New methods for evaluating uncertainty also can be used to devise model simplification strategies

A holistic upscaling from the point source to the landscape scale requires incorporation of several interacting, complex components, adding substantial complexity above and beyond the agricultural system itself. Thus, a major consideration in environmental modeling is how to best capture essential interactions while maintaining models that are feasible to implement with available data and computational resources. Fig. 4 illustrates the various components linking point to landscape scales. A first element for the linkage from point to landscape is estimation of surface and subsurface fluxes and ecological transitions along the lateral scale. Coupling with landscape microclimate models provides the vertical inputs used by the agricultural systems models, as well as gradients along the landscape. Coupling with hydrological models provides water flow paths like surface runoff, vertical and lateral groundwater flow, and interactions between vadose and groundwater zones and with adjacent surface water bodies . Water quality models provides sediment and solute transport along the landscape controlled by water flows , and other effects like wind erosion. Integration and upscaling of landscapes into the watershed scale requires 3-dimensional coupling of the surface and subsurface water, energy and mass transfers. At this scale, the groundwater aquifer system typically transcends the boundaries of the watershed and necessitates analysis at the regional scale to evaluate not only the impacts of the cropping and animal production systems on water quantity and quality, but also feed backs from the hydrological system in the agricultural system . Further, mesoscale rainfall and evapotranspiration distribution models control the local surface and subsurface flow intensities,hydroponic pots pollution and abatement. At this scale, human effects through land-use changes as well as ecological dynamics and transitions on natural or protected lands are also an important and critical component to evaluate the overall sustainability of the agricultural system.

Current crop modeling upscaling approaches based on land use maps can be considered an efficient first-order approximation of the environmental linkages. For example, in the USA the US Geological Survey hierarchical system of Hydrologic Units Codes  is commonly used as the reference spatial mapping system to link spatially-explicit hydrological and crop yield simulations. Srinivasan et al. applied the Soil and Water Assessment Tool model to 8-digit subbasin HUCs in the Upper Mississippi River Basin and compared yields of the main crops with observed county-level USDA National Agricultural Statistical Survey data obtained for 1991–2001. SWAT uses spatially distributed watershed inputs to simulate hydrology, sediment and contaminant transport and cycling in soils and streams, and crop/vegetative uptake, growth and yields. Because many counties in the NASS database have missing data it was necessary to aggregate the crop yield data and simulation results to 4-digit sub-region HUCs . In general SWAT predicted crop yields satisfactorily over the long-term average for most 4-digit HUC , although errors greater than 20% were found for 14% of the HUCs studied. Further information on crop management may improve SWAT’s perform conclude that these errors stem likely from those predicting AET and soil moisture storage at these large aggregated scale, and “one could extend the validity and confidence in the model prediction of AET and soil moisture using a well-compared model on crop yield” . Thus, next generation models should consider the lateral connections through the landscape and regional scales to evaluate the sustainability of the integrated system, including effects on water and soil resources quality and quantity and ecological value. Although model complexity has increased in recent years and is a natural outcome of the proposed next generation integrated modeling, there has been little work to rigorously characterize the threshold of relevance in integrated and complex models. Formally assessing the relevance of the model in the face of increasing complexity would be valuable because there is growing unease among developers and users of complex models about the cumulative effects of various sources of uncertainty on model outputs .

