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Timely and accurate monitoring of tree crop extent and productivities are necessary for informing policy-making and investments. However, except for a very few tree species (e.g., oil palms) with obvious canopy and extensive planting, most small-crown tree crops are understudied in the remote sensing domain. To conduct large-scale small-crown tree mapping, several key questions remain to be answered, such as the choice of satellite imagery with different spatial and temporal resolution and model generalizability. In this study, we use olive trees in Morocco as an example to explore the two abovementioned questions in mapping small-crown orchard trees using 0.5 m DigitalGlobe (DG) and 3 m Planet imagery and deep learning (DL) techniques. Results show that compared to DG imagery whose mean overall accuracy (OA) can reach 0.94 and 0.92 in two climatic regions, Planet imagery has limited capacity to detect olive orchards even with multi-temporal information. The temporal information of Planet only helps when enough spatial features can be captured, e.g., when olives are with large crown sizes (e.g., >3 m) and small tree spacings (e.g., <3 m). Regarding model generalizability, experiments with DG imagery show a decrease in F1 score up to 5% and OA to 4% when transferring models to new regions with distribution shift in the feature space. Findings from this study can serve as a practical reference for many other similar mapping tasks (e.g., nuts and citrus) around the world.
Chenxi Lin; Zhenong Jin; David Mulla; Rahul Ghosh; Kaiyu Guan; Vipin Kumar; Yaping Cai. Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco. Remote Sensing 2021, 13, 1740 .
AMA StyleChenxi Lin, Zhenong Jin, David Mulla, Rahul Ghosh, Kaiyu Guan, Vipin Kumar, Yaping Cai. Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco. Remote Sensing. 2021; 13 (9):1740.
Chicago/Turabian StyleChenxi Lin; Zhenong Jin; David Mulla; Rahul Ghosh; Kaiyu Guan; Vipin Kumar; Yaping Cai. 2021. "Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco." Remote Sensing 13, no. 9: 1740.
Harmful algal blooms (HABs) and the high biomass associated with them have afflicted marine desalination plants along coastal regions around the world. Few studies of HABs have been conducted in the Red Sea, where desalination plants along the Saudi Arabian Red Sea coast provide drinking water for millions of people. This study was conducted along the Saudi Arabian Red Sea coast from 2014 to 2015 to assess the potential for using Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing of chlorophyll a (Chl a) or fluorescence line height (FLH) to identify risks for biofouling at these desalination plants. Ship-based surveys of phytoplankton were conducted along the Saudi Arabian coastline offshore of desalination plants at Jeddah, Al Shoaibah and Al Qunfudhuh to assess the density of phytoplankton populations and identify any potential HAB species. Ship-based surveys showed low to moderate concentrations of phytoplankton, averaging from 1800–10,000 cells L−1 at Jeddah, 2000–11,000 cells L−1 at Al Shoaibah and 1000–20,500 cells L−1 at Al Qunfudhuh. Sixteen different species of potentially toxigenic HABs were identified through these surveys. There was a good relationship between ship-based total phytoplankton counts and monthly averaged coastal MODIS Chl a (R2 = 0.49, root mean square error (RMSE) = 0.27 mg m−3) or FLH (R2 = 0.47, RMSE = 0.04 mW m−2 µm−1 sr−1) values. Monthly average near shore Chl a concentrations obtained using MODIS satellite imagery were much higher in the Red Sea coastal areas at Al Qunfudhuh (maximum of about 1.3 mg m−3) than at Jeddah or Al Shoaibah (maximum of about 0.4 and 0.5 mg m−3, respectively). Chlorophyll a concentrations were generally highest from the months of December to March, producing higher risks of biofouling desalination plants than in other months. Concentrations decreased significantly, on average, from April to September. Long-term (2005–2016) monthly averaged MODIS Chl a values were used to delineate four statistically distinct zones of differing HAB biomass across the entire Red Sea. Sinusoidal functions representing monthly variability were fit to satellite Chl a values in each zone (RMSE values from 0.691 to 0.07 mg m−3, from Zone 1 to 4). December to January mean values and annual amplitudes for Chl a in these four sinusoidal functions decreased from Zones 1–4. In general, the greatest risk of HABs to desalination occurs during winter months in Zone 1 (Southern Red Sea), while HAB risks to desalination plants in winter months are low to moderate in Zone 2 (South Central Red Sea), and negligible in Zones 3 (North Central) and 4 (Northern).
M. N. Gomaa; D. J. Mulla; J. C. Galzki; K. M. Sheikho; N. M. Alhazmi; H. E. Mohamed; I. Hannachi; A. M. Abouwarda; E. A. Hassan; W. W. Carmichael. Red Sea MODIS Estimates of Chlorophyll a and Phytoplankton Biomass Risks to Saudi Arabian Coastal Desalination Plants. Journal of Marine Science and Engineering 2020, 9, 11 .
AMA StyleM. N. Gomaa, D. J. Mulla, J. C. Galzki, K. M. Sheikho, N. M. Alhazmi, H. E. Mohamed, I. Hannachi, A. M. Abouwarda, E. A. Hassan, W. W. Carmichael. Red Sea MODIS Estimates of Chlorophyll a and Phytoplankton Biomass Risks to Saudi Arabian Coastal Desalination Plants. Journal of Marine Science and Engineering. 2020; 9 (1):11.
