One of the most interesting outcomes from the recent collaboration between natural and social scientists is the concept of resilience, which imported from engineering to ecology. The problem with that concept is that ...One of the most interesting outcomes from the recent collaboration between natural and social scientists is the concept of resilience, which imported from engineering to ecology. The problem with that concept is that it is hard if not impossible to get simple measures for resilience as far as social-ecological systems are complex ones. Using a system dynamics model, the author shows that, for assessing resilience of systems like irrigation systems, it probably helps to see the process of resilience loss as a systemic one, in which dynamics is given by positive self-reinforcing loops, like the one we have labeled in this paper--the death spiral. The author also presents a list of symptoms of collapse in irrigation systems, in order to assess the resilience of those systems, and suggest some future avenues of research on the subject.展开更多
Comparative performance analysis of four irrigation schemes within Cagayan River Basin was assessed using comparative performance indicators between the years 2008 and 2012. The objectives were to establish benchmarks...Comparative performance analysis of four irrigation schemes within Cagayan River Basin was assessed using comparative performance indicators between the years 2008 and 2012. The objectives were to establish benchmarks for both productivity and performance of irrigation schemes along the valley and to inquire whether small schemes function better than large schemes. The performance evaluation study of the systems composed of three general performance indicators, based on three domains-（1） system operation performance; （2） agricultural productivity and economics; （3） financial performance. Each indicator was assessed based on the prescribed descriptors used by the International Water Management Institute （IWMI） and Food and Agriculture Organization （FAO）. Analysis showed an overall system performance efficiency of 59%, 55%, 47% and 36% for Magat River Integrated Irrigation System （MARI1S）, Lucban, Garab and Divisoria Communal Irrigation Systems （CIS）, respectively. In terms of annual productivity performance, Lucban CIS dominates the three other systems with 0.35 kg/m3, which was classified as moderately performing system, while the rest were classified with low productivity index. Financial sustainability of the systems were extremely poor with cost recovery ratio of 0, 0.33, 0.41 and 0.49 for Divisoria, Garab, Lucban and MARIIS, respectively, which were exceptionally below the standard value of at least one. Also, analysis of the indicators revealed that on average, large schemes performed similarly to small-scale schemes, but small schemes were more variable, particularly in input-use efficiency. The benchmarking study will provide strategic information to policy makers of agricultural and irrigation agencies on the existing weaknesses of irrigation systems in the country and determine in a more quantifiable terms levels of potential improvement and intervention targets.展开更多
Farmer-managed irrigation systems(FMIS) in the high altitude valleys of the Karakorum,Pakistan, continue to be managed effectively despite increased pressure on the social arrangements that sustain them. Colonial era ...Farmer-managed irrigation systems(FMIS) in the high altitude valleys of the Karakorum,Pakistan, continue to be managed effectively despite increased pressure on the social arrangements that sustain them. Colonial era records shows that over a century ago government agencies undertook irrigation support projects. In the past three decades,government agencies and the non-government agency Aga Khan Rural Support Programme(AKRSP), which channels foreign funds into the region, have actively engaged in the provision of irrigation support. This article seeks to explore whether such projects support or undermine farmer-managed irrigation systems and the complex institutional arrangements that underpin them. Field research using ethnographic and participatory methods was conducted in spring 2013 in the upper Shigar valley, Skardu district, GilgitBaltistan. The findings show that irrigation development is a political activity that involves village-based actors, religious leaders, local politicians,and government and non-government agencies.Government agencies operate in a largely top-down,engineering mode, their larger projects limited to villages suffering water scarcity. The local government provides small funds for renovation work of FMIS,though allocation of funds is highly politicized. Nongovernment agencies, for a variety of reasons including donor-funding cycles, apply a one-size-fitsall ‘participatory' model in an attempt to socially engineer rules and institutions. In communities divided by factionalism the use of such external models that stress formation of committees are unlikely to yield positive results, and could instead contribute to undermining the very systems they seek to support. This research argues that irrigation interventions should take care to build upon the rich and complex social arrangements that have sustained FMIS through the centuries.展开更多