New approaches have been proposed recently to evaluate the uncertainty-complexity relevance modeling trilemma , or to identify which parts of a model are redundant in particular simulations . Innovative approaches to simplify model outcomes to make them relevant in decision-making will be central to the next generation modeling efforts. For example, the identification of processes that do not influence particular scenarios, and the use of meta models, could allow simplification without affecting results .This thesis is divided into two sections. In the first section, a technical primer is given to provide a starting point for readers interested in sensor, printing, and machine learning technologies. In the second section, these three technologies are combined to demonstrate my dissertation work in developing nitrogen sensor nodes for precision farming applications. Grain growers apply on the order of a hundred to a few hundred pounds of nitrogen per acre, depending on the crop and field conditions. At a cost of tens of cents to a dollar per pound, with prices rapidly increasing in recent months, it is the second highest cost for many crops, outdone only by seeds. Nitrate fertilizer is conventionally applied uniformly across a field despite studies that have shown existing nitrate concentration in the soil can vary significantly on the order of tens of meters. Precision agriculture practitioners aim to designate site-specific management zones to direct more efficient nitrogen application, but the tools they have to gather data are limited. Optical remote sensing can be used to estimate nitrogen in growing plant material, but to get measurements of nitrate in the soil, a soil sample must be collected and taken back to a laboratory, for analysis via chromatography or spectrographic methods. Such measurements are highly accurate, but they are also expensive, labor-intensive, and give data for only one point in time and space. Nitrate is highly mobile, so concentrations change over time. Models can be developed to estimate nitrate fluxes based on measurements at the beginning and end of a season, but these rely on many estimations and assumptions. Environmental quality monitoring and precision agriculture require nitrate sensors that are robust enough to survive field deployment and soil insertion, can be mass-produced, and involve few or no moving parts. Additionally, the data must be simple to read. Printed solid-state potentiometric ion-selective electrode sensors have the potential to meet these criteria. The use of printing methods for sensor fabrication offers several advantages such as low cost, high throughput, and ease of fabrication.

In order to realize the benefits of printing and enable large-scale sensor deployment, both electrodes must be printed.Previous works have shown printed nitrate ISEs for use in aqueous environments and agriculture. Dam et al.demonstrated potentiometric nitrate sensors having a screen-printed nitrate ISE paired with a commercial RE for agriculture applications. Similarly, inkjet-printed nitrate ISEs were reported by Jiang et al. using a commercial Ag/AgCl reference electrode during measurements. In this work, we demonstrate fully printed, potentiometric nitrate sensors and characterized their sensitivity, selectivity, and stability. We then integrated the sensors into a wireless sensor node and characterized its sensitivity to nitrate concentration and moisture levels in the soil. We then replaced the components of the nitrate sensor node with naturally degradable components and characterized the devices. We propose a model-driven paradigm of measuring these sensors using swarms of UAV drones whose flight paths are optimized using machine learning. Finally, we demonstrate the need for sensor arrays to account for the interference that different analytes could cause,grow pot and provide preliminary results for a nitrogen sensor array that measures nitrate, nitrite, and ammonium concentration in aqueous solutions.A sensor is a device that is able to detect and measure some physical quantity of interest and communicate that information to another device or person. A common example of a sensor that you might recognize is the liquid-in-glass thermometer, shown in Figure 1.1. In this type of thermometer, a thin glass tube is filled with a small volume of liquid mercury that collects in a bulb at the bottom. Then, as temperature changes, the mercury expands or contracts in response, causing the peak of the mercury to move up or down the long stem. The stem, as you may know, is calibrated and marked with numbers corresponding to the temperature in Fahrenheit, Celsius, or both. In this example, the sensor detects the change in temperature by the liquid expanding or contracting in the glass tube. The temperature is measured and communicated to a person by the numbered ticks on the thermometer stem. A sensor should not be mistaken for a detector, which is able to detect and communicate a physical quantity, but fails to measure it. Consider for example a smoke detector, such as the one shown in Figure 1.1B. A smoke detector is able to detect whether or not there is smoke, but it doesn’t measure how much smoke there is. To a smoke detector, there is no distinction between a blazing house fire and overcooked salmon: both cause it to brazenly communicate its detection of smoke.Contrary to the thermometer example above, most modern sensors are electronic devices, which will be the type of sensor that will be discussed in this dissertation.

