Quantifying Crop Yield, Bioenergy Production And Greenhouse Gas Emissions From Cropland And Marginal Land Using A Model-Data Fusion Approach

Bioenergy is becoming increasingly attractive to many countries, but has sparked an intensive debate regarding energy, economy, society and environment. Biofuels provide alternative energy to conventional fossil fuels. However, the environmental impact of producing and using biofuel is a major concern to our society. This study is dedicated to quantifying and evaluating biofuel production and potential climate change mitigation due to potential large-scale bioenergy expansion in the conterminous United States, using model-data fusion approaches. Biofuel made from conventional (e.g., maize (Zea mays L.)) and cellulosic crops (e.g., switchgrass (Panicum virgatum L.) and Miscanthus (Miscanthus × giganteus)) provides alternative energy to fossil fuels and has been considered to mitigate greenhouse gas emissions. To estimate the large-scale carbon and nitrogen dynamics of these biofuel ecosystems, process-based models are needed. Here, we developed an agroecosystem model (AgTEM) based on the Terrestrial Ecosystem Model for these ecosystems. The model incorporated biogeochemical and ecophysiological processes including crop phenology, biomass allocation, nitrification and denitrification as well as agronomic management of irrigation and fertilization. It was used to estimate crop yield, biomass, net carbon exchange, and nitrous oxide (N2O) emissions at an ecosystem level. We found that AgTEM reproduces the observed annual net primary production and N2O emissions of most sites, with over 85% of total variations explained by the model. Local sensitivity analysis indicated that the model sensitivity varies among different ecosystems. Net primary production of maize is sensitive to temperature, precipitation, cloudiness, fertilizer and irrigation and less sensitive to atmospheric carbon dioxide (CO2) concentrations. In contrast, the net primary production of switchgrass and Miscanthus is most sensitive to temperature among all factors. The N2O emissions are sensitive to management in maize ecosystems, and sensitive to climate factors in cellulosic ecosystems. The developed model should help advance our understanding of carbon and nitrogen dynamics of these biofuel ecosystems at both field and regional scales. Next, we estimated the potential emissions of greenhouse gases from bioenergy ecosystems with AgTEM, assuming maize, switchgrass and Miscanthus will be grown on the current maize-producing areas in the conterminous United States. The modeling experiments suggested that, the maize ecosystem acts as a mild net carbon source while cellulosic ecosystems (i.e., switchgrass and Miscanthus) act as mild sinks. Nitrogen fertilizer use is an important factor affecting biomass production and N2O emissions, especially in the maize ecosystem. To maintain high biomass productivity, the maize ecosystem emits much more greenhouse gases, including CO2 and N2O, than switchgrass and Miscanthus cosystems, when high-rate nitrogen fertilizers are applied. For maize, the global warming potential amounts to 1-2 Mg CO2eq ha-1 yr-1, with a dominant contribution of over 90% from N2O emissions. Cellulosic crops contribute to the global warming potential of less than 0.3 Mg CO2eq ha-1 yr-1. Among all three bioenergy crops, Miscanthus is the most biofuel productive and the least GHG intensive at a given cropland. Regional model simulations suggested that, substituting Miscanthus for maize to produce biofuel could potentially save land and reduce GHG emissions. Since growing biomass from marginal lands is becoming an increasingly attractive choice for producing biofuel, we looked further into bioenergy potential and possible GHG emissions from bioenergy crops grown on marginal lands in the United States. Two broadly tested cellulosic crops, switchgrass and Miscanthus, were assumed to be grown on the abandoned land and mixed crop-vegetation land with marginal productivity. Production of biomass and biofuel as well as net carbon exchange and N2O emissions were estimated in a spatially explicit manner, using AgTEM. Modeling experiments showed that, cellulosic crops, especially Miscanthus, could produce a considerable amount of biomass and thus ethanol. For every hectare of marginal land, switchgrass and Miscanthus could produce 1.4-2.3 kL and 4.1-6.9 kL ethanol, respectively. The actual amount of ethanol production depends on nitrogen fertilization rate and biofuel conversion efficiency. Switchgrass has high global warming intensity (100-190 g CO2eq L-1 ethanol), in terms of GHG emissions per unit ethanol produced. Miscanthus, however, emits only 21-36 g CO2eq to produce every liter of ethanol. To reach the mandated cellulosic ethanol target of 21 billion gallons by 2022 in the United States, growing Miscanthus on the marginal lands could save a large amount of land and reduce GHG emissions in comparison to growing switchgrass. It should be noted that, ecosystem modeling may be useful for evaluating ecosystem services and environmental impacts, and the results could be informative for policy making concerning energy, food security and sustainability. However, the modeling results are limited in terms of advising agricultural management practices, land use change and energy system analysis, due to modeling uncertainties, data unavailability, and simulation scale and boundary limitations. High-accuracy data assimilation, model improvement and life cycle assessment still await future study