Improving irrigation water management is a key concern for the agricultural sector,and it requires extensive and comprehensive tools that provide a complete knowledge of crop water use and requirements.This study pres...Improving irrigation water management is a key concern for the agricultural sector,and it requires extensive and comprehensive tools that provide a complete knowledge of crop water use and requirements.This study presents a novel methodology to explicitly estimate daily gross and net crop water requirements,actual crop water use,and irrigation efficiency of center pivot irrigation systems,by mainly utilizing the Sentinel-2 MultiSpectral Instrument(MSI)imagery at the farm scale.ETMonitor model is adapted to estimate actual water use(as the sum of canopy transpiration and evaporation of water intercepted by canopy and evaporation from soil)at daily/10-m resolution,benefiting from the high-resolution Sentinel-2 data and thus to assess the irrigation efficiency at the farm scale.The gross irrigation water requirement is estimated from the net crop water requirement and the water loss,including the water droplet evaporation directly into the air during application before droplets fall on the canopy and canopy interception loss.The method was applied to a pilot farmland with two major crops(wheat and potato)in the Inner Mongolia Autonomous Region of China,where modern equipment and appropriate irrigation methods are deployed for efficient water use.The estimated actual crop water use showed good agreement with the ground observations,e.g.the determination coefficients range from 0.67 to 0.81 and root mean square errors range from 0.56 mm/day to 1.24 mm/day for wheat and potato when comparing the estimated evapotranspiration with the measurement by the eddy covariance system.It also showed that the losses of total irrigated volume were 25.4%for wheat and 23.7%for potato,respectively,and found that the water allocation was insufficient to meet the water requirement in this irrigated area.This suggests that the amount of water applied was insufficient to meet the crop water requirement and the inherent water losses in the center pivot irrigation system,which imply the necessity to improve the irrigation practice to use the water more efficiently.展开更多
In Northern Nigeria, irrigation systems are operated manually. Agriculture has over the years been practiced primitively by farmers, especially in sub-Saharan Africa. This is due to the absence of intelligent technolo...In Northern Nigeria, irrigation systems are operated manually. Agriculture has over the years been practiced primitively by farmers, especially in sub-Saharan Africa. This is due to the absence of intelligent technological know-how where its practice could be leveraged upon. Agricultural practice is constrained by some major challenges ranging from traditional way of farming, understating of concepts, practices, policy, environmental and financial factors. The aim of this study was to optimize an IoT-based model for smart agriculture and irrigation water management. The objectives of the study were to: design, implement, test and evaluate the performance of the optimized IoT-based model for smart agriculture and irrigation water management. The method used in the study was the prototyping model. The system was designed using balsamiq application tools. The system has a login page, dashboard, system USE-CASE diagrams, actuators page, sensor page and application interface design. Justinmind tool was used to show the flow of information in the system, which included data input and output, data stores and all the sub-processes the data moves through. The Optimized IoT model was implemented using four core platforms namely, ReactJS Frontend Application development platform, Amazon web services IoT Core backend, Arduino Development platform for developing sensor nodes and Python programming language for the actuator node based on Raspberry Pi board. When compared with the existing system, the results show that the optimized system is better than the existing system in accuracy of measurement, irrigation water management, operation node, platform access, real-time video, user friendly and efficiency. The study successfully optimized an IoT-based model for smart agriculture and irrigation water management. The study introduced the modern way of irrigation farming in the 21<sup>st</sup> century against the traditional or primitive way of irrigation farming that involved intensive human participation.展开更多
Context:In the dynamic and constantly evolving world of agriculture,promoting innovation and ensuring sustainable growth are crucial.A planned division of tasks and responsibilities within agricultural systems,known a...Context:In the dynamic and constantly evolving world of agriculture,promoting innovation and ensuring sustainable growth are crucial.A planned division of tasks and responsibilities within agricultural systems,known as efficient role allocation,is necessary to make this vision a reality.Climate-smart agriculture(CSA)movement enjoys widespread support from the research and development community because it seeks to improve livelihoods in response to climate change.Objective:This study explores an innovative approach to optimizing role assignment within agricultural frameworks to effectively scale AI-driven innovations.By leveraging advanced algorithms and machine learning techniques,the research aims to streamline the allocation of tasks and responsibilities among various stakeholders,including farmers,agronomists,technicians,and AI systems.Methods:The methodology involves the development of a dynamic role assignment model that considers factors such as expertise,resource availability,and real-time environmental data.This model is tested in various agricultural scenarios to evaluate its impact on operational efficiency and innovation scalability.The findings demonstrate that optimized role assignment not only enhances the performance of AI applications but also fosters a collaborative ecosystem that is adaptable to changing agricultural demands.Results:&Discussion:This research finds a number of elements that affect how well duties are distributed within agricultural frameworks,including organizational frameworks,leadership,resource accessibility,and cooperative efforts through AI.In addition to advocating for its comprehensive integration into the sector's culture,this paper offers a collection of best practices and techniques for optimizing role allocation in agriculture.Additionally,the study gives a thorough overview,summary,and analysis of a few papers that are specifically concerned with scaling innovation in the field of agricultural research for development.Significance:Furthermore,the study highlights the potential of AI to transform traditional farming practices,reduce labor-intensive processes,and improve decision-making accuracy.The proposed approach serves as a blueprint for agricultural enterprises aiming to adopt AI technologies while ensuring optimal utilization of human and technological resources.By addressing the challenges of role ambiguity and resource allocation,this research contributes to the broader goal of achieving sustainable and resilient agricultural systems through technological innovation.展开更多
