Using multivariate adaptive regression splines (MARS) to identify relationships between soil and corn (<i>Zea mays</i> L.) production properties

Turpin, K.M.; Lapen, David R.; Gregorich, E.G.; Topp, G. C.; Edwards, M. H.; McLaughlin, Neil; Curnoe, W. E.; Robin, Michel

doi:10.4141/s04-062

Cited by 10 publications

(10 citation statements)

References 16 publications

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“…Based on the reported importance of micro-topographic attributes on crop growth properties [18,19,23], we included Lidar-derived variables to represent the micro-topography of the fields over this low topographic relief study area. Lidar data were collected over the experimental site by GeoDigital (Sandy Springs, GA) in November 2011, and a 1-m spatial resolution digital terrain elevation dataset was derived from the Lidar point cloud using LAStools (rapidlasso GmbH, Gilching, Germany).…”

Section: Lidar and Topographic Derivativesmentioning

confidence: 99%

“…At this scale, our results showed the importance of variables related to the canopy and surface structure, the SR index and slope, respectively. These structural variables contain implicit information on spatial patterns of soil, water and nutrient distributions [23], which can ultimately affect crop yield. In 2011, for example, the ANN models underestimated soybean yield in most of the eastern fields of the study area.…”

Section: Predicted Within-field Corn and Soybean Yieldsmentioning

confidence: 99%

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Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields

et al. 2020

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Crop yield prediction prior to harvest is important for crop income and insurance projections, and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and predictor variables, especially at the field scale. In this study, an artificial neural network (ANN) method was used: (1) to evaluate the relative importance of predictor variables for the prediction of within-field corn and soybean end-of-season yield and (2) to evaluate the performance of the ANN models with a minimal optimized variable dataset for their capacity to predict corn and soybean yield over multiple years at the within-field level. Several satellite derived vegetation indices (normalized difference vegetation index—NDVI, red edge NDVI and simple ratio—SR) and elevation derived variables (slope, flow accumulation, aspect) were used as crop yield predictor variables, hypothesizing that the different variables reflect different crop and site conditions. The study identified the SR index and the slope as the most important predictor variables for both crop types during two training and testing years (2011, 2012). The dates of the most important SR images, however, were different for the two crop types and corresponded to their critical crop developmental stages (phenology). The relative mean absolute errors were overall smaller for corn compared to soybean: all of the 2011 corn study fields had errors below 10%; 75% of the fields had errors below 10% in 2012. The errors were more variable for soybean. In 2011, 37% of the fields had errors below 10%, while in 2012, 100% of the fields had errors below 20%. The results are promising and can provide yield estimates at the farm level, which could be useful in refining broader scale (e.g., county, region) yield projections.

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Section: Lidar and Topographic Derivativesmentioning

confidence: 99%

Section: Predicted Within-field Corn and Soybean Yieldsmentioning

confidence: 99%

Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields

et al. 2020

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“…A spline is a utility defined piecewise by polynomials, and it is applied to a class of functions used in input-output data interpolation [28]. A common spline function is the cubic spline or knote [43].…”

Section: Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 99%

“…First, the forward algorithm chooses all possible fundamental functions and their related knots. Second, the backward algorithm gets rid of all basic functions in order to produce the best combinations of existing knots, and, finally, the smoothing operation is performed to obtain continuous partition borders [43].…”

Section: Multivariate Adaptive Regression Splines (Mars)mentioning

confidence: 99%

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Mustafa

Liu

et al. 2020

Advances in Civil Engineering

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The soft computing models used for predicting land surface temperature (LST) changes are very useful to evaluate and forecast the rapidly changing climate of the world. In this study, four soft computing techniques, namely, multivariate adaptive regression splines (MARS), wavelet neural network (WNN), adaptive neurofuzzy inference system (ANFIS), and dynamic evolving neurofuzzy inference system (DENFIS), are applied and compared to find the best model that can be used to predict the LST changes of Beijing area. The topographic change is considered in this study to accurately predict LST; furthermore, Landsat 4/5 TM and Landsat 8OLI_TIRS images for four years (1995, 2004, 2010, and 2015) are used to study the LST changes of the research area. The four models are assessed using statistical analysis, coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) in the training and testing stages, and MARS is used to estimate the important variables that should be considered in the design models. The results show that the LST for the studied area increases by 0.28°C/year due to the urban changes in the study area. In addition, the topographic changes and previously recorded temperature changes have a significant influence on the LST prediction of the study area. Moreover, the results of the models show that the MARS, ANFIS, and DENFIS models can be used to predict the LST of the study area. The ANFIS model showed the highest performances in the training (R2 = 0.99, RMSE = 0.78°C, MAE = 0.55°C) and testing (R2 = 0.99, RMSE = 0.36°C, MAE = 0.16°C) stages; therefore, the ANFIS model can be used to predict the LST changes in the Beijing area. The predicted LST shows that the change in climate and urban area will affect the LST changes of the Beijing area in the future.

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“…Multivariate Adaptive Regression Spline (MARS) approach has also proved to be an important methodology to identify and correlate yield and variables impacting crop production (Emamgolizadeh et al, 2015;Zabihi et al, 2019). Turpin et al (2005) has identified dominant factors affecting corn establishment and grain yields using MARS automated regression data mining method. Their study concluded that soil water content and cone penetration resistance are key parameters than elevation and mineralizable soil nitrogen in predicting crop establishment and grain yield.…”

Section: Learning) With Modern Agricultural Technologymentioning

confidence: 99%

Regenerative agriculture and integrative permaculture for sustainable and technology driven global food production and security

et al. 2021

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Intensive agriculture can cause a loss in biodiversity and concerns for global food security. Regenerative agriculture and integrative permaculture are keys to address food security. Digital agricultural tools can aid in attaining agricultural and environmental sustainability. Precision agriculture should be implemented as an agricultural decision support system. Artificial intelligence and machine learning can guide integrated agricultural input management.

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Using multivariate adaptive regression splines (MARS) to identify relationships between soil and corn (Zea mays L.) production properties

Cited by 10 publications

References 16 publications

Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields

Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields

Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

Regenerative agriculture and integrative permaculture for sustainable and technology driven global food production and security

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