Chicago/Turabian StyleM. N. Gomaa; D. J. Mulla; J. C. Galzki; K. M. Sheikho; N. M. Alhazmi; H. E. Mohamed; I. Hannachi; A. M. Abouwarda; E. A. Hassan; W. W. Carmichael. 2020. "Red Sea MODIS Estimates of Chlorophyll a and Phytoplankton Biomass Risks to Saudi Arabian Coastal Desalination Plants." Journal of Marine Science and Engineering 9, no. 1: 11.
In this study, hydrogeochemical analyses were combined with geographic information system (GIS) tools to investigate salinization sources of groundwater in the downstream part of the Essaouira basin, and to analyze the spatiotemporal trends in groundwater quality. To assess groundwater suitability for drinking purposes, the quality of sampled water was compared with the World Health Organization (WHO) and the Moroccan guidelines. Wilcox and US salinity laboratory (USSL) diagrams were used to evaluate groundwater suitability for irrigation. Hydrogeochemical analyses revealed that groundwater is of Na-Cl and Ca-Mg-Cl types. The analyses of the correlation between the chemical elements showed that the water–rock interaction and the reverse ion exchange are the major processes impacting groundwater degradation in the study area. The study of groundwater suitability for drinking and irrigation purposes shows that groundwater quality in the study area is permissible, but not desirable for human consumption. Additionally, groundwater is permissible for agricultural use but with high-salinity hazards. The spatial distribution of the physicochemical elements shows a general upward gradient from the north to the south and from the east to the west. The trend in groundwater quality during the last five years shows a shifting in the quality from the mixed Ca-Mg-Cl to the Na-Cl type.
Mohamed Ouarani; Mohammed Bahir; David J. Mulla; Driss Ouazar; Abdelghani Chehbouni; Driss Dhiba; Salah Ouhamdouch; Otman El Mountassir. Groundwater Quality Characterization in an Overallocated Semi-Arid Coastal Area Using an Integrated Approach: Case of the Essaouira Basin, Morocco. Water 2020, 12, 3202 .
AMA StyleMohamed Ouarani, Mohammed Bahir, David J. Mulla, Driss Ouazar, Abdelghani Chehbouni, Driss Dhiba, Salah Ouhamdouch, Otman El Mountassir. Groundwater Quality Characterization in an Overallocated Semi-Arid Coastal Area Using an Integrated Approach: Case of the Essaouira Basin, Morocco. Water. 2020; 12 (11):3202.
Chicago/Turabian StyleMohamed Ouarani; Mohammed Bahir; David J. Mulla; Driss Ouazar; Abdelghani Chehbouni; Driss Dhiba; Salah Ouhamdouch; Otman El Mountassir. 2020. "Groundwater Quality Characterization in an Overallocated Semi-Arid Coastal Area Using an Integrated Approach: Case of the Essaouira Basin, Morocco." Water 12, no. 11: 3202.
The dynamic interactions between soil, weather and crop management have considerable influences on crop yield within a region, and should be considered in optimizing nitrogen (N) management. The objectives of this study were to determine the influence of soil type, weather conditions and planting density on economic optimal N rate (EONR), and to evaluate the potential benefits of site-specific N management strategies for maize production. The experiments were conducted in two soil types (black and aeolian sandy soils) from 2015 to 2017, involving different N rates (0 to 300 kg ha−1) with three planting densities (55,000, 70,000, and 85,000 plant ha−1) in Northeast China. The results showed that the average EONR was higher in black soil (265 kg ha−1) than in aeolian sandy soil (186 kg ha−1). Conversely, EONR showed higher variability in aeolian sandy soil (coefficient of variation (CV) = 30%) than in black soil (CV = 10%) across different weather conditions and planting densities. Compared with farmer N rate (FNR), applying soil-specific EONR (SS-EONR), soil- and year-specific EONR (SYS-EONR) and soil-, year-, and planting density-specific EONR (SYDS-EONR) would significantly reduce N rate by 25%, 30% and 38%, increase net return (NR) by 155 $ ha−1, 176 $ ha−1, and 163 $ ha−1, and improve N use efficiency (NUE) by 37–42%, 52%, and 67–71% across site-years, respectively. Compared with regional optimal N rate (RONR), applying SS-EONR, SYS-EONR and SYDS-EONR would significantly reduce N application rate by 6%, 12%, and 22%, while increasing NUE by 7–8%, 16–19% and 28–34% without significantly affecting yield or NR, respectively. It is concluded that soil-specific N management has the potential to improve maize NUE compared with both farmer practice and regional optimal N management in Northeast China, especially when each year’s weather condition and planting density information is also considered. More studies are needed to develop practical in-season soil (site)-specific N management strategies using crop sensing and modeling technologies to better account for soil, weather and planting density variation under diverse on-farm conditions.
Xinbing Wang; Yuxin Miao; Rui Dong; Zhichao Chen; Krzysztof Kusnierek; Guohua Mi; David J. Mulla. Economic Optimal Nitrogen Rate Variability of Maize in Response to Soil and Weather Conditions: Implications for Site-Specific Nitrogen Management. Agronomy 2020, 10, 1237 .
