Despite this, some chemical treatments have been effective. For example, methyl anthranilate, a common food additive, is used as a biodegradable non-toxic bird repellant for grapes and berries. One study showed a decrease in crop loss by 88% to 99% when crops were treated with methyl anthranilate . However, in another study, methyl anthranilate was not effective against frugivorous bird species in the northeastern US . Anthraquinone is another commonly used chemical used to deter birds. There was up to a 93% decrease in rice consumption by blackbirds and grackles when seeds were treated with anthraquinone before planting . Horned larks are also affected by anthraquinone, damaging 60% of treated lettuce seedlings but 100% of untreated seedlings . Other chemicals, including 3-chloro-4-methylaniline hydrochloride, 3- chloro ptoluidine hydrochloride, and 3-chloro-4- methylaniline all were able to control starlings at a concentrated animal feeding operation , thereby eliminating Salmonella enterica from feedbunks and causing a substantial decline of the pathogen in the water troughs . In developing a multinational plan for bird control, there were two important recommendations: 1) Collect better data about bird pest damage and 2) develop alternative approaches to toxicants or develop environmentally safe toxicants. . Lethal measures used to control birds include avicides, shooting, egg destruction, and nest destruction. Not only are these measures expensive and time consuming, they are generally frowned upon by the public since they are in direct contrast to the concepts of wildlife protection and environmental stewardship. Linz, Bucher, et. al. assert that lethal control of birds is not an effective or appropriate method to prevent crop damage, especially when used in isolation,drainage pot due to environmental risks, including to non-target birds, and a lack of efficacy in the long-term.
Other researchers agree, citing that avicide is costly, unstable, and unsustainable for a community since birds are a necessary component of agricultural ecosystems . In fact, when researchers modeled an idea that was proposed to lethally remove 2 million red winged blackbirds per year for five years with 3-chloro-4-methylalanine-treated rice, a cost:benefit analysis found that the results of culling these birds, even in combination with other non-lethal efforts, would be negligible . In a study conducted in the United Kingdom, nine bird deterrent techniques were implemented at six landfill sites. While distress calls, lethal falconry, and lethal and non-lethal ammunition worked best for initial deterrence, birds quickly became habituated to non-lethal measures. Despite this, public perception often prevents the use of lethal techniques . Drones and unmanned aerial vehicles are being used with increasing frequency in agriculture to conduct stand counts of crops, to identify areas with potential disease or insect presence, and to survey land prior to planting. They are also being used to protect crops from nuisance birds right now in Yuma, Arizona. Unlike other methods, birds cannot anticipate when and where a drone will appear, and since most have not been exposed to drones previously, birds see them as potential predators. Some drones must be operated in person by a pilot, while others are designed to launch, deploy to specific way points, and then land completely under autonomous control. In general, they employ visual, auditory, and predator mimicry to discourage habituation . Studies are currently underway to determine how effective drones will be when used in isolation, as well as in combination with other bird deterrent techniques. Falconry is an age-old hobby and sport dating back to 722 BC. As far back as 1893, people acknowledged that hawks and owls could be beneficial to agriculture . However, it wasn’t until the past 10 years that falconry has come into its own as a means of nuisance bird abatement in the United States.
Their presence was associated with a significant decrease in the number of nuisance birds present, and a 95% reduction in crop loss relative to vineyards without falcons . In the United Kingdom, falconry was used at landfills to hunt scavenging gulls and corvids. Falcons were effective against corvids and black headed gulls, but not against larger gulls . Since the trained birds were flown seven days per week for up to 12 weeks, it became impractical to maintain the falconers on site. In fact, that is one of several reasons that falconry is not used more regularly in agriculture; It requires licensed falconers that train for years, assistants, multiple species of trained birds that require specialized care, radio communication, and field vehicles. In addition, trained birds cannot be flown at night or during some weather conditions, and nuisance birds often return after falcons are removed. Due to these limitations, some view falconry as impractical for use in large scale agriculture . Others, however, cite positive outcomes associated with falconry in agriculture, including increasing predation pressure, decreasing the cost of biological controls applied to agricultural land , and minimizing the use of fields by nuisance birds during peak activity . A survey conducted of the public’s perception of bird control showed the methods that are most positively received are falconry and the installation of owl nest boxes, which were both typically described as more natural techniques. Conversely, the methods that were reported as most negative were live ammunition and methyl anthranilate, both viewed as less natural .Boundary layer atmospheric water vapor is a critical element of climate, an indicator of land surface hydrologic processes, and a potent greenhouse gas.
