Computers and Electronics in Agriculture, Feb 1, 2020
The spatial distribution of crop yield has been assessed under current and future climate conditi... more The spatial distribution of crop yield has been assessed under current and future climate conditions using gridded crop growth simulations. This task usually requires considerable efforts to prepare input data and postprocess the outputs. In the present study, the Gridded cRop grOWth simuLation suppoRt System (GROWLERS) was developed to automate repetitive and tedious tasks using multiple PCs. In particular, the system was designed to aid researchers who have minimum knowledge on computer programming, network, and cluster management. An object oriented programming language, C++, was used to design and implement the GRO-WLERS, which would increase flexibility of a system while simplifying complexity including supports for different types of gridded data. Functionality of the GROWLERS includes preparation of weather input files, launch of crop growth model, and creation of gridded output files. Tools for the GROWLERS were installed on virtual machines connected through local network, which allows for building of a cluster computer without dedicated workstations. In a case study, 5.8 × 10 7 simulations using the ORYZA2000 model were performed to examine spatial distribution of the optimum sowing date for rice under current climate conditions in Korea. The subsets of these simulations were allocated to groups of virtual machines hosted within five custom built personal computers of which the central processing unit was manufactured about 10 years ago. Weather input data were prepared automatically using the GROWLERS. A set of scripts were also prepared using the GROWLERS, which allowed to reduce the wall clock time by 88% using 16 processor cores for worker nodes. These results suggest that the GROWLERS would minimize researcher's time involved in preparation and operation of a large number of crop growth simulations. Still, the support for nested simulations using multi-scale datasets would be needed to improve the GROWLERS, which merits further development as a next step.
The capacity for food production at the regional level under climate change depends on the natura... more The capacity for food production at the regional level under climate change depends on the natural resources base, and on the management and physical inputs available to farmers. In the Northeast U.S. (12 states, from West Virginia to Maine), more than 200 crops are grown, but yield and land area estimates are available for only a few. Our objective was to use long-term, county-level data from the region to 1) assess the potential for development of a multi-crop yield index, and 2) establish temporal and spatial trends for crops yields that could be incorporated in projections of productive capacit under climate change. County-level yield estimates were obtained for the period 1981-2010, from the USDA-National Agricultural Statistics Service (NASS) annual survey, for corn (Zea maize L.), wheat (Triticum aestivum L.,) and potato (Solanum tuberosum L.), soybean [Glycine max (L.) Merr.], and perennial hay (across categories). The correlation between yields of these crops was low (r <...
A multi-model inter-comparison study was conducted to evaluate the performance of ten potato crop... more A multi-model inter-comparison study was conducted to evaluate the performance of ten potato crop models to accurately predict potato yield in response to elevated CO2 (Ce) when calibrated with ambient CO2 data (Ca). Experimental data from seven open-top chambers (OTC) and free-air−CO2-enrichment (FACE) facilities across continental Europe were used. Model ensemble percent errors averaged over all datasets for simulated yields were 26.5 % for Ca and 27.2 % Ce data. Metrics such as Wilmott’s index of agreement (IA) and root mean square relative error (RMSRE) ranged broadly among individual models and locations, such that four of the ten models outperformed the median or mean of the ensemble for about half of the Ce datasets. These top performing models were representative of three different model structural groups, including radiation use efficiency, transpiration efficiency, or leaf-level based approaches. Relative response to an increase in CO2 was more accurately modeled than absolute yield responses when averaged across all locations, and within 3.3 kg ppm−1 (or 5%) of observed values. Specific targets in the model structure needed for improvement were not identified due to large and inconsistent variation in the accuracy of yield predictions across locations. However, models with the lowest calibration errors tended to be top performers for Ce predictions as well. Such results suggest calibration is at least as important as model structure. Where possible, modelers using potato models to estimate Ce responses should use Ce calibration data to improve confidence in such predictions
The comparison between spatial or temporal patterns is often needed for model evaluation and chan... more The comparison between spatial or temporal patterns is often needed for model evaluation and change detection in ecological studies. The statistics developed for image quality assessment, such as the structural similarity index (SSIM) and the composite similarity measure based on means, standard deviations, and correlation coefficient (CMSC), have been introduced for comparing ecological patterns. However, these measures can be applied only when a positive relationship is expected between patterns having the same scale. We propose a new index, generic composite similarity measure (GCSM), to meet a wide range of potential applications. A set of numerical experiments was performed to illustrate the properties of GCSM in comparison with SSIM and CMSC. Two case studies were conducted examining the (dis)agreement between two products of gross primary production (GPP), and the relative (dis)similarity between GPP and precipitation, respectively. GCSM has advantages over both SSIM and CMSC, including higher sensitivity and the ability to quantify the dissimilarity, which cannot be properly revealed with the latter two indices. The normalization preprocessing constructs universal criteria for assessing the relative (dis)similarity between patterns having unequal scales. The GCSM, overcoming the limitations of preexisting composite measures in quantifying the similarity or dissimilarity between patterns, would aid assessment of heterogeneous relationship between ecological factors over space or time.