AMA StyleXinbing Wang, Yuxin Miao, Rui Dong, Zhichao Chen, Krzysztof Kusnierek, Guohua Mi, David J. Mulla. Economic Optimal Nitrogen Rate Variability of Maize in Response to Soil and Weather Conditions: Implications for Site-Specific Nitrogen Management. Agronomy. 2020; 10 (9):1237.
Chicago/Turabian StyleXinbing Wang; Yuxin Miao; Rui Dong; Zhichao Chen; Krzysztof Kusnierek; Guohua Mi; David J. Mulla. 2020. "Economic Optimal Nitrogen Rate Variability of Maize in Response to Soil and Weather Conditions: Implications for Site-Specific Nitrogen Management." Agronomy 10, no. 9: 1237.
Fertilizer management practices which focus on applying nitrogen (N) fertilizer at the right rate and time have been proposed as a practical option to reduce nitrate‐N losses from subsurface drained agricultural fields. In this study, regression equations were developed to predict nitrate‐N losses for a corn (Zea mays L .) and soybean (Glycine max L.) rotation in southern Minnesota, using fertilizer application timing and rate, and growing season precipitation as inputs. The equations were developed using the results of the field‐scale hydrologic and N simulation model DRAINMOD‐NII, first calibrated and validated for three sites in southern Minnesota, and then run with different combinations of N fertilizer application rates and timings. Fertilizer timing treatments included a single application in the fall or spring, or a split‐spring application (half applied pre‐plant and the remaining applied as side‐dress). The predictive regression equations showed the split fertilizer application timing could reduce regional N loads by 28% compared to spring and fall applications. Greater reductions were predicted when the split timing was combined with lower N fertilizer rates. Utilizing the split application timing and reducing fertilizer rate by 10% and 30%, showed a 33% and 41% reduction in N loads respectively, compared to current fertilizer management practices. Such reductions in fertilizer application rates could be achieved through use of variable‐rate nitrogen (VRN) fertilizer technologies. Results of this modelling study indicate that synchronizing fertilizer application with crop requirements and utilizing VRN technologies, could significantly reduce N loads to surface waters in southern Minnesota. This article is protected by copyright. All rights reserved
Grace L. Wilson; David J. Mulla; Jeffrey A. Vetsch; Gary R. Sands. Predicting nitrate‐nitrogen loads in subsurface drainage as a function of fertilizer application rate and timing in southern Minnesota. Journal of Environmental Quality 2020, 49, 1347 -1358.
AMA StyleGrace L. Wilson, David J. Mulla, Jeffrey A. Vetsch, Gary R. Sands. Predicting nitrate‐nitrogen loads in subsurface drainage as a function of fertilizer application rate and timing in southern Minnesota. Journal of Environmental Quality. 2020; 49 (5):1347-1358.
Chicago/Turabian StyleGrace L. Wilson; David J. Mulla; Jeffrey A. Vetsch; Gary R. Sands. 2020. "Predicting nitrate‐nitrogen loads in subsurface drainage as a function of fertilizer application rate and timing in southern Minnesota." Journal of Environmental Quality 49, no. 5: 1347-1358.
The ability to predict spatially explicit nitrogen uptake (NUP) in maize (Zea mays L.) during the early development stages provides clear value for making in-season nitrogen fertilizer applications that can improve NUP efficiency and reduce the risk of nitrogen loss to the environment. Aerial hyperspectral imaging is an attractive agronomic research tool for its ability to capture spectral data over relatively large areas, enabling its use for predicting NUP at the field scale. The overarching goal of this work was to use supervised learning regression algorithms—Lasso, support vector regression (SVR), random forest, and partial least squares regression (PLSR)—to predict early season (i.e., V6–V14) maize NUP at three experimental sites in Minnesota using high-resolution hyperspectral imagery. In addition to the spectral features offered by hyperspectral imaging, the 10th percentile Modified Chlorophyll Absorption Ratio Index Improved (MCARI2) was made available to the learning models as an auxiliary feature to assess its ability to improve NUP prediction accuracy. The trained models demonstrated robustness by maintaining satisfactory prediction accuracy across locations, pixel sizes, development stages, and a broad range of NUP values (4.8 to 182 kg ha−1). Using the four most informative spectral features in addition to the auxiliary feature, the mean absolute error (MAE) of Lasso, SVR, and PLSR models (9.4, 9.7, and 9.5 kg ha−1, respectively) was lower than that of random forest (11.2 kg ha−1). The relative MAE for the Lasso, SVR, PLSR, and random forest models was 16.5%, 17.0%, 16.6%, and 19.6%, respectively. The inclusion of the auxiliary feature not only improved overall prediction accuracy by 1.6 kg ha−1 (14%) across all models, but it also reduced the number of input features required to reach optimal performance. The variance of predicted NUP increased as the measured NUP increased (MAE of the Lasso model increased from 4.0 to 12.1 kg ha−1 for measured NUP less than 25 kg ha−1 and greater than 100 kg ha−1, respectively). The most influential spectral features were oftentimes adjacent to each other (i.e., within approximately 6 nm), indicating the importance of both spectral precision and derivative spectra around key wavelengths for explaining NUP. Finally, several challenges and opportunities are discussed regarding the use of these results in the context of improving nitrogen fertilizer management.