The paper presents an efficient form of growing arugula plants by means of automatic control of an aeroponic culture irrigation system.The system considers a reprogrammable electronic circuit that uses software to gen...The paper presents an efficient form of growing arugula plants by means of automatic control of an aeroponic culture irrigation system.The system considers a reprogrammable electronic circuit that uses software to generate different irrigation cycles to obtain an adequate growth of arugula crops.Results show how different samples grown in a greenhouse had the same growth behavior as field-grown samples during the test period.It was possible to obtain a more efficient and sustained five-week production to supply consumers by having a continuous cycle irrigation system,which was operated for 35 d.The growth and number of leaves were maintained in a similar way for different plants analyzed.Roots grow similarly,but some of them showed size differences during the five weeks.展开更多
This research aims to enhance the performance of photovoltaic(PV)systems on a 2-fold basis.Firstly,it introduces an advanced deep artificial neural network algorithm for accurate and fast maximum power point tracking,...This research aims to enhance the performance of photovoltaic(PV)systems on a 2-fold basis.Firstly,it introduces an advanced deep artificial neural network algorithm for accurate and fast maximum power point tracking,ensuring optimal extraction of electrical power from PV arrays.Secondly,it proposes the use of 96-V,2.98-kW direct-current(DC)water pumps for farm irrigation,aiming to improve efficiency,reduce cost and complexity,and overcome challenges associated with connecting faraway farm irrigation systems to the utility grid.In this study,it has been demonstrated that the use of DC pumps greatly improves system performance and efficiency by eliminating the need for isolation transformers,power passive filters and inverters,therefore simplifying the architecture of the system.The efficacy of the proposed methodology is confirmed by MATLAB®/Simulink®simulation results,whereby the proposed algorithm attains a mean squared error of 6.5705×10^(-5)and a system efficiency approaching 99.8%,ensuring a steady voltage under varying load conditions.展开更多
Lodging occurs when the crop canopy is too heavy for the strength of the stem and it fallsover onto the ground. This decreases crop yield and quality, and it makes harvest difficult.A research experiment was set up in...Lodging occurs when the crop canopy is too heavy for the strength of the stem and it fallsover onto the ground. This decreases crop yield and quality, and it makes harvest difficult.A research experiment was set up in a spearmint field on a center pivot with mid elevationspray application (MESA) overhead sprinklers, where the water was applied from a “midelevation” of 2 m above the ground level (AGL), and low elevation precision application(LEPA) sprinklers, where the water was emitted directly onto the soil surface through draghoses without wetting the crop canopy. Every-other span of this full-size center pivot wasconfigured with MESA and LEPA sprinklers alternatively. In 2018, imagery was collectedwith an unmanned aerial vehicle (UAV) from a cross section of this field. In 2019, a crosssection was again collected, but in addition UAV imagery was collected from marked lodgedand un-lodged areas of the field to validate the lodging detection method. These UAV-basedimagery data were captured with a ground sample distance (GSD) of 0.03 m. This researchintroduces using the texture feature, which is based on image entropy, was used to evaluate the degree of lodging. The results from 2018 showed that the average entropy of thegrayscale image from LEPA (5.5 (mean) ± 0.27 (standard deviation)) was significantly(P < 0.0001) greater than the average entropy (5.0 ± 0.25) of MESA. Also, the entropy valueextracted from the images in 2019 from the marked un-lodged locations were significantlyhigher compared to that of the lodged areas. Overall, the LEPA irrigation treatment was significantly less lodged compared to MESA. Moreover, the entropy value, or texture feature, isa viable method for estimating lodging using low altitude RGB imagery.展开更多
文摘One of the most interesting outcomes from the recent collaboration between natural and social scientists is the concept of resilience, which imported from engineering to ecology. The problem with that concept is that it is hard if not impossible to get simple measures for resilience as far as social-ecological systems are complex ones. Using a system dynamics model, the author shows that, for assessing resilience of systems like irrigation systems, it probably helps to see the process of resilience loss as a systemic one, in which dynamics is given by positive self-reinforcing loops, like the one we have labeled in this paper--the death spiral. The author also presents a list of symptoms of collapse in irrigation systems, in order to assess the resilience of those systems, and suggest some future avenues of research on the subject.