Crop yield maps are valuable for many applications in precision agriculture, but are often inacce... more Crop yield maps are valuable for many applications in precision agriculture, but are often inaccessible to growers and researchers wishing to better understand yield determinants and improve site-specific management strategies. A method for mapping sub-field crop yields from remote sensing imagery could increase the availability of crop yield maps. A variation of the scalable crop yield mapping approach (SCYM, Lobell et al. in Remote Sensing of Environment 164:324-333, 2015) was developed and tested for estimating subfield maize (Zea mays L.) yields at 10-30 m without the use of site-specific input data. The method was validated using harvester yield monitor records for 21 site-years for irrigated and rainfed fields in eastern Nebraska, USA. Prediction error ranged greatly across site-years, with relative RMSE scores of 10.8 to 38.5%, and R 2 values of 0.003 to 0.37. Significant proportional bias was detected in the predictions, but could be corrected with a small amount of ground truth data. Crop yield prediction accuracies without calibration were suitable for some precision applications such as mapping relative yields and delineating management zones, but model improvements or calibration datasets are needed for applications requiring absolute yield estimates.
Supply for the fresh strawberry (Fragaria × ananassa Duchesne ex Rozier) market in the U.S. Mid-A... more Supply for the fresh strawberry (Fragaria × ananassa Duchesne ex Rozier) market in the U.S. Mid-Atlantic region is frequently supplemented by product grown in states outside the region. The adoption of repeat-fruiting cultivars specially bred for this area can benefit local and regional growers, but production systems suited to meet the cultural needs of these varieties must be evaluated. The relationships between yields from five repeat-fruiting cultivars grown in either uncovered (open) or plastic-covered (tunnel) raised-bed systems and associated microclimate factors were assessed from data collected over a three-year period at the USDA-ARS facility in Beltsville, Maryland. Average in-season yields were 40% higher and berry numbers were 150% higher for production in tunnel versus open systems when averaged across all cultivars, years, and harvests. This yield difference was attributed to warmer temperatures in the tunnel system that enabled extension of the growing season, higher weekly yields, and higher light use efficiency. Temperature and solar radiation accounted for more than 41% of the variance between yield and all measured microclimatic factors. Maximum 24 h temperatures averaged 3.5°C, 1.6°C, and 0.8°C higher, respectively, at the air (T a), crown (T c), and bed (T b) positions in the tunnel system, and daily photosynthetically active radiation was 34% lower in the tunnel system. A four-week period between floral initiation and fruit maturity was estimated as representative of the floral development period and was used as a basis for obtaining cardinal temperatures. The optimum temperature averaged 26.8°C, 28.6°C, and 27.9°C at T a , T c , and T b , respectively. Yields were positively correlated with daily light integral at temperatures below these thresholds, and higher slopes from the relationship of yield versus light were observed for tunnel production. Predicted yields using the beta function were shown to match observed values well in either production system, especially when T c data were used, and can be used for production system design and management.