Evaluation of Stabilized Fertilizer and Crop Canopy Sensors as Next-Generation Nitrogen Management Technologies in Irrigated Corn

Nitrogen (N) is often the most limiting nutrient to corn. Once applied to the field, N can be lost through different pathways, which contributes to low N use efficiency (NUE) by plants. Increases in NUE and decreases in N losses can be potentially achieved by using management options that allow a better synchrony between N supply and demand, such as stabilized fertilizers, and spatially-variable sensor-derived in-season N application. Three studies were conducted in order to assess the effects of different stabilized fertilizers and crop canopy sensors on irrigated corn yield. The first study evaluated the effect of urease inhibitor on ammonia losses and corn grain yield. The use of urease inhibitors significantly reduced ammonia volatilization losses by 21 to 62%, but this did not translate into higher corn yields. The second study evaluated the effect of various management practices along with the use of a nitrification inhibitor and their interaction with weather on irrigated corn grain yield over 28 yrs. The use of a nitrification inhibitor had negative, neutral, and positive effects on corn grain yield, and the magnitude of its effect was less than other management practices. The most important weather variables in explaining different yield responses were year- yield potential, precipitation volume and distribution, and air temperature. The third study compared active and passive crop canopy sensors in assessing corn N deficiency and the accuracy of recommended side-dress N rates compared to the economic optimum N rate. This study included eight field studies using different N fertilizer rates and the use of both active and passive crop canopy sensor during the mid-vegetative growth stage in corn. Active and passive sensors recommended comparable side- dress N rates given proper selection of algorithm inputs. Their recommendation was partially or fully accurate in four of six studies. Both stabilized fertilizers and crop canopy sensors are important management tool options for producers, and an understanding of their strengths and weaknesses is needed to guide proper adoption decisions. Advisor: Richard B. Ferguso

Integrated modelling to assess N pollution swapping in slurry amended soils

In the present work, it was hypothesized that through modelling it is possible to overcome the constraints that arise in the quantification of N pollution swapping associated to slurry application practiceswhen using individual experimental data. For this, environmental N losses were assessed under two methods of dairy slurry application to a double cropping system (rainfed oats (Avena strigosa)/irrigated maize (Zea mays)) in two different soils. An integrated experimentation and modelling approach was applied using the RZWQM2 model. The modelwas first tested using four years of experimental data concerningN fluxes to/fromdifferent environmental compartments (soilmineralization, N gas emissions, and N leaching). Themodel estimated emissionswith overall efficiencies of ~70% and r2 ~ 0.75. Total N losses were higher for surface band application (95.4 and 40.2 kg ha−1 for the sandy and sandy loam soils, respectively). However, when slurry was injected, nitrate leaching considerably increased (by 107 and 64% for the sandy and sandy loam soils, respectively), even though gas emissions were minimized. This N swapping among path losses requires targeting of the N mitigation measures to the environmental compartment showing the highest vulnerability. Generally, the estimated emission factors (EFs) were lower than or equal to (slurry injection in the sandy loam soil) the IPCC default. The values showed high variability, reinforcing the need to use agricultural system specific EFs. The methodologies used in this study, focused on scenario analysis, can support policy as they can be used to set up integral strategies to decrease N emissions fromlivestock farming systems, taking into account possible synergies and antagonisms produced by the measures among NH3 and N2O emissions and NO3 − leachinginfo:eu-repo/semantics/publishedVersio

CHARACTERIZING NITROGEN LOSS AND GREENHOUSE GAS FLUX ACROSS AN INTENSIFICATION GRADIENT IN DIVERSIFIED VEGETABLE SYSTEMS

The area of vegetable production is growing rapidly world-wide, as are efforts to increase production on existing lands in these labor- and input-intensive systems. Yet information on nutrient losses, greenhouse gas emissions, and input efficiency is lacking. Sustainable intensification of these systems requires knowing how to optimize nutrient and water inputs to improve yields while minimizing negative environmental consequences. This work characterizes soil nitrogen (N) dynamics, nitrate (NO3¯) leaching, greenhouse gas emissions, and crop yield in five diversified vegetable systems spanning a gradient of intensification that is characterized by inputs, tillage and rotational fallow periods. The study systems included a low input organic system (LI), a mechanized, medium scale organic system (CSA), an organic movable high tunnel system (MOV), a conventional system (CONV) and an organic stationary high tunnel system (HT). In a three-year vegetable crop rotation with three systems (LI, HT and CONV), key N loss pathways varied by system; marked N2O and CO2 losses were observed in the LI system and NO3– leaching was greatest in the CONV system. Yield-scaled global warming potential (GWP) was greater in the LI system compared to HT and CONV, driven by greater greenhouse gas flux and lower yields in the LI system. The field data from CONV system were used to calibrate the Root Zone Water Quality Model version 2 (RZWQM2) and HT and LI vegetable systems were used to validate the model. RZWQM2 simulated soil NO3¯-N content reasonably well in crops grown on bare ground and open field (e.g. beet, collard, bean). Despite use of simultaneous heat and water (SHAW) option in RZWQM2 to incorporate the use of plastic mulch, we were not able to successfully simulate NO3¯-N data. The model simulated cumulative N2O emissions from the CONV vegetable system reasonably well, while the model overestimated N2O emissions in HT and LI systems