Tyler Nigon; Ce Yang; Gabriel Dias Paiao; David Mulla; Joseph Knight; Fabián Fernández. Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery. Remote Sensing 2020, 12, 1234 .
AMA StyleTyler Nigon, Ce Yang, Gabriel Dias Paiao, David Mulla, Joseph Knight, Fabián Fernández. Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery. Remote Sensing. 2020; 12 (8):1234.
Chicago/Turabian StyleTyler Nigon; Ce Yang; Gabriel Dias Paiao; David Mulla; Joseph Knight; Fabián Fernández. 2020. "Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery." Remote Sensing 12, no. 8: 1234.
Nitrogen (N) loss from cropping systems has important environmental implications, including contamination of drinking water with nitrate. A 2‐yr study evaluated the effects of six N rate, source, and timing treatments, including a variable rate (VR) N treatment based on the N sufficiency index approach using remote sensing, and two irrigation rate treatments, including conventional and reduced rate, on nitrate leaching, residual soil nitrate, and plant N uptake for potato (Solanum tuberosum L. cv. Russet Burbank) production in 2016 and 2017 on a Hubbard loamy sand. Nitrate leaching losses measured with suction‐cup lysimeters varied between 2016 and 2017 with flow‐weighted mean nitrate N concentrations of 5.6 and 12.8 mg N L−1, respectively, and increased from 7.1 to 10.4 mg N L−1 as N rate increased from 45 to 270 kg N ha−1. Despite reductions in N rate of 22 and 44 kg N ha−1 in 2016 and 2017, respectively, for the VR N treatment, there was no significant difference in nitrate leaching compared with the existing N best management practices (BMPs). Reducing irrigation rate by 15% decreased nitrate leaching load by 17% through a reduction in percolation. Residual soil nitrate N in the top 60 cm across all treatments (7.9 mg N kg−1) suggests a risk for nitrate leaching during the nongrowing season, and plant N uptake did not explain yearly variation in nitrate leaching and residual soil nitrate. Although existing N BMPs are effective at controlling N losses, development of alternative practices is needed to further reduce the risk of groundwater contamination. This article is protected by copyright. All rights reserved
Brian J. Bohman; Carl J. Rosen; David J. Mulla. Impact of variable rate nitrogen and reduced irrigation management on nitrate leaching for potato. Journal of Environmental Quality 2020, 49, 281 -291.
AMA StyleBrian J. Bohman, Carl J. Rosen, David J. Mulla. Impact of variable rate nitrogen and reduced irrigation management on nitrate leaching for potato. Journal of Environmental Quality. 2020; 49 (2):281-291.
Chicago/Turabian StyleBrian J. Bohman; Carl J. Rosen; David J. Mulla. 2020. "Impact of variable rate nitrogen and reduced irrigation management on nitrate leaching for potato." Journal of Environmental Quality 49, no. 2: 281-291.
Woodchip denitrifying bioreactors (WDBR) reduce off‐field tile drainage nitrogen (N) losses from agricultural fields. Limited evaluation exists regarding the influence of flow direction through WDBRs. Changing flow direction could reduce short circuiting. This study evaluated the dependency of nitrate‐N removal and dissolved nitrous oxide (d N2O) production rates on vertical flow direction in triplicate column bioreactors at 12‐h (without carbon dosing) and 2‐h (with carbon dosing) hydraulic residence times. Results presented demonstrate that there was no significant difference in overall N removal rates from these column bioreactors as a function of flow direction. There was the suggestion of lower N2O production in the downflow direction, although this was not statistically significant due to the high variability of the N2O production observed in the upflow direction. Carbon addition led to bioclogging of downflow columns; future work needs to identify dosing rate, placement, and conditions that prevent biofilm formation.
Gary W. Feyereisen; Kurt A. Spokas; Jeffrey S. Strock; David J. Mulla; Andry Z. Ranaivoson; Jeffrey A. Coulter. Nitrate removal and nitrous oxide production from upflow and downflow column woodchip bioreactors. Agricultural & Environmental Letters 2020, 5, 1 .
AMA StyleGary W. Feyereisen, Kurt A. Spokas, Jeffrey S. Strock, David J. Mulla, Andry Z. Ranaivoson, Jeffrey A. Coulter. Nitrate removal and nitrous oxide production from upflow and downflow column woodchip bioreactors. Agricultural & Environmental Letters. 2020; 5 (1):1.
Chicago/Turabian StyleGary W. Feyereisen; Kurt A. Spokas; Jeffrey S. Strock; David J. Mulla; Andry Z. Ranaivoson; Jeffrey A. Coulter. 2020. "Nitrate removal and nitrous oxide production from upflow and downflow column woodchip bioreactors." Agricultural & Environmental Letters 5, no. 1: 1.