The effect of climate change on recent and projected increases in surface temperatures is well-do... more The effect of climate change on recent and projected increases in surface temperatures is well-documented. For agriculture, such changes can impact crop phenology and production, but the degree of impact will depend, in part, on contemporaneous changes in crop management. In the current study, we quantified recent (last 40 years) and projected (to 2095) changes in air temperature and associated changes in growing season duration for rice along a latitudinal north-south gradient of the lower Mississippi valley. Recent and projected climate data indicated an ongoing increase in air temperature and growing season length with latitudes above ∼31°N. We then applied the DD50 growing degree day model to these data to determine if ratooning, a management practice that produces a second rice harvest with minimal resource input, could be employed. The model results were analyzed and used relative to the southernmost location, Cameron Parish, where the season length and daily temperatures currently allow for ratooning to be a common practice for long-grain cultivars (e.g., Cocodrie, Catahoula). The recent and projected increases in temperature and seasonality indicate that ratooning could already be adopted in Avoyelles Parish, and is potentially possible as far north as Cape Girardeau County (37°N) by the end of the 21 st century. While additional information regarding possible effects of heat stress, water availability, rising carbon dioxide (CO 2) levels, and other factors will be necessary to fully assess ratooning potential, our research indicated that ongoing increases in temperature and season length may allow agronomic management practices, such as ratooning, to help adapt rice production to climatic uncertainty.
In order to obtain high level of control over plant production, systems of high degree of closure... more In order to obtain high level of control over plant production, systems of high degree of closure for growing plants have been developed. These “closed” systems frequently exhibit the integration of automation, plant cultural requirements, and environmental control (i.e. the concept of ACESYS). Examples include plant factories, biomass production units for space journeys, and transplant production facilities. An object-oriented approach was taken to analyze these plant production systems. The purpose was to develop a set of foundation classes that could be used to effectively describe the components of closed plant production systems. Eight foundation classes were developed as the result of the object-oriented analysis, namely: Automation, Culture_Plant, Culture_Task, Culture _Facility, Environment_Rootzone, Environment_Aerial, Environment_Spatial, and Shell. An object-oriented model representing closed plant production systems was subsequently developed. The first version of the model is a crop production model for systems study of biomass production units within an advanced life support system for long duration human exploration of space. This JAVA based computer model is capable of calculating crop yield, inedible plant material, transpiration water, power usage, labor requirement, etc. over time for various crop mixes and scheduling scenarios. This biomass production model can be modified for simulating other closed plant production systems.
Computers and Electronics in Agriculture, Mar 1, 2021
Fine particulate matter (PM 2.5) is a current environmental issue that has an impact on the globa... more Fine particulate matter (PM 2.5) is a current environmental issue that has an impact on the global ecology. Vegetation is a known sink for PM 2.5 deposition but the effects of these particles on plant growth, and specifically on plant photosynthesis by changing their leaf water potential, are still not well understood. This study aimed to determine and characterize possible relationships between PM 2.5 and plant photosynthesis under different PM 2.5 concentrations. Both indoor and outdoor measurements were carried out to evaluate the variation dynamics of net photosynthetic rate and stomatal conductance of four plant species with different leaf characteristics under different PM 2.5 levels. A calibrated coupled model of photosynthesis and stomatal conductance was developed to estimate the relationship between plant photosynthesis and PM 2.5 reliably. Net photosynthetic rate and stomatal conductance declined over time at elevated PM 2.5 , with large variations with PM 2.5 concentrations. Using a calibrated model of photosynthesis coupled to stomatal conductance, we show that PM 2.5 can influence plant photosynthesis that primarily occurs through the stomata on leaves. Although the effect of particles on plant photosynthesis was not as high as that of photosynthetically active radiation, temperature, and CO 2 concentration around the leaf, the effect from PM 2.5 can be significant, in particular, in highly polluted atmospheres. Keywords PM 2.5. Leaf characteristics. Net photosynthetic rate. Stomatal conductance. Photosynthesis model. Model parameterization Highlights ?• Relationship between plant photosynthesis and PM 2.5 concentration among species. • An adjusted coupled model of photosynthesis and stomatal conductance. • Comparison of net photosynthetic rate between observed values and estimated values. • Response curves of net photosynthetic rate to individual environmental factors.