Improving The Utility of Precision Agriculture Through Machine Learning and Climate-Smart Practices

Climate Smart Practices are management strategies that focus on increasing soil and crop productivity, utilize site-specific strategies to increase resiliency against the effects of climate change, and mitigate these negative effects by reducing greenhouse gas (GHG) emissions. Decision Support Systems (DSSs) using machine learning (ML) can adjust models based on new information and help farmers make climate smart decisions within their operation. The 4R nutrient management model of right source, rate, location, and time also demonstrates a framework that may be considered climate smart by improving soil and crop productivity. However, when initially conceptualized, the 4R model did not consider GHG emissions. Additionally, the long-term adoption of DSSs has been low in agriculture, reducing the ability of farmers to collect and analyze farm data to the fullest. Therefore, the objective of the first chapter is to examine applications of, and barriers to, DSSs in precision agriculture (PA). The objective of the second chapter evaluates the 4R model to determine the impact of GHG emissions when utilizing near continuous chambers over a two-year period. The GHG emissions were measured by analyzing nitrous oxide and carbon dioxide emissions from a 50/50 split application of 157 kg N/ha that was applied to corn (Zea mays) at pre-emergence and V6 compared to a single application at pre-emergence 157 kg N/ha in a two-year replicated study. Results from the first chapter identify the barriers preventing farmers from using DSSs as well as suggesting solutions to these challenges. Results from the second chapter indicate that the split application can reduce carbon dioxide and carbon equivalent emissions and therefore, may be a useful framework for DSSs to follow in achieving Climate Smart Practices

Factors That Influence Nitrous Oxide Emissions from Agricultural Soils as Well as Their Representation in Simulation Models: A Review

Nitrous oxide (N2O) is a long-lived greenhouse gas that contributes to global warming. Emissions of N2O mainly stem from agricultural soils. This review highlights the principal factors from peer-reviewed literature affecting N2O emissions from agricultural soils, by grouping the factors into three categories: environmental, management and measurement. Within these categories, each impact factor is explained in detail and its influence on N2O emissions from the soil is summarized. It is also shown how each impact factor influences other impact factors. Process-based simulation models used for estimating N2O emissions are reviewed regarding their ability to consider the impact factors in simulating N2O. The model strengths and weaknesses in simulating N2O emissions from managed soils are summarized. Finally, three selected process-based simulation models (Daily Century (DAYCENT), DeNitrification-DeComposition (DNDC), and Soil and Water Assessment Tool (SWAT)) are discussed that are widely used to simulate N2O emissions from cropping systems. Their ability to simulate N2O emissions is evaluated by describing the model components that are relevant to N2O processes and their representation in the model

High groundwater in irrigated regions: model development for assessing causes, identifying solutions, and exploring system dynamics