Many electronic sensors work by having some material that is sensitive to the physical quantity that is being measured, causing a property of that material to change with respect to the physical quantity. Other electronic sensors take advantage of natural laws, such as conductive metal wires arranged in a loop to measure the strength of the magnetic field that it’s in. Later in Section 1.2, we will go over the various types of sensors and the transduction mechanisms that a sensor might use. However, regardless of the mechanism or the physical property that is being measured, all electronic sensors have a sensing element that converts the signal of the physical quantity to an electric signal. As a quick aside, the opposite of a sensor is an actuator, which converts an electric signal into a mechanical action. Some common examples of actuators are electrical motors, hydraulic pumps, or pneumatic valves. Sensors and actuators are both transducers, which is a device that converts energy from one form to another. The distinction here is the intended purpose of the device: sensors measure and detect, while actuators perform an action. A more quantitative way of thinking of this is to look at energy conversion efficiency. The efficiency of energy conversion for sensors is immaterial because their purpose is to detect and measure. For example, if one sensor is 10% efficient at energy conversion but is less accurate than a second sensor that is only 2% efficient, then the second sensor is still an objectively better sensor of the two sensors because it is better at detecting and measuring. Contrarily, efficiency is an important metric for actuators because their purpose is to perform an action. An electric motor with a 2W load is objectively better than a motor with a 5W load, assuming they perform the same task equally well. The organization and classification of sensors vary throughout the academic literature and commercial marketplace. This is because there really is no perfect form of organization, as there are many ‘one-off’ devices that sense for some unique purpose or by some unique method. Further, there are many ways to categorize sensors: sensor specifications, sensor materials, transduction mechanisms, the quantity being measured, the field of application, whether the sensor is active or passive, direct or complex, and many, many more. It is analogous to classifying humans: humans can be classified by their age, gender, race, nationality, preferred sports team, favorite color, or the size of their ears. Similarly, sensors can be classified in many such ways.Sensors can also be classified as passive or active types. The distinction is simple: active sensors provide their own energy source to operate, while passive sensors use naturally available energy. An interesting example of both an active and a passive sensor is a camera. In a brightly lit location, natural light will illuminate the photographed subjects and then reflect toward the camera lens, where the camera simply records the radiation provided . In a dark room, however, there won’t be enough ambient light for the camera to record the subjects adequately. Instead, the camera uses its own energy source – the ‘flash’ – to illuminate the subjects and record the radiation reflected off them .

Sustainable development is now globally accepted as a supreme long-term goal of humanity

The recent publication of two almond reference genomes and the increasing availability of quality genomic data opens opportunities to complement our study and obtain more complete and accurate pedigrees based on genomic variability. This kind of studies can be useful even when some genotypes were discarded due to breeding process, as is the case in our almond pedigree work. Although almond showed a higher genetic variability than other Prunus species, the historical expansion of almond from the Mediterranean region to California and from California to Australia could have caused a bottleneck effect in the breeding population under study. Different studies have reported a high genetic relatedness between Australian and Californian cultivars, possibly caused by the introduction of a limited number of cultivars from Europe to these countries. In addition, breeding programs worldwide have used cultivars from French origin as main founders as Aï, Princesse, Ardechoise, Nonpareil, IXL, Ne Plus Ultra, or Nikitskij. This situation could have led to an underestimation of relatedness and inbreeding. The use of large-scale genomic data would provide most valuable information in this respect,plastic pots plants expanding the almond pedigree beyond breeding records.Man’s interaction with nature is rapidly becoming more complex due to a multitude of activities that directly or indirectly cause a disturbance in the natural system. The deeper interactions between human activities and natural ecosystems call for an interdisciplinary approach to natural resources management, while the inputs from multiple disciplines need to be effectively utilized in achieving it. This prudent natural resources management will enable sustainable development of a region without losing the resource base. Sustainable development essentially aims to reconcile conflicting objectives of economic development and improvement in human welfare, and ecological sustenance and functioning of ecosystems.