Computers and Electronics in Agriculture, Feb 1, 2020
The spatial distribution of crop yield has been assessed under current and future climate conditi... more The spatial distribution of crop yield has been assessed under current and future climate conditions using gridded crop growth simulations. This task usually requires considerable efforts to prepare input data and postprocess the outputs. In the present study, the Gridded cRop grOWth simuLation suppoRt System (GROWLERS) was developed to automate repetitive and tedious tasks using multiple PCs. In particular, the system was designed to aid researchers who have minimum knowledge on computer programming, network, and cluster management. An object oriented programming language, C++, was used to design and implement the GRO-WLERS, which would increase flexibility of a system while simplifying complexity including supports for different types of gridded data. Functionality of the GROWLERS includes preparation of weather input files, launch of crop growth model, and creation of gridded output files. Tools for the GROWLERS were installed on virtual machines connected through local network, which allows for building of a cluster computer without dedicated workstations. In a case study, 5.8 × 10 7 simulations using the ORYZA2000 model were performed to examine spatial distribution of the optimum sowing date for rice under current climate conditions in Korea. The subsets of these simulations were allocated to groups of virtual machines hosted within five custom built personal computers of which the central processing unit was manufactured about 10 years ago. Weather input data were prepared automatically using the GROWLERS. A set of scripts were also prepared using the GROWLERS, which allowed to reduce the wall clock time by 88% using 16 processor cores for worker nodes. These results suggest that the GROWLERS would minimize researcher's time involved in preparation and operation of a large number of crop growth simulations. Still, the support for nested simulations using multi-scale datasets would be needed to improve the GROWLERS, which merits further development as a next step.
The capacity for food production at the regional level under climate change depends on the natura... more The capacity for food production at the regional level under climate change depends on the natural resources base, and on the management and physical inputs available to farmers. In the Northeast U.S. (12 states, from West Virginia to Maine), more than 200 crops are grown, but yield and land area estimates are available for only a few. Our objective was to use long-term, county-level data from the region to 1) assess the potential for development of a multi-crop yield index, and 2) establish temporal and spatial trends for crops yields that could be incorporated in projections of productive capacit under climate change. County-level yield estimates were obtained for the period 1981-2010, from the USDA-National Agricultural Statistics Service (NASS) annual survey, for corn (Zea maize L.), wheat (Triticum aestivum L.,) and potato (Solanum tuberosum L.), soybean [Glycine max (L.) Merr.], and perennial hay (across categories). The correlation between yields of these crops was low (r <...
A multi-model inter-comparison study was conducted to evaluate the performance of ten potato crop... more A multi-model inter-comparison study was conducted to evaluate the performance of ten potato crop models to accurately predict potato yield in response to elevated CO2 (Ce) when calibrated with ambient CO2 data (Ca). Experimental data from seven open-top chambers (OTC) and free-air−CO2-enrichment (FACE) facilities across continental Europe were used. Model ensemble percent errors averaged over all datasets for simulated yields were 26.5 % for Ca and 27.2 % Ce data. Metrics such as Wilmott’s index of agreement (IA) and root mean square relative error (RMSRE) ranged broadly among individual models and locations, such that four of the ten models outperformed the median or mean of the ensemble for about half of the Ce datasets. These top performing models were representative of three different model structural groups, including radiation use efficiency, transpiration efficiency, or leaf-level based approaches. Relative response to an increase in CO2 was more accurately modeled than absolute yield responses when averaged across all locations, and within 3.3 kg ppm−1 (or 5%) of observed values. Specific targets in the model structure needed for improvement were not identified due to large and inconsistent variation in the accuracy of yield predictions across locations. However, models with the lowest calibration errors tended to be top performers for Ce predictions as well. Such results suggest calibration is at least as important as model structure. Where possible, modelers using potato models to estimate Ce responses should use Ce calibration data to improve confidence in such predictions
The comparison between spatial or temporal patterns is often needed for model evaluation and chan... more The comparison between spatial or temporal patterns is often needed for model evaluation and change detection in ecological studies. The statistics developed for image quality assessment, such as the structural similarity index (SSIM) and the composite similarity measure based on means, standard deviations, and correlation coefficient (CMSC), have been introduced for comparing ecological patterns. However, these measures can be applied only when a positive relationship is expected between patterns having the same scale. We propose a new index, generic composite similarity measure (GCSM), to meet a wide range of potential applications. A set of numerical experiments was performed to illustrate the properties of GCSM in comparison with SSIM and CMSC. Two case studies were conducted examining the (dis)agreement between two products of gross primary production (GPP), and the relative (dis)similarity between GPP and precipitation, respectively. GCSM has advantages over both SSIM and CMSC, including higher sensitivity and the ability to quantify the dissimilarity, which cannot be properly revealed with the latter two indices. The normalization preprocessing constructs universal criteria for assessing the relative (dis)similarity between patterns having unequal scales. The GCSM, overcoming the limitations of preexisting composite measures in quantifying the similarity or dissimilarity between patterns, would aid assessment of heterogeneous relationship between ecological factors over space or time.