文摘Comparative performance analysis of four irrigation schemes within Cagayan River Basin was assessed using comparative performance indicators between the years 2008 and 2012. The objectives were to establish benchmarks for both productivity and performance of irrigation schemes along the valley and to inquire whether small schemes function better than large schemes. The performance evaluation study of the systems composed of three general performance indicators, based on three domains-（1） system operation performance; （2） agricultural productivity and economics; （3） financial performance. Each indicator was assessed based on the prescribed descriptors used by the International Water Management Institute （IWMI） and Food and Agriculture Organization （FAO）. Analysis showed an overall system performance efficiency of 59%, 55%, 47% and 36% for Magat River Integrated Irrigation System （MARI1S）, Lucban, Garab and Divisoria Communal Irrigation Systems （CIS）, respectively. In terms of annual productivity performance, Lucban CIS dominates the three other systems with 0.35 kg/m3, which was classified as moderately performing system, while the rest were classified with low productivity index. Financial sustainability of the systems were extremely poor with cost recovery ratio of 0, 0.33, 0.41 and 0.49 for Divisoria, Garab, Lucban and MARIIS, respectively, which were exceptionally below the standard value of at least one. Also, analysis of the indicators revealed that on average, large schemes performed similarly to small-scale schemes, but small schemes were more variable, particularly in input-use efficiency. The benchmarking study will provide strategic information to policy makers of agricultural and irrigation agencies on the existing weaknesses of irrigation systems in the country and determine in a more quantifiable terms levels of potential improvement and intervention targets.
基金funded by the Federal Ministry of Education and Research (BMBF), Germany
文摘Farmer-managed irrigation systems(FMIS) in the high altitude valleys of the Karakorum,Pakistan, continue to be managed effectively despite increased pressure on the social arrangements that sustain them. Colonial era records shows that over a century ago government agencies undertook irrigation support projects. In the past three decades,government agencies and the non-government agency Aga Khan Rural Support Programme(AKRSP), which channels foreign funds into the region, have actively engaged in the provision of irrigation support. This article seeks to explore whether such projects support or undermine farmer-managed irrigation systems and the complex institutional arrangements that underpin them. Field research using ethnographic and participatory methods was conducted in spring 2013 in the upper Shigar valley, Skardu district, GilgitBaltistan. The findings show that irrigation development is a political activity that involves village-based actors, religious leaders, local politicians,and government and non-government agencies.Government agencies operate in a largely top-down,engineering mode, their larger projects limited to villages suffering water scarcity. The local government provides small funds for renovation work of FMIS,though allocation of funds is highly politicized. Nongovernment agencies, for a variety of reasons including donor-funding cycles, apply a one-size-fitsall ‘participatory' model in an attempt to socially engineer rules and institutions. In communities divided by factionalism the use of such external models that stress formation of committees are unlikely to yield positive results, and could instead contribute to undermining the very systems they seek to support. This research argues that irrigation interventions should take care to build upon the rich and complex social arrangements that have sustained FMIS through the centuries.
基金supported by the National Natural Science Foundation of China(NSFC)[Grant no.42090014,42171039,42271394]the Open Research Program of the International Research Center of Big Data for Sustainable Development Goals[Grant no.CBAS2023ORP05]+2 种基金ESA-NSRCC Dragon 5 Program[Grant no.59061]MOST High Level Foreign Expert program[Grant No.G2022055010L]the Chinese Academy of Sciences President’s International Fellowship Initiative[Grant No.2020VTA0001].