2021 Spring.Includes bibliographical references.Waterlogging occurs in irrigated areas around the world due to over-irrigation and lack of adequate natural or artificial drainage. This phenomenon can lead to adverse social, physical, economic, and environmental issues, such as: damage to crops and overall land productivity; soil salinization; and damage to homes and building foundations. Solutions to waterlogging include implementation of high-efficient irrigation practices, installation of artificial drainage systems, and increased groundwater pumping to lower the water table. However, in regions governed by strict water law, wherein groundwater pumping is constrained by impact on nearby surface water bodies, these practices can be challenging to implement. In addition, current engineering and modeling approaches used to quantify soil-groundwater and groundwater-surface water interactions are crude, perhaps leading to erroneous results. An accurate representation of groundwater state variables, groundwater sources and sinks, and plant-soil-water interaction is needed at the regional scale to assist with groundwater management issues. This dissertation enhances understanding of major hydrological processes and trade-offs in waterlogged agricultural areas, through the use of numerical modeling strategies. This is accomplished by developing numerical modeling tools to: (1) analyze and quantify the cause of high groundwater levels in highly managed, irrigated stream-aquifer systems; (2) assess the impact of artificial recharge ponds on groundwater levels, groundwater-surface water interactions, and stream depletions in irrigated stream-aquifer systems; (3) and gain a better understanding of plant-soil-water dynamics in irrigated areas with high water tables. These objectives use a combination of agroecosystem (DayCent) and groundwater flow (MODFLOW) models, sensitivity analysis, and management scenario analysis. Each of these sub-objectives is applied to the Gilcrest/LaSalle agricultural region within the South Platte River Basin in northeast Colorado, a region subject to high groundwater levels and associated waterlogging and infrastructure damage in the last 7 years. This region is also subject to strict water law, which constrains groundwater pumping due to the effect on the water rights of the nearby South Platte River. Results indicate that recharge from surface water irrigation, canal seepage, and groundwater pumping have the strongest influence on water table elevation, whereas precipitation recharge and recharge from groundwater irrigation have small influences from 1950 to 2012. Mitigation strategy implementation scenarios show that limiting canal seepage and transitioning > 50% of cultivated fields from surface water irrigation to groundwater irrigation can decrease the water table elevation by 1.5 m to 3 m over a 5-year period. Decreasing seepage from recharge ponds has a similar effect, decreasing water table elevation in local areas by up to 2.3 m. However, these decreases in water table elevation, while solving the problem of high groundwater levels for residential areas and cultivated fields, results in a decrease in groundwater discharge to the South Platte River. As the intent of the recharge ponds is to increase groundwater discharge and thereby offset stream depletions caused by groundwater pumping, mitigating high water table issues in the region can be achieved only by (1) modifying fluxes of sources and sinks of groundwater besides recharge pond seepage, or (2) modifying or relaxing the adjudication of water law, which dictates the need for offsetting pumping-induced stream depletion, in this region. The modeling tools developed in this dissertation, specifically the loose and tight coupling between DayCent and MODFLOW, can be used in the study region to quantify pumping-induced stream depletion, recharge pond induced stream accretion, and the interplay between them in space and time. In addition, these models can be used in other irrigated stream-aquifer systems to assess baseline conditions and explore possible effects of water management strategies