The term sustainable development is defined by the Brundtland Commission as “the development that constitutes meeting needs of the current generation without compromising the ability of meeting needs of future generations” .Sustainable utilization of natural resources is essential for sustainable development. It follows that the sustainability of renewable natural resources in general and common property resources in particular assumes importance in both developed as well as developing countries because of the finite capacity of the resource base and the increasing demand for its exploitation. The technical definition of the term sustainability is given as, “the ability of a natural resource system to produce a socially optimum level of output that is necessary to meet the needs and aspirations of the people dependent on the system perpetually without any detrimental effects on the resource system itself and the physical environment, and with no imposition of significantly greater risks on future generations” . This might be comprehensive and deep rooted, but in other words, sustainability implies not only conserving natural resources but also maintaining ecological functions and the supply of natural resource products, which are essential to the livelihoods of local people. Sustainability in this sense is a dynamic concept that reflects changing levels of output corresponding with changing human needs and production technologies over time.Natural resources discussed here are broadly covered under land, water, and biomass . These three resources are crucial for production under various systems namely, agriculture, horticulture, silviculture, pisciculture, and animal husbandry. Management of these three major resources is crucial for making production in these systems sustainable and enhanced. Sustainability of these systems can be threatened due to disruption in the linkages between these resources. This may assume various forms, for example, increased soil erosion resulting in nutrient loss rendering it unfit for use in the case of land; soil water deficiency or excessiveness affecting its productivity and degradation in the case of water resources; decline in vegetation density and diversity leading to reduction in soil and water conserving properties in the case of biomass or vegetation resources, and so forth. A more detailed discussion of these follows.

Soil productivity can be defined as those properties of soil that influence crop production. The increased yields from better-managed soils are due to increased inputs and improved practices rather than with improvements in the basic fabric of soil . In recent years the sediment derived from soil erosion has been the major non-point source of pollution in surface water bodies while loss of in-situ topsoil has caused reduction in productivity. Erosion reduces long-term production potential and seldom improves the immediate capacity of eroded soil to sustain plant growth or produced crops . Results of recent studies show that soil physical and biological properties seem to be the predominant constraints to maximizing plant production on eroded soil, compared with chemical and fertilizer constraints. For example, Rosenberry, Knutson, and Harmon suggested that yields generally decline as soils shift from one erosion phase to another, even with increased fertilizer. This is attributed to surface soil physical and biological properties. However, in addition to irrigation, other water management techniques, such as surface mulching, can also be applied for amelioration of eroded soil. Crop productivity is also affected by moisture availability in soil. In the post-green revolution, particularly in the 1980s, instability in crop productivity increased on account of the rise in sensitivity of output to variations in rainfall in India . This increasing vulnerability of agricultural output to variations in rainfall, particularly during droughts when the soil moisture is scarce, is attributed to inadequate expansion of irrigation by these same authors. It is minor irrigation, which is not given priority that can be part of the strategy under watershed development. Similarly, decline in water quality has affected crop productivity in saline and alkaline lands that were created by excessive irrigation or polluted water in northern parts of India. At the same time, vegetation adds much to resource endowment and has crucial linkages with soil and water. Good vegetation cover functions as a soil and water-conserving agent, whereas, lack of vegetation will make the soil vulnerable to erosion and allow water to flush off the sediments. Biological diversity of vegetation is crucial in the survival of the vegetation itself and the sustenance of the ecosystem in that region. In fact, planning based on land use can effectively conserve soil as well as water. This is little elaborated under Production Systems Planning .