Crop yield maps are valuable for many applications in precision agriculture, but are often inacce... more Crop yield maps are valuable for many applications in precision agriculture, but are often inaccessible to growers and researchers wishing to better understand yield determinants and improve site-specific management strategies. A method for mapping sub-field crop yields from remote sensing imagery could increase the availability of crop yield maps. A variation of the scalable crop yield mapping approach (SCYM, Lobell et al. in Remote Sensing of Environment 164:324-333, 2015) was developed and tested for estimating subfield maize (Zea mays L.) yields at 10-30 m without the use of site-specific input data. The method was validated using harvester yield monitor records for 21 site-years for irrigated and rainfed fields in eastern Nebraska, USA. Prediction error ranged greatly across site-years, with relative RMSE scores of 10.8 to 38.5%, and R 2 values of 0.003 to 0.37. Significant proportional bias was detected in the predictions, but could be corrected with a small amount of ground truth data. Crop yield prediction accuracies without calibration were suitable for some precision applications such as mapping relative yields and delineating management zones, but model improvements or calibration datasets are needed for applications requiring absolute yield estimates.
Supply for the fresh strawberry (Fragaria × ananassa Duchesne ex Rozier) market in the U.S. Mid-A... more Supply for the fresh strawberry (Fragaria × ananassa Duchesne ex Rozier) market in the U.S. Mid-Atlantic region is frequently supplemented by product grown in states outside the region. The adoption of repeat-fruiting cultivars specially bred for this area can benefit local and regional growers, but production systems suited to meet the cultural needs of these varieties must be evaluated. The relationships between yields from five repeat-fruiting cultivars grown in either uncovered (open) or plastic-covered (tunnel) raised-bed systems and associated microclimate factors were assessed from data collected over a three-year period at the USDA-ARS facility in Beltsville, Maryland. Average in-season yields were 40% higher and berry numbers were 150% higher for production in tunnel versus open systems when averaged across all cultivars, years, and harvests. This yield difference was attributed to warmer temperatures in the tunnel system that enabled extension of the growing season, higher weekly yields, and higher light use efficiency. Temperature and solar radiation accounted for more than 41% of the variance between yield and all measured microclimatic factors. Maximum 24 h temperatures averaged 3.5°C, 1.6°C, and 0.8°C higher, respectively, at the air (T a), crown (T c), and bed (T b) positions in the tunnel system, and daily photosynthetically active radiation was 34% lower in the tunnel system. A four-week period between floral initiation and fruit maturity was estimated as representative of the floral development period and was used as a basis for obtaining cardinal temperatures. The optimum temperature averaged 26.8°C, 28.6°C, and 27.9°C at T a , T c , and T b , respectively. Yields were positively correlated with daily light integral at temperatures below these thresholds, and higher slopes from the relationship of yield versus light were observed for tunnel production. Predicted yields using the beta function were shown to match observed values well in either production system, especially when T c data were used, and can be used for production system design and management.