by integrating deep learning, mechanistic model and field observations

학위논문(박사) -- 서울대학교대학원 : 농업생명과학대학 협동과정 농림기상학, 2022. 8. Youngryel Ryu.Rice (Oryza sativa) is a vital cereal crop that feeds more than 50% of the world population. However, the traditional anaerobic management leads rice production to consume ~40% of the irrigation water and emit ~10% of the global anthropogenic methane. A new paradigm for sustainable rice farming is urgently required amid challenges from increasing food demand, water scarcity, and reducing greenhouse gases emissions. Rice plants transpire considerable water overnight. Saving nighttime water loss is desirable but first need to understand the underlying mechanism of nocturnal stomatal opening. Apart from the night, optimizing daytime management is pivotal for designing an environmentally sustainable rice farming system. In a long-term strategy, detailed and reliable crop type map is compulsory to upscale new leaf level findings and site level methods to regional or global scale. Therefore, in this dissertation, we improved mechanistic understanding of nocturnal stomatal conductance in rice plants (Chapter II); provided an interdisciplinary and heuristic approach for designing an environmentally sustainable rice farming system with a case study in South Korea (Chapter III); and developed a new crop type referencing method by mining off-the-shelf Google Street View images to map crop types (Chapter IV). In chapter II, we proposed a “coordinated leaf trait” hypothesis to explain the ecological mechanism of nocturnal stomatal conductance (gsn) in rice. We conducted an open-field experiment by applying drought, nutrient deficiency, and the combined drought-nutrient deficiency stress. We found that gsn was neither strongly reduced by drought nor consistently increased by nutrient deficiency. With abiotic stress as a random effect, gsn was strongly positively correlated with nocturnal respiration (Rn). Notably, gsn primed early morning photosynthesis, as follows: Rn (↑) → gsn (↑) → gsd (daytime stomatal conductance) (↑) → A (assimilation) (↑). This photosynthesis priming effect diminished after mid-morning. Leaves were cooled by gsn as follows: gsn (↑) → E (transpiration) (↑) → Tleaf (leaf temperature) (↓). However, our results clearly suggest that evaporative cooling did not reduce Rn cost. Our results indicate that gsn is more closely related to carbon respiration and assimilation than water and nutrient availability, and that leaf trait coordination (Rn − gsn − gsd − A) is likely the primary mechanism controlling gsn. In chapter III, we aimed to increase current crop yield, reduce irrigation water consumption, and tackle the dilemma to simultaneously reducing CH4 and N2O emissions in a flooded rice production system. By proposing a heuristic and holistic method, we optimized farm management beyond previous most emphasized irrigation regimes while also exploring niches from other pivotal options regarding sowing window, fertilization rate, tillage depth, and their interactions. Specifically, we calibrated and validated the process-based DNDC model with five years of eddy covariance observations. The DNDC model later was integrated with the non-dominated sorting genetic algorithm (NSGA-III) to solve the multi-objective optimization problem. We found that the optimized management would maintain or even increase current crop yield to its potential (~10 t/ha) while reducing more than 50% irrigation demand and GHGs (CH4 & N2O) emissions. Our results indicate that earlier sowing window and improvements on irrigation practice together would be pivotal to maximizing crop yield while sustaining environmental benefits. We found that the optimal fraction of non-flooded days was around 54% of growing season length and its optimal temporal distributions were primarily in vegetative stages. Our study shows that the present farm yield (8.3-8.9 t/ha) in study site not only has not achieved its potential level but also comes at a great environmental cost to water resources (604-810 mm/yr) and GHGs emissions (CH4: 186-220 kg C/ha/yr; N2O: 0.3-1.6 kg C/ha/yr). Furthermore, this simple method could further be