A distinction, however, needs to be made between the goals of attaining sustainability and of increasing productivity. While higher productivity may be required to achieve the sustainability goal, the requisite increase in productivity must be achieved in a manner that will not jeopardize the ability of a natural resource system to meet future needs. In other words, it is possible to achieve increases in productivity through unsustainable short-term approaches . The term watershed denotes the area defined by natural boundaries characterized by terrain , soils, and drainage delimitations. Watershed is an appropriate unit for environmental planning for sustainable management of natural resources of a region. Watershed Management is a practice of conserving soil, water, and biological resources using scientific principles,plastic nursery pots traditional and systems knowledge, and local resources with an objective of increasing crop productivity. It involves rational utilization of land and water resources for optimum production with minimum hazards to national resources. It essentially relates to soil and water conservation in the watershed which means land use according to land potential, protection of land against all kinds of deterioration, building and maintaining soil fertility, conserving water for farm use, proper management of water for drainage, flood protection, sediment reduction, and increasing productivity for all kinds of land uses . Watershed management has come into focus in India with the advent of productivity fluctuations with rainfall, necessitating micro-irrigation in drier parts, and also with the advent of space technology tools, which are useful in the micro-level planning. Land water related management projects and schemes have been implemented under various programs since the beginning of the Five Year plans. In particular, the third Five Year plan introduced the watershed as the basic hydrological unit for soil conservation planning and execution . Increased emphasis on watershed development programs for dry land plain regions in India, inter alia, is a manifestation of the shifting priorities in the agricultural sector, which until recently concentrated mainly on crops and regions with assured irrigation . Successful case studies of Ralegaonsiddhi, Myrada, are well known . In the sequence of evolution of natural resources management using the watershed approach for sustainability of these resources, thrust is on the productivity of natural systems. It is the productivity of natural systems that needs to be conserved through planning so that the needs of an increasing population are met and the threat to their renewability is thwarted. The watershed approach is an ideal approach to carry out a planning operation, and its planning framework shall fit well under the implementation and execution activities. Central to the success of the process is the participation of the population during the crucial implementation. Production systems planning is a method of planning for the use of natural resources under the watershed approach with a focus on ecological characteristics. It essentially involves spatial allocation of land use for various production systems, namely, agriculture, horticulture, and animal husbandry, by which conservation goals are met through better decision-making. PSP is similar to regional land use planning, but it differs from it in that it depends on ecological characteristics at the watershed level rather than activities at the regional level. However, in both cases land use is an important element.

The role of production systems in soil and water conservation is evident from the water and soil losses of catchments under such production systems such as mentioned by Mallik . Fruit development is mediated by plant growth regulators that control its major developmental processes. As grape berries develop, they exhibit a double sigmoid growth curve separated by veraison that marks the beginning of ripening. Cell division and expansion are the major events during the first phase and are accompanied by synthesis and accumulation of organic acids, methoxypyrazines, and phenolic compounds such as proanthocyanidins and hydroxycinnamates. This phase of berry growth is under the control of auxins, gibberellins, and cytokinins. Auxins and gibberellins are mostly produced by the seeds while the source of cytokinins in fruits is less established and it is likely to be imported from the plant. The number of seeds in the berry can determine berry size, and the lack of seeds or the presence of unfertilized ovules in sternospermocarpic berries can be partly complemented by external gibberellin. Schematic representation of the levels of PGRs during development suggests that auxin levels are high at berry set and decrease during phase 1 while cytokinins and gibberellins peak during phase. The second phase, verasion, marks the beginning of major processes in grapes ripening, berry softening and anthocyanins accumulation in colored varieties. There are reports of a small peak of ethylene preceded by a large peak of abscisic acid which coincide with veraison. Brassinosteroids increase at veraison and participate in ripening, possibly by modulation of ethylene content. On the other hand, auxin treatments retard sugar andanthocyanin accumulation and prevent the decrease in acidity and chlorophyll concentration, and also cause a delay in the usual ripening-associated increase in the levels of ABA. The third phase in berry development is ripening which is characterized by accumulation of glucose and fructose, as well as a decrease in the levels of organic acids. Volatile compounds that are produced by grape berries during development and ripening include fatty acid derivatives that are the most abundant group, monoterpenes that are prominent flavor compounds in Muscat flflavored grapes, sesquiterpenes, C13 norisoprenoids, volatile sulfur compounds, and methoxypyrazines. Some volatile compound types such as methoxypyrazines and sesquiterpenes, C13 norisoprenoids and volatile fatty acid derivatives accumulate in the berry before veraison while volatile monoterpenes and volatile sulfur compounds accumulate during ripening . Physiological studies on the role of PGRs in fruit development often rely on external applications of the hormones or their agonist followed by observations of the changes in fruit development. In table grapes there is a plethora of applicative studies aimed at increasing the berry size in ‘seedless’ varieties and the color of red varieties grown in hot regions. The effect of GA was studied from the late 1950s with timing and concentration being major factors. In ‘seedless’ grapes, application of GA to increase berry size is performed at a fruitlet diameter of 4–6 mm because earlier application can have negative impacts on fruit-set and berry shot and later application is less effective.