As such, analysis of vapor patterns at a fine spatial scale can inform climate and plant water use studies. Imaging spectrometers such as NASA’s Airborne Visible Infrared Imaging Spectrometer measure reflected radiance at fine spatial and spectral resolution, and in so doing provide measurements of column water vapor as well as a highly detailed reflected signal from the land surface below. These two spatially corresponding products allow us to uniquely observe surface processes and characteristics as they relate to the atmospheric patterns above them. As such, this research proposes to leverage water vapor and reflectance imagery to observe and assess spatial patterns of water vapor in the Central Valley of California to evaluate the assets and limitations of this dataset for evaluation of agricultural water use. Observation and evaluation of water vapor over agricultural fields in California’s Central Valley have substantial value for water resource management, irrigation assessments, and regional climate patterns. The Central Valley contains one of the world’s largest contiguous areas of high irrigation density with more than 3.6 million irrigated hectares of farmland that use over 80% of the state’s managed water supply. Worldwide,plant pot with drainage arid and semi-arid regions such as the Central Valley see upwards of ninety percent of precipitated water returned back to the atmosphere via evapotranspiration. In the Central Valley, where precipitation is low and managed water inputs are extreme, annual ET exceeds precipitation by about 60%. These extreme irrigation inputs, therefore, significantly modify the spatial and temporal distribution of hydrologic flows across the region by transforming liquid water resources into transpired atmospheric water vapor that can be transported and distributed as rainfall elsewhere. Further, as local atmospheric water vapor is intensified by ET, it is indicative of agricultural water inputs and crop functioning throughout the region. Imaging spectrometers such as AVIRIS quantify column water vapor using several water absorption features across the infrared portion of the electromagnetic spectrum as part of the reflectance retrieval process . Atmospheric water vapor absorption features occur at 0.94, 1.14, 1.38 and 1.88 μm, and the relative depth of these features can be used to derive atmospheric water content. Hyperspectral imagery is uniquely suited to estimate water vapor because its high spectral resolution captures water absorption features that multi-spectral sensors, such as Landsat, are designed to avoid. In addition, the fine spatial resolution of the retrievals enable observation of water vapor patterns that are obscured in spatially coarser imagery, such as MODIS or GPS meteorology, which estimates column water vapor based on delays in the signal between GPS satellites and a receiver. While water vapor imagery is produced as a byproduct of most visible to shortwave infrared reflectance retrievals, few analyses have been conducted with this rich dataset, leaving many questions as to the utility of these data unanswered. A notable exception is the work of Ogunjemiyo et al. who studied water vapor over poplar plantations in Washington State to assess the feasibility of using AVIRIS-retrieved column water vapor as a tool to study plant ET.
That study proposed a conceptual model of water vapor and its relationship to the surface , hypothesizing that plants with higher rates of transpiration will produce more water vapor, which will advect downwind and accumulate to a level detectable in the imagery, and that crops with higher water use rates will have steeper water vapor slopes, modified by wind speeds. Ogunjemiyo et al. found that the patterns and magnitude of retrieved water vapor in their study areas were consistent with wind direction and reasonable transpiration rates for poplars with unlimited access to water, concluding that AVIRIS water vapor is sensitive to ET under certain boundary layer conditions. Here, we build upon the work of Ogunjemiyo et al. to further evaluate if AVIRIS is sensitive to field scale ET by testing a series of specific hypotheses to investigate how AVIRIS estimates of water vapor vary with the surface properties and atmospheric conditions that might be expected to influence water vapor in a complex agricultural environment . Relating water vapor patterns to crop water use will be more challenging in the Central Valley of in June, prior to solar noon over heavily irrigated fields when rates of evapotranspiration should be high and water vapor still strongly coupled to the surface. A third data set was available from June, 2014, however, that data set was acquired much later in the day under less ideal conditions. Initial inspection of this image showed little or no relationships between water vapor and surface patterns. In this case water vapor may be dominated by upper atmospheric mechanisms and decoupled from the surface. Here we choose to utilize only images where coupling with the surface is detectible. We analyze water vapor at two scales–field, and scene. Our hypotheses are designed to explore whether consistent relationships between imagery, atmospheric conditions, and surface properties may be derived from hyperspectral imagery in a complex agricultural landscape and at which scales. We assume active ET is occurring given the date and time of the acquisition and heavy use of irrigation in the Central Valley. Prior work demonstrating an inverse relationship between green vegetation cover and Land Surface Temperature for these data sets further supports an assumption of active ET. If ET is occurring, evapotranspired moisture should advect downwind. At the regional or scene scale, water vapor concentration should increase downwind due to moisture advection. For example, if the wind is blowing from the North, fields in the southern part of the study area should show more water vapor than fields in the northern part of the study area . At the field scale, gradients should form that follow wind direction . The relationship between water vapor slope and wind should be quadratic with relatively high or low winds creating water vapor gradients less steep than winds that are of an “intermediate” magnitude. Only at intermediate winds, will a strong linear trend be observed. Light and/or inconsistent winds will not produce any gradients while higher winds will move water vapor at a faster rate, leading to shallower gradients . Field conditions should impact relationships between water vapor and wind. Thus we also investigate how these properties impact our evaluation of Hypothesis B and C. Field size, which will impact fetch, should impact the gradient . Fields below some critical threshold in size would not contribute enough water vapor to generate a detectable gradient whereas larger field should generate steeper gradients. The gradient should also vary with vegetation cover . Positive correlations would indicate that fields with more vegetation are adding more moisture to the air than less vegetated fields. Similarly, fields with higher water-demand crops should have more pronounced gradients .