High resolution RGB imagery collected using a UAV and a handheld camera was used with structure from motion to reconstruct 3D canopies of small groups of corn plants. A methodology for the automated extraction of phenotypic characteristics of individual plants is presented based on these 3D reconstructed canopies. Such information can enhance the evaluation of crop traits and provide accurate and frequent statistics for in-season assessment of their changes with growth stage. Industries that target yield optimization and crop hybrid production can benefit greatly from this approach. The use of 3D models provides elevated information content, when compared to alternative planar methods, mainly due to the alleviation of leaf occlusions. High resolution images of corn stalks are collected and used to obtain 3D models for individual plants. Based on those extracted 3D point clouds, the calculation of phenotypic characteristics are obtained, such as the number of plants in an area, the Leaf Area Index (LAI), the individual and average plant height, the individual leaf length, the location and the angles of leaves with respect to the stem. An experimental validation using both artificial corn plants emulating real world scenarios and real corn plants in different growth stages, supports the accuracy of the proposed methodology. Our experiments conclude that phenotypic characteristics of individual plants can be extracted automatically with high accuracy based on a 3D model. The results include the individual plant segmentation and counting from a given 3D reconstructed field scene with 88.1% accuracy, the Leaf Area Index (LAI) estimation with 92.5% accuracy, the individual plant height computation with 89.2% accuracy, the leaf length extraction with 74.8% accuracy, the measurement of angles between leaves and stems, and the distance between the leaves of the same plant. We interpret the last two variables qualitatively to show that the method can show the trend of the angles to change with respect to the leaf position on the stem as the crops grow.
Dimitris Zermas; Vassilios Morellas; David Mulla; Nikos Papanikolopoulos. 3D model processing for high throughput phenotype extraction – the case of corn. Computers and Electronics in Agriculture 2019, 172, 105047 .
AMA StyleDimitris Zermas, Vassilios Morellas, David Mulla, Nikos Papanikolopoulos. 3D model processing for high throughput phenotype extraction – the case of corn. Computers and Electronics in Agriculture. 2019; 172 ():105047.
Chicago/Turabian StyleDimitris Zermas; Vassilios Morellas; David Mulla; Nikos Papanikolopoulos. 2019. "3D model processing for high throughput phenotype extraction – the case of corn." Computers and Electronics in Agriculture 172, no. : 105047.
A. Bchir; D.J. Mulla; A. Ben Dhiab; F. Ben Meriem; W. Bousetta; M. Braham. Assessing spatial and temporal variability in evapotranspiration for olive orchards in Tunisia using satellite remote sensing. Precision agriculture ’19 2019, 1 .
AMA StyleA. Bchir, D.J. Mulla, A. Ben Dhiab, F. Ben Meriem, W. Bousetta, M. Braham. Assessing spatial and temporal variability in evapotranspiration for olive orchards in Tunisia using satellite remote sensing. Precision agriculture ’19. 2019; ():1.
Chicago/Turabian StyleA. Bchir; D.J. Mulla; A. Ben Dhiab; F. Ben Meriem; W. Bousetta; M. Braham. 2019. "Assessing spatial and temporal variability in evapotranspiration for olive orchards in Tunisia using satellite remote sensing." Precision agriculture ’19 , no. : 1.
Nitrogen (N) from farm fields is a source of pollution to fresh and marine waters. Modifying N fertilizer application rate and timing to consider the spatial and temporal variability in plant N requirements could reduce N losses from farmlands, resulting in improvements to surface water quality. In this study, the field-scale hydrologic and N simulation model DRAINMOD-NII was used to predict nitrate–N losses from fields planted in a corn-soybean rotation at Waseca, Minnesota, USA, over a 15-year period (2003–2017) for two fertilizer application treatments. The N fertilizer treatments simulated included a single uniform fertilizer application in the spring before planting and a variable rate N practice (VRN) where fertilizer was applied as a split pre-plant, side-dress application, based on in-season monitoring of plant N requirements to determine fertilizer rate. Measured discharge (2003–2008) and nitrate–N concentrations in subsurface drainage (2003–2008 and 2016–2017) at the site were used to calibrate discharge and nitrate–N losses in model simulations and validate model performance for uniform vs VRN fertilizer management. Measured nitrate–N concentrations in weekly samples were 13% lower for fields utilizing VRN versus a single spring application in 2016, and 18% lower in 2017. Model predictions of nitrate concentrations based on daily predictions of discharge accurately matched observed data for these years, predicting reductions of 23% and 19% for the years 2016 and 2017, respectively. The results of model simulation for the 15-year period indicated that changing the timing of fertilizer application from a single application to a VRN application could reduce annual N loads lost in drainage by 40%.
Grace L. Wilson; David J. Mulla; Jake Galzki; Aicam Laacouri; Jeff Vetsch; Gary Sands. Effects of fertilizer timing and variable rate N on nitrate–N losses from a tile drained corn-soybean rotation simulated using DRAINMOD-NII. Precision Agriculture 2019, 21, 311 -323.
AMA StyleGrace L. Wilson, David J. Mulla, Jake Galzki, Aicam Laacouri, Jeff Vetsch, Gary Sands. Effects of fertilizer timing and variable rate N on nitrate–N losses from a tile drained corn-soybean rotation simulated using DRAINMOD-NII. Precision Agriculture. 2019; 21 (2):311-323.