applied to evaluate the environmental sustainability of a farming system under various climate and local conditions and to guide policymakers and farming practices with comprehensive solutions. In chapter IV, we apply a convolutional neural network (CNN) model to explore the efficacy of automatic ground truthing via Google Street View (GSV) images in two distinct farming regions: Illinois and the Central Valley in California. Ground reference data are an essential prerequisite for supervised crop mapping. The lack of a low-cost and efficient ground referencing method results in pervasively limited reference data and hinders crop classification. In this study, we demonstrate the feasibility and reliability of our new ground referencing technique by performing pixel-based crop mapping at the state level using the cloud-based Google Earth Engine platform. The mapping results are evaluated using the United States Department of Agriculture (USDA) crop data layer (CDL) products. From ~130,000 GSV images, the CNN model identified ~9,400 target crop images. These images are well classified into crop types, including alfalfa, almond, corn, cotton, grape, rice, soybean, and pistachio. The overall GSV image classification accuracy is 92% for the Central Valley and 97% for Illinois. Subsequently, we shifted the image geographical coordinates 2–3 times in a certain direction to produce 31,829 crop reference points: 17,358 in Illinois, and 14,471 in the Central Valley. Evaluation of the mapping results with CDL products revealed satisfactory coherence. GSV-derived mapping results capture the general pattern of crop type distributions for 2011–2019. The overall agreement between CDL products and our mapping results is indicated by R2 values of 0.44–0.99 for the Central Valley and 0.81–0.98 for Illinois. To show the applicational value of the proposed method in other countries, we further mapped rice paddy (2014–2018) in South Korea which yielded fairly well outcomes (R2=0.91). These results indicate that GSV images used with a deep learning model offer an efficient and cost-effective alternative method for ground referencing, in many regions of the world.쌀(오리자 사티바)은 세계 인구의 50% 이상을 먹여 살리는 중요한 곡물 작물이다. 그러나 전통적인 혐기성 관리는 쌀 생산으로 관개수의 40%를 소비하고 전 세계 인공 메탄의 10%를 배출한다. 식량 수요 증가, 물 부족, 온실가스 배출 감소 등의 과제 속에서 지속 가능한 벼농사를 위한 새로운 패러다임이 시급하다. 벼는 하룻밤 사이에 상당한 양의 물을 내뿜는다. 야간 수분 손실을 줄이는 것은 바람직하지만, 먼저 야간 기공 개방의 기본 메커니즘을 이해할 필요가 있다. 야간과 별도로 주간 경영의 최적화는 환경적으로 지속 가능한 벼농사 시스템을 설계하는 데 매우 중요하다. 장기 전략에서, 새로운 잎 수준 발견과 현장 수준 방법을 지역적 또는 전역적 규모로 상향 조정하려면 상세하고 신뢰할 수 있는 작물 유형 맵이 필수적이다. 따라서, 본 논문에서 우리는 벼농사의 야간 기공 전도도에 대한 기계적 이해를 향상시켰다(제2장). 환경적으로 지속 가능한 벼농사 시스템을 설계하기 위한 학제 간 및 휴리스틱 접근법 제공(제3장). 그리고 새로운 작물 유형 참조 방법을 개발했다. 기성품인 Google Street View 이미지를 마이닝하여 자르기 유형을 매핑합니다. 2장에서 우리는 벼의 야행성 기공 전도도(gsn)의 생태학적 메커니즘을 설명하기 위해 "협동된 잎 형질" 가설을 제안했습니다. 가뭄, 영양 결핍 및 가뭄-영양소 결핍 복합 스트레스를 적용하여 노지 실험을 수행했습니다. 우리는 gsn이 가뭄에 의해 크게 감소하지도 않고 영양 결핍에 의해 지속적으로 증가하지도 않는다는 것을 발견했습니다. 무생물적 스트레스를 무작위 효과로 사용하여 gsn은 야간 호흡(Rn)과 강한 양의 상관관계를 보였습니다. 특히, gsn은 Rn(↑) → gsn(↑) → gsd(주간 기공 전도도)(↑) → A(동화)(↑)와 같이 이른 아침 광합성을 프라이밍했습니다. 이 광합성 프라이밍 효과는 오전 중반 이후에 감소했습니다. 잎은 gsn에 의해 다음과 같이 냉각되었습니다: gsn(↑) → E(증산)(↑) → Tleaf(잎 온도)(↓). 그러나 우리의 결과는 증발 냉각이 Rn 비용을 감소시키지 않았다는 것을 분명히 시사합니다. 우리의 결과는 gsn이 물 및 영양소 가용성보다 탄소 호흡 및 동화와 더 밀접하게 관련되어 있으며 잎 형질 조정(Rn - gsn - gsd - A)이 gsn을 제어하는 주요 메커니즘일 가능성이 있음을 나타냅니다. 제3장에서 우리는 현재의 작물 수확량을 늘리고 관개 용수 소비를 줄이며 침수된 쌀 생산 시스템에서 CH4와 N2O 배출량을 동시에 줄이는 딜레마를 해결하는 것을 목표로 했다. 휴리스틱하고 전체론적 방법을 제안함으로써, 우리는 이전에 가장 강조되었던 관개 체제를 넘어 농장 관리를 최적화함과 동시에 파종 창, 수정률, 경작 깊이 및 이들의 상호 작용과 관련된 다른 중추적 옵션의 틈새를 탐색했다. 구체적으로, 우리는 5년간의 와류 공분산 관찰로 프로세스 기반 DNDC 모델을 교정하고 검증했다. DNDC 모델은 나중에 다중 객관적 최적화 문제를 해결하기 위해 비지배적 정렬 유전 알고리듬(NSGA-III)과 통합되었다. 우리는 최적화된 관리를 통해 50% 이상의 관개 수요와 GHG(CH4 & N2O) 배출량을 줄이면서 현재 농작물 수확량을 잠재력(~10t/ha)까지 유지하거나 증가시킬 수 있다는 것을 발견했습니다. 우리의 결과는 더 이른 파종 기간과 관개 관개 관행의 개선이 환경적 이익을 유지하면서 농작물 수확량을 최대화하는 데 중추적일 것이라는 것을 보여준다. 우리는 홍수 없는 날의 최적 부분이 성장기 길이의 약 54%였고 최적의 시간 분포는 주로 식물 단계에 있다는 것을 발견했다. 우리의 연구는 연구 현장의 현재 농장 수확량(8.3-8.9 t/ha)이 잠재적 수준을 달성했을 뿐만 아니라 수자원(604-810 mm/yr)과 GHGs 배출(CH4: 186-220 kg C/ha/yr; N2O: 0.3-1.6 kg C/ha/yr)에 막대한 환경 비용을 초래한다는 것을 보여준다. 또한, 이 간단한 방법은 다양한 기후 및 지역 조건 하에서 농업 시스템의 환경 지속 가능성을 평가하고 정책 입안자와 농업 관행을 포괄적인 해결책으로 안내하는 데 추가로 적용될 수 있다. 