文摘Improving irrigation water management is a key concern for the agricultural sector,and it requires extensive and comprehensive tools that provide a complete knowledge of crop water use and requirements.This study presents a novel methodology to explicitly estimate daily gross and net crop water requirements,actual crop water use,and irrigation efficiency of center pivot irrigation systems,by mainly utilizing the Sentinel-2 MultiSpectral Instrument(MSI)imagery at the farm scale.ETMonitor model is adapted to estimate actual water use(as the sum of canopy transpiration and evaporation of water intercepted by canopy and evaporation from soil)at daily/10-m resolution,benefiting from the high-resolution Sentinel-2 data and thus to assess the irrigation efficiency at the farm scale.The gross irrigation water requirement is estimated from the net crop water requirement and the water loss,including the water droplet evaporation directly into the air during application before droplets fall on the canopy and canopy interception loss.The method was applied to a pilot farmland with two major crops(wheat and potato)in the Inner Mongolia Autonomous Region of China,where modern equipment and appropriate irrigation methods are deployed for efficient water use.The estimated actual crop water use showed good agreement with the ground observations,e.g.the determination coefficients range from 0.67 to 0.81 and root mean square errors range from 0.56 mm/day to 1.24 mm/day for wheat and potato when comparing the estimated evapotranspiration with the measurement by the eddy covariance system.It also showed that the losses of total irrigated volume were 25.4%for wheat and 23.7%for potato,respectively,and found that the water allocation was insufficient to meet the water requirement in this irrigated area.This suggests that the amount of water applied was insufficient to meet the crop water requirement and the inherent water losses in the center pivot irrigation system,which imply the necessity to improve the irrigation practice to use the water more efficiently.
文摘In Northern Nigeria, irrigation systems are operated manually. Agriculture has over the years been practiced primitively by farmers, especially in sub-Saharan Africa. This is due to the absence of intelligent technological know-how where its practice could be leveraged upon. Agricultural practice is constrained by some major challenges ranging from traditional way of farming, understating of concepts, practices, policy, environmental and financial factors. The aim of this study was to optimize an IoT-based model for smart agriculture and irrigation water management. The objectives of the study were to: design, implement, test and evaluate the performance of the optimized IoT-based model for smart agriculture and irrigation water management. The method used in the study was the prototyping model. The system was designed using balsamiq application tools. The system has a login page, dashboard, system USE-CASE diagrams, actuators page, sensor page and application interface design. Justinmind tool was used to show the flow of information in the system, which included data input and output, data stores and all the sub-processes the data moves through. The Optimized IoT model was implemented using four core platforms namely, ReactJS Frontend Application development platform, Amazon web services IoT Core backend, Arduino Development platform for developing sensor nodes and Python programming language for the actuator node based on Raspberry Pi board. When compared with the existing system, the results show that the optimized system is better than the existing system in accuracy of measurement, irrigation water management, operation node, platform access, real-time video, user friendly and efficiency. The study successfully optimized an IoT-based model for smart agriculture and irrigation water management. The study introduced the modern way of irrigation farming in the 21<sup>st</sup> century against the traditional or primitive way of irrigation farming that involved intensive human participation.
文摘Context:In the dynamic and constantly evolving world of agriculture,promoting innovation and ensuring sustainable growth are crucial.A planned division of tasks and responsibilities within agricultural systems,known as efficient role allocation,is necessary to make this vision a reality.Climate-smart agriculture(CSA)movement enjoys widespread support from the research and development community because it seeks to improve livelihoods in response to climate change.Objective:This study explores an innovative approach to optimizing role assignment within agricultural frameworks to effectively scale AI-driven innovations.By leveraging advanced algorithms and machine learning techniques,the research aims to streamline the allocation of tasks and responsibilities among various stakeholders,including farmers,agronomists,technicians,and AI systems.Methods:The methodology involves the development of a dynamic role assignment model that considers factors such as expertise,resource availability,and real-time environmental data.This model is tested in various agricultural scenarios to evaluate its impact on operational efficiency and innovation scalability.The findings demonstrate that optimized role assignment not only enhances the performance of AI applications but also fosters a collaborative ecosystem that is adaptable to changing agricultural demands.Results:&Discussion:This research finds a number of elements that affect how well duties are distributed within agricultural frameworks,including organizational frameworks,leadership,resource accessibility,and cooperative efforts through AI.In addition to advocating for its comprehensive integration into the sector's culture,this paper offers a collection of best practices and techniques for optimizing role allocation in agriculture.Additionally,the study gives a thorough overview,summary,and analysis of a few papers that are specifically concerned with scaling innovation in the field of agricultural research for development.Significance:Furthermore,the study highlights the potential of AI to transform traditional farming practices,reduce labor-intensive processes,and improve decision-making accuracy.The proposed approach serves as a blueprint for agricultural enterprises aiming to adopt AI technologies while ensuring optimal utilization of human and technological resources.By addressing the challenges of role ambiguity and resource allocation,this research contributes to the broader goal of achieving sustainable and resilient agricultural systems through technological innovation.
