status diagnoses but have not been tested in the US Midwest. The objective of this study was to evaluate under highly diverse soil and weather conditions in the US Midwest the performance of CNDCs developed in France and China for assessing maize N status. Maize N rate response trials were conducted across eight US Midwest states over three years. This analysis utilized plant and soil measurements at V9 and VT development stages and final grain yield. Results showed that the French CNDC (y = 34.0x^−0.37, where y is critical PNC, and x is aboveground biomass) was better with a 91% N status classification accuracy compared to only 62% with the Chinese CNDC (y = 36.5x^−0.48). The N nutrition index (NNI), which is the quotient of the measured PNC and the calculated critical N concentration (Nc) based on the French CNDC was significantly related to soil nitrate-N content (R² = 0.38–0.56). Relative grain yield on average reached a plateau at NNI values of 1.36 at V9 and 1.21 at VT but for individual sites ranging from 0.80 to 1.41 at V9 and from 0.62 to 1.75 at VT. The NNI threshold values or ranges optimal for crop biomass production may not be optimal for grain yield production. It is concluded that the CNDC developed in France is suitable as a general diagnostic tool for assessing maize N status in US Midwest. However, the threshold values of NNI for diagnosing maize N status and guiding N applications vary significantly across the region, making it challenging to guide specific on-farm N management. More studies are needed to determine how to effectively use CNDC to make in-season N recommendations in the US Midwest. Full article

(This article belongs to the Special Issue The Importance of Soil Spatial Variability in Precision Agriculture)

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20 pages, 5864 KiB

Open AccessEditor’s ChoiceArticle

Spatial Analysis of Soil Moisture and Turfgrass Health to Determine Zones for Spatially Variable Irrigation Management

by Ruth Kerry, Ben Ingram, Keegan Hammond, Samantha R. Shumate, David Gunther, Ryan R. Jensen, Steve Schill, Neil C. Hansen and Bryan G. Hopkins

Agronomy 2023, 13(5), 1267; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13051267 - 28 Apr 2023

Cited by 2 | Viewed by 1094

Abstract

Irrigated turfgrass is a major crop in urban areas of the drought-stricken Western United States. A considerable proportion of irrigation water is wasted through the use of conventional sprinkler systems. While smart sprinkler systems have made progress in reducing temporal mis-applications, more research [...] Read more.

Irrigated turfgrass is a major crop in urban areas of the drought-stricken Western United States. A considerable proportion of irrigation water is wasted through the use of conventional sprinkler systems. While smart sprinkler systems have made progress in reducing temporal mis-applications, more research is needed to determine the most appropriate variables for accurately and cost-effectively determining spatial zones for irrigation application. This research uses data from ground and drone surveys of two large sports fields. Surveys were conducted pre-, within and towards the end of the irrigation season to determine spatial irrigation zones. Principal components analysis and k-means classification were used to develop zones using several variables individually and combined. The errors associated with uniform irrigation and different configurations of spatial zones are assessed to determine comparative improvements in irrigation efficiency afforded by spatial irrigation zones. A determination is also made as to whether the spatial zones can be temporally static or need to be re-determined periodically. Results suggest that zones based on spatial soil moisture surveys and simple observations of whether the grass felt wet or dry are better than those based on NDVI, other variables and several variables in combination. In addition, due to the temporal variations observed in spatial patterns, ideally zones should be re-evaluated periodically. However, a less labor-intensive solution is to determine temporally static zones based on patterns in soil moisture averaged from several surveys. Of particular importance are the spatial patterns observed prior to the start of the irrigation season as they reflect more temporally stable variation that relates to soil texture and topography rather than irrigation management. Full article

(This article belongs to the Special Issue The Importance of Soil Spatial Variability in Precision Agriculture)