제4장에서는 컨볼루션 신경망(CNN) 모델을 적용하여 두 개의 구별되는 농업 지역에서 구글 스트리트 뷰(GSV) 이미지를 통해 자동 지상 트러싱의 효과를 탐구한다. 일리노이와 캘리포니아의 센트럴 밸리. 지상 참조 데이터는 감독된 작물 매핑을 위한 필수 전제 조건이다. 저렴하고 효율적인 지상 참조 방법이 없기 때문에 참조 데이터가 광범위하게 제한되고 작물 분류를 방해한다. 본 연구에서는 클라우드 기반 Google 어스 엔진 플랫폼을 사용하여 상태 수준에서 픽셀 기반 크롭 매핑을 수행하여 새로운 지상 참조 기술의 실현 가능성과 신뢰성을 입증한다. 매핑 결과는 미국 농무부(USDA) 작물 데이터층(CDL) 제품을 사용하여 평가된다. 약 130,000개의 GSV 이미지에서 CNN 모델은 약 9,400개의 목표 크롭 이미지를 식별했다. 이 이미지들은 알팔파, 아몬드, 옥수수, 면화, 포도, 쌀, 콩, 피스타치오 등의 작물 유형으로 잘 분류된다. 전체 GSV 이미지 분류 정확도는 센트럴 밸리의 경우 92%, 일리노이 주의 경우 97%이다. 그 후 이미지 지리적 좌표를 특정 방향으로 2~3회 이동하여 31,829개의 크롭 기준점을 생성했다. 즉, 일리노이에서 17,358개, 센트럴 밸리에서 14,471개였다. CDL 제품으로 매핑 결과를 평가한 결과 만족스러운 일관성이 나타났다. GSV에서 파생된 매핑 결과는 2011-2019년 작물 유형 분포의 일반적인 패턴을 포착한다. CDL 제품과 우리의 매핑 결과 사이의 전체 합치는 센트럴 밸리의 경우 0.44–0.99의 R2 값과 일리노이 주의 경우 0.81–0.98의 R2 값으로 표시된다. 제안된 방법의 다른 국가에서 적용 가치를 보여주기 위해, 꽤 좋은 결과를 얻은 한국의 논(2014–2018)을 추가로 매핑했다(R2=0.91). 이러한 결과는 딥 러닝 모델과 함께 사용되는 GSV 이미지가 세계의 많은 지역에서 지상 참조를 위한 효율적이고 비용 효율적인 대체 방법을 제공한다는 것을 나타낸다.1. Abstract 3 LIST OF FIGURES 9 LIST OF TABLES 13 ACKNOWLEDGEMENTS 14 Chapter I. Introduction 15 1.1. Study Background 15 1.2. Purpose of Research 15 Chapter II. Nocturnal stomatal conductance in rice: a coordinating bridge between prior respiration and photosynthesis next dawn 17 Abstract 17 1. Introduction 18 2. Materials and Methods 22 2.1 Plants and growth conditions 22 2.2 Leaf physiological traits 22 2.3 Rapid A/Ci response curves 24 2.4 Stomatal anatomy measurements 24 2.5 Statistical analyses 24 3. Results 25 3.1 Effects of abiotic stress on leaf traits 25 3.2 Nighttime leaf physiological traits 26 3.3 Significant priming effects of gsn on early morning photosynthesis (~5:00 – 7:00) 27 3.4 Path analyses only support the leaf trait coordination 28 3.5 Impacts of gsn on gsd and Amax under light-saturated conditions 29 3.6 Photosynthesis priming effects not detected after mid-morning (9:00) 31 4. Discussion 32 4.1 Abiotic stress results: Implications for different hypotheses 33 4.2 Enhanced carbon assimilation through coordinated regulation by gsn 34 4.3 Evaporative cooling: Passive thermoregulation via leaf trait coordination 36 References 37 Chapter III. Multi-objective optimization of crop yield, water consumption, and greenhouse gases emissions for sustainable rice production 42 Abstract 42 1. Introduction 43 2. Materials and methods 46 2.1 Study site 46 2.2 DNDC model 46 2.3 In situ data 47 2.4 Multi-objective optimization (MOO) algorithm 48 2.5 DNDC-NSGA-III integration and optimization 48 3. Results 50 3.1 DNDC model validation 50 3.2 The gaps between the current farming outcomes and optimized objectives 53 3.3 Approaching Pareto fronts through the heuristic and holistic management 55 3.4 The gaps between current farming practices to potential crop yield with optimal holistic management 56 4. Discussion 58 4.1 Could heuristic and holistic management increase current rice yield with less irrigation water? 58 4.2 Could heuristic and holistic management simultaneously reduce CH4 and N2O emissions? 59 4.3 Limitations and uncertainties 60 Reference 61 Chapter IV. Exploring Google Street View with Deep Learning for Crop Type Mapping 70 Abstract 70 1. Introduction 71 2. Materials and Methods 74 2.1 Study area 74 2.2 General methodology 75 2.3 Google Street View image collection 76 2.4 CNN model training and validation 77 2.5 Producing ground reference data and quality control 79 2.6 Mapping crop types 80 2.7 Mapping results evaluation 81 2.8 Additional test case 82 3. Results 83 3.1 GSV image classification 83 3.2 Producing ground reference data from classified GSV images 84 3.3 Mapping using the GSV derived ground reference 86 4. Discussion 96 4.1 Can we use GSV images to efficiently produce low-cost, sufficient, and reliable crop type ground reference data covering large areas? 96 4.2 Can we use GSV-derived reference data as “ground truth” to map crop types for large areas spanning many years? 97 Appendix 99 References 105 Chapter V. Conclusions 123 Supplementary Information Chapter II 125 Supplementary Information Chapter III 131 Supplementary Information Chapter IV 135 5. Abstract in Korean 138박