基金This work was supported by the Universidad Nacional de Colombia Sede Medellín under the projects HERMES 45887.The authors thank COLCIENCIAS,the National Doctorate program and the laboratory of the research group Scientific and Industrial Instrumentation of the School of Physics and the Department of Electrical Energy and Automation for their valuable support to conduct this research.
文摘The paper presents an efficient form of growing arugula plants by means of automatic control of an aeroponic culture irrigation system.The system considers a reprogrammable electronic circuit that uses software to generate different irrigation cycles to obtain an adequate growth of arugula crops.Results show how different samples grown in a greenhouse had the same growth behavior as field-grown samples during the test period.It was possible to obtain a more efficient and sustained five-week production to supply consumers by having a continuous cycle irrigation system,which was operated for 35 d.The growth and number of leaves were maintained in a similar way for different plants analyzed.Roots grow similarly,but some of them showed size differences during the five weeks.
文摘This research aims to enhance the performance of photovoltaic(PV)systems on a 2-fold basis.Firstly,it introduces an advanced deep artificial neural network algorithm for accurate and fast maximum power point tracking,ensuring optimal extraction of electrical power from PV arrays.Secondly,it proposes the use of 96-V,2.98-kW direct-current(DC)water pumps for farm irrigation,aiming to improve efficiency,reduce cost and complexity,and overcome challenges associated with connecting faraway farm irrigation systems to the utility grid.In this study,it has been demonstrated that the use of DC pumps greatly improves system performance and efficiency by eliminating the need for isolation transformers,power passive filters and inverters,therefore simplifying the architecture of the system.The efficacy of the proposed methodology is confirmed by MATLAB®/Simulink®simulation results,whereby the proposed algorithm attains a mean squared error of 6.5705×10^(-5)and a system efficiency approaching 99.8%,ensuring a steady voltage under varying load conditions.
文摘Lodging occurs when the crop canopy is too heavy for the strength of the stem and it fallsover onto the ground. This decreases crop yield and quality, and it makes harvest difficult.A research experiment was set up in a spearmint field on a center pivot with mid elevationspray application (MESA) overhead sprinklers, where the water was applied from a “midelevation” of 2 m above the ground level (AGL), and low elevation precision application(LEPA) sprinklers, where the water was emitted directly onto the soil surface through draghoses without wetting the crop canopy. Every-other span of this full-size center pivot wasconfigured with MESA and LEPA sprinklers alternatively. In 2018, imagery was collectedwith an unmanned aerial vehicle (UAV) from a cross section of this field. In 2019, a crosssection was again collected, but in addition UAV imagery was collected from marked lodgedand un-lodged areas of the field to validate the lodging detection method. These UAV-basedimagery data were captured with a ground sample distance (GSD) of 0.03 m. This researchintroduces using the texture feature, which is based on image entropy, was used to evaluate the degree of lodging. The results from 2018 showed that the average entropy of thegrayscale image from LEPA (5.5 (mean) ± 0.27 (standard deviation)) was significantly(P < 0.0001) greater than the average entropy (5.0 ± 0.25) of MESA. Also, the entropy valueextracted from the images in 2019 from the marked un-lodged locations were significantlyhigher compared to that of the lodged areas. Overall, the LEPA irrigation treatment was significantly less lodged compared to MESA. Moreover, the entropy value, or texture feature, isa viable method for estimating lodging using low altitude RGB imagery.