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14 pages, 1963 KiB

Open AccessEditor’s ChoiceArticle

Irrigation Zone Delineation and Management with a Field-Scale Variable Rate Irrigation System in Winter Wheat

by Elisa A. Flint, Bryan G. Hopkins, Jeffery D. Svedin, Ruth Kerry, Matthew J. Heaton, Ryan R. Jensen, Colin S. Campbell, Matt A. Yost and Neil C. Hansen

Agronomy 2023, 13(4), 1125; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy13041125 - 15 Apr 2023

Cited by 3 | Viewed by 1689

Abstract

Understanding spatial and temporal dynamics of soil water within fields is critical for effective variable rate irrigation (VRI) management. The objectives of this study were to develop VRI zones, manage irrigation rates within VRI zones, and examine temporal differences in soil volumetric water [...] Read more.

Understanding spatial and temporal dynamics of soil water within fields is critical for effective variable rate irrigation (VRI) management. The objectives of this study were to develop VRI zones, manage irrigation rates within VRI zones, and examine temporal differences in soil volumetric water content (VWC) from irrigation events via soil sensors across zones. Five irrigation zones were delineated after two years (2016 and 2017) of yield and evapotranspiration (ET) data collection. Soil sensors were placed within each zone to give real time data of VWC values and assist in irrigation decisions within a 23 ha field of winter wheat (Triticum aestivum ‘UI Magic’) near Grace, Idaho, USA (2019). Cumulative irrigation rates among zones ranged from 236 to 298 mm. Although a statistical comparison could not be made, the irrigation rates were 0.6 to 21% less than an estimated uniform grower standard practice (GSP) irrigation approach. Based on soil sensor data, crop water stress was avoided with VRI management in all but Zone 3. Thus, this simple approach to VRI zone delineation and VWC monitoring has the potential to reduce irrigation, such as this study, on average by 12% and should be evaluated in other site-years to assess its viability. Full article

(This article belongs to the Special Issue The Importance of Soil Spatial Variability in Precision Agriculture)

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19 pages, 5747 KiB

Open AccessArticle

Using Soil, Plant, Topographic and Remotely Sensed Data to Determine the Best Method for Defining Aflatoxin Contamination Risk Zones within Fields for Precision Management

by Ruth Kerry, Ben Ingram, Brenda V. Ortiz and Arnold Salvacion

Agronomy 2022, 12(10), 2524; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy12102524 - 16 Oct 2022

Cited by 2 | Viewed by 1405

Abstract

Contamination of crops by aflatoxins (AFs) is a real risk in the South-Eastern USA. Contamination risk at the county level based on soil type and weather in different years has been investigated. However, defining AFs contamination risk zones within fields has not yet [...] Read more.

Contamination of crops by aflatoxins (AFs) is a real risk in the South-Eastern USA. Contamination risk at the county level based on soil type and weather in different years has been investigated. However, defining AFs contamination risk zones within fields has not yet been attempted. Drought conditions, particularly within the month of June have been linked to high levels of AFs contamination at the county level. Soil characteristics and topography are the factors influencing drought status that vary most within fields. Here, soil, plant, topography and remotely sensed information are used to define AFs contamination risk zones within two fields using different approaches. Normalized difference vegetation index (NDVI) data were used to indicate potential droughty areas and thermal IR data from LandSat imagery were used to identify hot areas. Topographic variables were also computed. Comparison tests showed that a combination of regression analysis of soil, plant and imagery data and bi-variate local Moran’s I analysis of NDVI and Thermal IR data from several years was the best way to define zones for mean and maximum AFs levels. An approach based on principal components analysis of soil, plant and imagery data from 2010, a high-risk year, was best for defining zones for minimum AFs levels. Analysis of imagery from several years suggested that the zones are likely to be relatively stable in time and could be defined using only freely available sensor, topographic and soil series data. Once defined, such zones can be managed to increase profitability and reduce waste. Full article

(This article belongs to the Special Issue The Importance of Soil Spatial Variability in Precision Agriculture)

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