The effect of climate change on recent and projected increases in surface temperatures is well-do... more The effect of climate change on recent and projected increases in surface temperatures is well-documented. For agriculture, such changes can impact crop phenology and production, but the degree of impact will depend, in part, on contemporaneous changes in crop management. In the current study, we quantified recent (last 40 years) and projected (to 2095) changes in air temperature and associated changes in growing season duration for rice along a latitudinal north-south gradient of the lower Mississippi valley. Recent and projected climate data indicated an ongoing increase in air temperature and growing season length with latitudes above ∼31°N. We then applied the DD50 growing degree day model to these data to determine if ratooning, a management practice that produces a second rice harvest with minimal resource input, could be employed. The model results were analyzed and used relative to the southernmost location, Cameron Parish, where the season length and daily temperatures currently allow for ratooning to be a common practice for long-grain cultivars (e.g., Cocodrie, Catahoula). The recent and projected increases in temperature and seasonality indicate that ratooning could already be adopted in Avoyelles Parish, and is potentially possible as far north as Cape Girardeau County (37°N) by the end of the 21 st century. While additional information regarding possible effects of heat stress, water availability, rising carbon dioxide (CO 2) levels, and other factors will be necessary to fully assess ratooning potential, our research indicated that ongoing increases in temperature and season length may allow agronomic management practices, such as ratooning, to help adapt rice production to climatic uncertainty.
In order to obtain high level of control over plant production, systems of high degree of closure... more In order to obtain high level of control over plant production, systems of high degree of closure for growing plants have been developed. These “closed” systems frequently exhibit the integration of automation, plant cultural requirements, and environmental control (i.e. the concept of ACESYS). Examples include plant factories, biomass production units for space journeys, and transplant production facilities. An object-oriented approach was taken to analyze these plant production systems. The purpose was to develop a set of foundation classes that could be used to effectively describe the components of closed plant production systems. Eight foundation classes were developed as the result of the object-oriented analysis, namely: Automation, Culture_Plant, Culture_Task, Culture _Facility, Environment_Rootzone, Environment_Aerial, Environment_Spatial, and Shell. An object-oriented model representing closed plant production systems was subsequently developed. The first version of the model is a crop production model for systems study of biomass production units within an advanced life support system for long duration human exploration of space. This JAVA based computer model is capable of calculating crop yield, inedible plant material, transpiration water, power usage, labor requirement, etc. over time for various crop mixes and scheduling scenarios. This biomass production model can be modified for simulating other closed plant production systems.
Computers and Electronics in Agriculture, Mar 1, 2021
Fine particulate matter (PM 2.5) is a current environmental issue that has an impact on the globa... more Fine particulate matter (PM 2.5) is a current environmental issue that has an impact on the global ecology. Vegetation is a known sink for PM 2.5 deposition but the effects of these particles on plant growth, and specifically on plant photosynthesis by changing their leaf water potential, are still not well understood. This study aimed to determine and characterize possible relationships between PM 2.5 and plant photosynthesis under different PM 2.5 concentrations. Both indoor and outdoor measurements were carried out to evaluate the variation dynamics of net photosynthetic rate and stomatal conductance of four plant species with different leaf characteristics under different PM 2.5 levels. A calibrated coupled model of photosynthesis and stomatal conductance was developed to estimate the relationship between plant photosynthesis and PM 2.5 reliably. Net photosynthetic rate and stomatal conductance declined over time at elevated PM 2.5 , with large variations with PM 2.5 concentrations. Using a calibrated model of photosynthesis coupled to stomatal conductance, we show that PM 2.5 can influence plant photosynthesis that primarily occurs through the stomata on leaves. Although the effect of particles on plant photosynthesis was not as high as that of photosynthetically active radiation, temperature, and CO 2 concentration around the leaf, the effect from PM 2.5 can be significant, in particular, in highly polluted atmospheres. Keywords PM 2.5. Leaf characteristics. Net photosynthetic rate. Stomatal conductance. Photosynthesis model. Model parameterization Highlights ?• Relationship between plant photosynthesis and PM 2.5 concentration among species. • An adjusted coupled model of photosynthesis and stomatal conductance. • Comparison of net photosynthetic rate between observed values and estimated values. • Response curves of net photosynthetic rate to individual environmental factors.
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Papers by David Fleisher