Chicago/Turabian StyleGrace L. Wilson; David J. Mulla; Jake Galzki; Aicam Laacouri; Jeff Vetsch; Gary Sands. 2019. "Effects of fertilizer timing and variable rate N on nitrate–N losses from a tile drained corn-soybean rotation simulated using DRAINMOD-NII." Precision Agriculture 21, no. 2: 311-323.
Precision nitrogen (N) management (PNM) strategies are urgently needed for the sustainability of rain-fed maize (Zea mays L.) production in Northeast China. The objective of this study was to develop an active canopy sensor (ACS)-based PNM strategy for rain-fed maize through improving in-season prediction of yield potential (YP0), response index to side-dress N based on harvested yield (RIHarvest), and side-dress N agronomic efficiency (AENS). Field experiments involving six N rate treatments and three planting densities were conducted in three growing seasons (2015–2017) in two different soil types. A hand-held GreenSeeker sensor was used at V8-9 growth stage to collect normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). The results indicated that NDVI or RVI combined with relative plant height (NDVI*RH or RVI*RH) were more strongly related to YP0 (R2 = 0.44–0.78) than only using NDVI or RVI (R2 = 0.26–0.68). The improved N fertilizer optimization algorithm (INFOA) using in-season predicted AENS optimized N rates better than the N fertilizer optimization algorithm (NFOA) using average constant AENS. The INFOA-based PNM strategies could increase marginal returns by 212 $ ha−1 and 70 $ ha−1, reduce N surplus by 65% and 62%, and improve N use efficiency (NUE) by 4%–40% and 11%–65% compared with farmer’s typical N management in the black and aeolian sandy soils, respectively. It is concluded that the ACS-based PNM strategies have the potential to significantly improve profitability and sustainability of maize production in Northeast China. More studies are needed to further improve N management strategies using more advanced sensing technologies and incorporating weather and soil information.
Xinbing Wang; Yuxin Miao; Rui Dong; Zhichao Chen; Yanjie Guan; Xuezhi Yue; Zheng Fang; David Mulla. Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China. Sustainability 2019, 11, 706 .
AMA StyleXinbing Wang, Yuxin Miao, Rui Dong, Zhichao Chen, Yanjie Guan, Xuezhi Yue, Zheng Fang, David Mulla. Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China. Sustainability. 2019; 11 (3):706.
Chicago/Turabian StyleXinbing Wang; Yuxin Miao; Rui Dong; Zhichao Chen; Yanjie Guan; Xuezhi Yue; Zheng Fang; David Mulla. 2019. "Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China." Sustainability 11, no. 3: 706.
Ravines are a source of sediment loading into surface waters of the Minnesota River Basin (MRB). Ravines formed as the natural product of a landscape adjusting to disequilibrium in the main channel of the MRB caused by a massive glacial flood 11,500 yr ago that lowered the base level of the river channel. Precision conservation techniques are needed to locate and correct ravines that contribute large amounts of sediment. This study uses a geographic information system (GIS) to identify the location of all ravines in the MRB and quantify their spatial distribution, area extent, and connectivity to the mainstem MRB. An analysis of test ravines with 3‐m light detection and ranging (LiDAR) digital elevation model (DEM) was conducted to quantify uncertainty in ravine aerial extent estimates. Ravines could be located with an accuracy of 90% using a GIS algorithm involving slope steepness, flow accumulation, and standard deviation of aspect. Calculations from the GIS algorithm to delineate ravines show that ravines compose a total of 197,830,000 m2 (0.45%) of the basin landscape. Watersheds and agroecoregions along the main channel of the MRB had a greater incidence of ravines than in other locations due to their proximity to the lower base level of the main channel. Statistical and GIS‐based analyses of ravine morphometrics showed that the elevation change from ravine to the main channel of the MRB was strongly correlated with ravine volume (r = 0.64) and relief (r = 0.8); both are characteristics that lead to greater sediment loading from ravines. Thus, ravines located near the main channel tended to be larger and steeper than ravines located farther from the channel. With the techniques developed in this study, conservationists can identify, for the first time, all ravines in the MRB and quantify features that are strongly related to sediment loading, such as volume, area, and relief.
David J. Mulla; Shannon Belmont. Identifying and Characterizing Ravines with GIS Terrain Attributes for Precision Conservation. Agronomy Monographs 2018, 109 -129.
AMA StyleDavid J. Mulla, Shannon Belmont. Identifying and Characterizing Ravines with GIS Terrain Attributes for Precision Conservation. Agronomy Monographs. 2018; ():109-129.
Chicago/Turabian StyleDavid J. Mulla; Shannon Belmont. 2018. "Identifying and Characterizing Ravines with GIS Terrain Attributes for Precision Conservation." Agronomy Monographs , no. : 109-129.
