Publications by Colleges and Departments (MSU - Bozeman)
Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/3
Browse
3 results
Search Results
Item Sentinel-2-based predictions of soil depth to inform water and nutrient retention strategies in dryland wheat(Elsevier BV, 2023-11) Fordyce, Simon I.; Carr, Patrick M.; Jones, Clain; Eberly, Jed O.; Sigler, W. Adam; Ewing, Stephanie; Powell, Scott L.The thickness or depth of fine-textured soil (zf) dominates water storage capacity and exerts a control on nutrient leaching in semi-arid agroecosystems. At small pixel sizes (< 1 m; ‘fine resolution’), the normalized difference vegetation index (NDVI) of cereal crops during senescence (Zadoks Growth Stages [ZGS] 90–93) offers a promising alternative to destructive sampling of zf using soil pits. However, it is unclear whether correlations between zf and NDVI exist (a) at larger pixel sizes (1–10 m; ‘intermediate resolution’) and (b) across field boundaries. The relationship of zf to NDVI of wheat (Triticum aestivum L.) was tested using images from a combination of multispectral sensors and fields in central Montana. NDVI was derived for one field using sensors of fine and intermediate spatial resolution and for three fields using intermediate resolution sensors only. Among images acquired during crop senescence, zf was correlated with NDVI (p < 0.05) independent of sensor (p = 0.22) and field (p = 0.94). The zf relationship to NDVI was highly dependent on acquisition day (p < 0.05), but only when pre-senescence (ZGS ≤ 89) images were included in the analysis. Results indicate that cereal crop NDVI of intermediate resolution can be used to characterize zf across field boundaries if image acquisition occurs during crop senescence. Based on these findings, an empirical index was derived from multi-temporal Sentinel-2 imagery to estimate zf on fields in and beyond the study area.Item Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network Trained via Remote Sensing(MDPI AG, 2023-01) Morales, Giorgio; Sheppard, John W.; Hedgedus, Paul B.; Maxwell, Bruce D.In recent years, the use of remotely sensed and on-ground observations of crop fields, in conjunction with machine learning techniques, has led to highly accurate crop yield estimations. In this work, we propose to further improve the yield prediction task by using Convolutional Neural Networks (CNNs) given their unique ability to exploit the spatial information of small regions of the field. We present a novel CNN architecture called Hyper3DNetReg that takes in a multi-channel input raster and, unlike previous approaches, outputs a two-dimensional raster, where each output pixel represents the predicted yield value of the corresponding input pixel. Our proposed method then generates a yield prediction map by aggregating the overlapping yield prediction patches obtained throughout the field. Our data consist of a set of eight rasterized remotely-sensed features: nitrogen rate applied, precipitation, slope, elevation, topographic position index (TPI), aspect, and two radar backscatter coefficients acquired from the Sentinel-1 satellites. We use data collected during the early stage of the winter wheat growing season (March) to predict yield values during the harvest season (August). We present leave-one-out cross-validation experiments for rain-fed winter wheat over four fields and show that our proposed methodology produces better predictions than five compared methods, including Bayesian multiple linear regression, standard multiple linear regression, random forest, an ensemble of feedforward networks using AdaBoost, a stacked autoencoder, and two other CNN architectures.Item Precision Agroecology(MDPI AG, 2021-12) Duff, Hannah; Hegedus, Paul B.; Loewen, Sasha; Bass, Thomas; Maxwell, Bruce D.In response to global calls for sustainable food production, we identify two diverging paradigms to address the future of agriculture. We explore the possibility of uniting these two seemingly diverging paradigms of production-oriented and ecologically oriented agriculture in the form of precision agroecology. Merging precision agriculture technology and agroecological principles offers a unique array of solutions driven by data collection, experimentation, and decision support tools. We show how the synthesis of precision technology and agroecological principles results in a new agriculture that can be transformative by (1) reducing inputs with optimized prescriptions, (2) substituting sustainable inputs by using site-specific variable rate technology, (3) incorporating beneficial biodiversity into agroecosystems with precision conservation technology, (4) reconnecting producers and consumers through value-based food chains, and (5) building a just and equitable global food system informed by data-driven food policy. As a result, precision agroecology provides a unique opportunity to synthesize traditional knowledge and novel technology to transform food systems. In doing so, precision agroecology can offer solutions to agriculture’s biggest challenges in achieving sustainability in a major state of global change.