Modeling bioenergy agroecosystems for climate change mitigation and vulnerability assessment

2017 Spring.Includes bibliographical references.Agriculture is a major driver of anthropogenic climate change while also directly bearing its impacts. In addition to emissions related to farm operations and inputs, substantial greenhouse gases are released from cropland soils. These include carbon dioxide (CO2) fluxes due to long-term changes in soil organic carbon pools, and nitrous oxide (N2O) produced by soil microbes primarily from excess nitrogen (N) fertilizer not assimilated by crops. Agricultural bioenergy systems are expected to produce liquid fuels with lower life-cycle emissions than gasoline. Current US policy specifies several emissions reduction tiers for biomass-derived liquid fuels, ranging from 20% lower than gasoline for corn grain ethanol to 60% lower for ethanol made from perennial grasses or agricultural residues. While these tiers are based on detailed life-cycle assessments of "average" production conditions, they fail to convey the potentially large variability in emissions arising from farm management and biophysical factors. The first half of this dissertation uses a survey of management practices from suppliers of corn grain to a biorefinery in the US Midwest to explore the magnitude and sources of this variability. The first phase of that study finds that feedstock from most of the farms would achieve the statutory threshold of 20%, but that best-performing farms may be producing grain that would lead to fuel with 50% lower life-cycle emissions than gasoline. Key management practices identified are tillage intensity, efficient N fertilizer use and application of livestock manure. Crop residues, such as corn stover, can also be converted to ethanol. The second part of this study explore the sustainability of corn stover collection for ethanol production by a hypothetical dual-feedstock biorefinery. Stover collection presents a tradeoff: when used to produce ethanol, it displaces emissions from gasoline, but at the cost of less soil organic carbon (SOC) accumulation. Still, soils on these farms could sustain relatively high stover collection rates without net SOC losses or erosion, especially in the context of manure application and reduced tillage intensity. Climate change entails two major phenomena – increasing atmospheric [CO2] and increasing extreme high temperatures – likely to have opposing impacts on agricultural productivity, and these impacts will tend to increase over the course of the 21st Century. Chapter 4 of this work reviews the current understanding of crop responses to elevated atmospheric [CO2] and extreme heat as determined from agronomic studies and analyses of historical climate-yield data. It summarizes consensus findings and presents emerging topics in need of further research, and compares the state of knowledge with the simulation approaches employed by several major crop models. The increasing atmospheric [CO2] that largely drives climate change supports increased rates of photosynthesis in C3 plants and improved water use efficiency in all plant types. The magnitude of this fertilization effect is uncertain, however, and recent free atmospheric CO2 enrichment (FACE) experiments appear to show reduced gains relative to earlier enclosure experiments. Chapter 5 tests the hypothesis that the algorithm designed to simulate the CO2 effect in the DayCent ecosystem model overestimates crop responses to elevated [CO2] as observed under FACE conditions

Machine-Learning and Meta-Analysis Techniques to Quantify and Predict Soil Organic Carbon, N\u3csub\u3e2\u3c/sub\u3eO-N and CO\u3csub\u3e2\u3c/sub\u3e-C Emissions in Cover Crop Systems

People worldwide are challenged by multiple threats including climate change, growing populations, and soil degradation. Addressing these challenges requires understanding of the local environment, farming systems and modern technologies. These technologies include new ways to process information that include artificial intelligence, machine learning and meta-analysis. Models produced using these technologies may be useful for predicting the consequences of implementing conservation practices that reduce GHG emissions as well as for determining the carbon footprint of cropping systems that include environmentally friendly conservation technologies such as growing cover crop. Therefore, our objectives of this study were to: 1) provide an overview of conservation agriculture technology as strategy to minimize soil degradation, climate change challenges, and food insecurity issues in developing countries like Nepal, 2) conduct global meta-analysis to quantify the impact of cover crops as one of conservation agriculture technique, on soil organic carbon (SOC) and crop yield in a corn (Zea mays L.) cropping system and 3) assess different machine learning based algorithms to predict the daily N2O-N and CO2-C emission from a decomposing rye (scientific name of rye) cover crop. For the first objective, historical data analysis indicated that air temperatures in Nepal have been increasing since 1901 at a rate of y 0.016 oC yr-1, whereas precipitation has been decreasing at a rate of -0.137 mm yr-1. Increasing air temperature, when combined with decreasing precipitation, are interacting to reduce crop growth and yield, diminishing Nepal’s food security. We proposed conservation agriculture practices such as planting cover crop as farmer and environment friendly approach to mitigate and adopt the climate change impact and enhance food security. In second objective, I used meta- analysis approach to measure the effect of cover crop on SOC values in corn at a global scale. During the meta-analysis, data from 62 globally published peer reviewed literature showed that cover crops in the corn production system increased SOC by an average of 7.8%. The SOC increased at rates of 0.46 and 0.80 Mg/ha/year at the 0-15 and 0-30 cm soil depths respectively, due to cover crop planting. To meet the third objective, several different machine learning prediction models were tested, which included multiple linear regression (MLR), partial least square regression (PLSR), support vector machine (SVM), random forest (RF), and artificial neural network (ANN), on daily N2ON and CO2-C emission data which were measured from a decomposing cover crop in 2019 and 2020 at Aurora, SD, USA. Each models’ performance was accessed using coefficient of determination (R2) (higher values close to one were deemed ‘best’), root mean square error (RMSE) and mean absolute error (MAE), where lowest values were ‘best’. Out of all models, the RF model accounted for 73% and 85% of the variability explained in N2O-N and CO2-C emissions, respectively. Across the three objectives, we found that new analysis approaches such as machine learning and meta-analysis can be used to determine the carbon footprint and prediction of GHG emission from conservation agriculture practices such as planting cover crops