The theoretical concept of a foodshed is nearly a century old, while the tools used to model them—computer software coupled with spatial and statistical datasets—are ever-evolving. In a previous study (Galzki, Mulla, & Peters, 2014), foodshed maps have been created in Southeastern Minnesota that display the potential for local food system capacity in the region. Several assumptions were made based on data and software limitations that make the former results quite theoretical; this study attempts to move those results closer to reality by updating, where relevant. We utilized data produced by a model developed at the University of Minnesota to more effectively estimate regional food expenditures to create a representative diet in the region. We used current land-use data along with site-specific crop yields to analyze the potential food capacity of the region. We used optimization software to allocate food supplies to 53 cities in an attempt to feed all residents in the region and minimize food transportation distances. Improvements in software capacities allowed us to incorporate larger datasets, resulting in more detailed maps and statistics that better represent the potential of local foods in the region. The optimization model indicated the region is capable of sustaining its population entirely on locally derived foods. Each resident can be fed on approximately one-third of a hectare (0.85 acre) of land in the region. The average distance a unit of food travels from farm to grocery store was found to be 15.6 km (9.7 miles). Results also show that 90% of the cultivated land remains in surplus after meeting the food demands of the region, minimizing the impacts on the local agroeconomic system. The surplus of pasture land is smaller, but over half the pasture land in the region is in surplus after food needs are met. We explore an alternative land-use scenario that removes environmentally sensitive cropland from cultivation to illustrate the impact conservation efforts may have on a potential local food system. The updated results of this study bolster the evocative effect of mapping foodsheds and provide a more realistic illustration of how the region could sustain itself on locally derived foods.
Jake C. Galzki; David J. Mulla; Erin Meier. Mapping Potential Foodsheds Using Regionalized Consumer Expenditure Data for Southeastern Minnesota. Journal of Agriculture, Food Systems, and Community Development 2017, 7, 1 -16.
AMA StyleJake C. Galzki, David J. Mulla, Erin Meier. Mapping Potential Foodsheds Using Regionalized Consumer Expenditure Data for Southeastern Minnesota. Journal of Agriculture, Food Systems, and Community Development. 2017; 7 (3):1-16.
Chicago/Turabian StyleJake C. Galzki; David J. Mulla; Erin Meier. 2017. "Mapping Potential Foodsheds Using Regionalized Consumer Expenditure Data for Southeastern Minnesota." Journal of Agriculture, Food Systems, and Community Development 7, no. 3: 1-16.
Demand for agricultural food production is projected to increase dramatically in the coming decades, putting at risk our clean water supply and prospects for sustainable development. Fragmentation-free land allocation (FF-LA) aims to improve returns on ecosystem services by determining both space partitioning of a study area and choice of land-use/land-cover management practice (LMP) for each partition under a budget constraint. In the context of large-scale industrialized food production, fragmentation (e.g., tiny LMP patches) discourages the use of modern farm equipment (e.g., 10- to 20-m-wide combine harvesters) and must be avoided in the allocation. FF-LA is a computationally challenging NP-hard problem. We introduce three frameworks for land allocation planning, namely collaborative geodesign, spatial optimization and a hybrid model of the two, to help stakeholders resolve the dilemma between increasing food production capacity and improving water quality. A detailed case study is carried out at the Seven Mile Creek watershed in the midwestern US. The results show the challenges of generating near-optimal solutions through collaborative geodesign, and the potential benefits of spatial optimization in assisting the decision-making process.
Yiqun Xie; Bryan C. Runck; Shashi Shekhar; Len Kne; David Mulla; Nicolas Jordan; Peter Wiringa. Collaborative Geodesign and Spatial Optimization for Fragmentation-Free Land Allocation. ISPRS International Journal of Geo-Information 2017, 6, 226 .
AMA StyleYiqun Xie, Bryan C. Runck, Shashi Shekhar, Len Kne, David Mulla, Nicolas Jordan, Peter Wiringa. Collaborative Geodesign and Spatial Optimization for Fragmentation-Free Land Allocation. ISPRS International Journal of Geo-Information. 2017; 6 (7):226.
Chicago/Turabian StyleYiqun Xie; Bryan C. Runck; Shashi Shekhar; Len Kne; David Mulla; Nicolas Jordan; Peter Wiringa. 2017. "Collaborative Geodesign and Spatial Optimization for Fragmentation-Free Land Allocation." ISPRS International Journal of Geo-Information 6, no. 7: 226.
David J. Mulla. Spatial Variability in Precision Agriculture. Encyclopedia of GIS 2017, 2118 -2125.
AMA StyleDavid J. Mulla. Spatial Variability in Precision Agriculture. Encyclopedia of GIS. 2017; ():2118-2125.
Chicago/Turabian StyleDavid J. Mulla. 2017. "Spatial Variability in Precision Agriculture." Encyclopedia of GIS , no. : 2118-2125.
Little is known about the effectiveness of sensor-based variable rate nitrogen (VRN) fertilizer application at reducing nitrate-N losses. A variable rate in-season nitrogen (VRN) treatment was compared to a conventional pre-plant uniform rate nitrogen treatment in eight Minnesota, USA sub-fields. Water samples collected weekly during 2016 were analyzed for nitrate-N. The Drainmod model was able to accurately represent measured tile discharge and nitrate-N concentration data. Modeled nitrate-N losses in tile discharge were 16% lower in VRN than uniform treatments. These results show that in-season VRN fertilizer N management can significantly reduce nitrate-N loads from tile drained maize fields.
G. Wilson; A. Laacouri; J. Galzki; D. Mulla. Impacts of Variable Rate Nitrogen (VRN) on Nitrate-N Losses from Tile Drained Maize in Minnesota, USA. Advances in Animal Biosciences 2017, 8, 317 -321.
AMA StyleG. Wilson, A. Laacouri, J. Galzki, D. Mulla. Impacts of Variable Rate Nitrogen (VRN) on Nitrate-N Losses from Tile Drained Maize in Minnesota, USA. Advances in Animal Biosciences. 2017; 8 (2):317-321.
Chicago/Turabian StyleG. Wilson; A. Laacouri; J. Galzki; D. Mulla. 2017. "Impacts of Variable Rate Nitrogen (VRN) on Nitrate-N Losses from Tile Drained Maize in Minnesota, USA." Advances in Animal Biosciences 8, no. 2: 317-321.
Ravines are a source of sediment loading into surface waters of the Minnesota River Basin (MRB). Ravines formed as the natural product of a landscape adjusting to disequilibrium in the main channel of the MRB caused by a massive glacial flood 11,500 yr ago that lowered the base level of the river channel. Precision conservation techniques are needed to locate and correct ravines that contribute large amounts of sediment. This study uses a geographic information system (GIS) to identify the location of all ravines in the MRB and quantify their spatial distribution, area extent, and connectivity to the mainstem MRB. An analysis of test ravines with 3-m light detection and ranging (LiDAR) digital elevation model (DEM) was conducted to quantify uncertainty in ravine aerial extent estimates. Ravines could be located with an accuracy of 90% using a GIS algorithm involving slope steepness, flow accumulation, and standard deviation of aspect. Calculations from the GIS algorithm to delineate ravines show that ravines compose a total of 197,830,000 m2 (0.45%) of the basin landscape. Watersheds and agroecoregions along the main channel of the MRB had a greater incidence of ravines than in other locations due to their proximity to the lower base level of the main channel. Statistical and GIS-based analyses of ravine morphometrics showed that the elevation change from ravine to the main channel of the MRB was strongly correlated with ravine volume (r = 0.64) and relief (r = 0.8); both are characteristics that lead to greater sediment loading from ravines. Thus, ravines located near the main channel tended to be larger and steeper than ravines located farther from the channel. With the techniques developed in this study, conservationists can identify, for the first time, all ravines in the MRB and quantify features that are strongly related to sediment loading, such as volume, area, and relief. Copyright © 2017. . Copyright © American Society of Agronomy and Crop Science Society of America, Inc.
David J. Mulla; Shannon Belmont; J. Delgado; G. Sassenrath; T. Mueller. Identifying and Characterizing Ravines with GIS Terrain Attributes for Precision Conservation. Agronomy Monographs 2017, 1 .
AMA StyleDavid J. Mulla, Shannon Belmont, J. Delgado, G. Sassenrath, T. Mueller. Identifying and Characterizing Ravines with GIS Terrain Attributes for Precision Conservation. Agronomy Monographs. 2017; ():1.
Chicago/Turabian StyleDavid J. Mulla; Shannon Belmont; J. Delgado; G. Sassenrath; T. Mueller. 2017. "Identifying and Characterizing Ravines with GIS Terrain Attributes for Precision Conservation." Agronomy Monographs , no. : 1.
Precision agriculture is one of the most important recent advances in approaches for producing food. Before precision agriculture, farmers uniformly managed their inputs of fertilizer, herbicides, and irrigation, despite spatial variability in soil properties, landscape features, crop stresses and crop yield. After precision agriculture emerged beginning in the 1980s, farmers could divide their heterogeneous fields into smaller units that were relatively homogeneous. These units could then receive customized rates of fertilizer, herbicides, tillage or irrigation, and these rates could vary from one unit to another. In succinct terms, precision agriculture involves putting the right crop inputs at the right rate, in the right place at the right time. The benefits of precision agriculture include more efficient management of farm inputs, improved crop yield or quality, and improved environmental sustainability (better air, water and soil quality). While the concept of ...
David J. Mulla. Spatial Variability in Precision Agriculture. Encyclopedia of GIS 2016, 1 -8.
AMA StyleDavid J. Mulla. Spatial Variability in Precision Agriculture. Encyclopedia of GIS. 2016; ():1-8.
Chicago/Turabian StyleDavid J. Mulla. 2016. "Spatial Variability in Precision Agriculture." Encyclopedia of GIS , no. : 1-8.
Daniel N. Moriasi; Prasanna H. Gowda; Jeffrey G. Arnold; David J. Mulla; Srinivasulu Ale; Jean L. Steiner. Modeling the impact of nitrogen fertilizer application and tile drain configuration on nitrate leaching using SWAT. Agricultural Water Management 2013, 130, 36 -43.
AMA StyleDaniel N. Moriasi, Prasanna H. Gowda, Jeffrey G. Arnold, David J. Mulla, Srinivasulu Ale, Jean L. Steiner. Modeling the impact of nitrogen fertilizer application and tile drain configuration on nitrate leaching using SWAT. Agricultural Water Management. 2013; 130 ():36-43.
Chicago/Turabian StyleDaniel N. Moriasi; Prasanna H. Gowda; Jeffrey G. Arnold; David J. Mulla; Srinivasulu Ale; Jean L. Steiner. 2013. "Modeling the impact of nitrogen fertilizer application and tile drain configuration on nitrate leaching using SWAT." Agricultural Water Management 130, no. : 36-43.