large plastic pots – Nature Agriculture

September 13, 2023

The AVIRIS results are analyzed for portability and band importance

MASTER is a thermal sensor that captures 8 bands of emissivity between 4-12 μm, used to represent the proposed SBG thermal bands . The AVIRIS data was resampled to a resolution of 18 m while the MASTER data was resampled to a 36 m resolution. This paired dataset was flown over a portion of the Southern Central Valley seasonally while the state experienced severe drought effects. This unique dataset allows for study of remote sensing capabilities while also providing valuable information as to the response of crops in California to drought. The goal of this dissertation is to use data from the HyspIRI Airborne Campaign to evaluate how hyperspectral and thermal imagery can be used to improve upon current initiatives to account for and manage food and water resources in the face of a changing climate. This research will study patterns of agriculture and crop water use in the Central Valley as they shift throughout the course of an intense drought period from 2013-2015. These patterns will be investigated using imaging spectrometry from AVIRIS and thermal imaging from MASTER by mapping crops into relevant water use groups and then analyzing three indirect measures of crop water use from the imagery: choice of crop plantings, land surface temperatures, and water vapor patterns. Moreover,large plastic pots this dissertation will serve as a proof of concept for actively monitoring and measuring agriculture from space when the proposed SBG satellite is launched. In Chapter 2, I use three hyperspectral images acquired from AVIRIS over the course of the 2013-2015 drought in the Central Valley of California to both evaluate the performance of hyperspectral imagery for crop classification and to study farmer decision making with drought. A random forest classifier is run on the AVIRIS imagery to classify crops into groups of similar water use. Results are then compared to equivalent classifications using Landsat Operational Land Imager and Sentinel-2 imagery.

The results of this classification are then used to study the prevalence of crops as they change with increasing drought. Analysis highlights the economic and environmental drivers of planting decisions, and what this means for the future of California agriculture. In Chapter 3, I use spatially coincident AVIRIS and MASTER imagery from 2013, 2014 and 2015 to study the health of perennial crops over drought. First, I use a mixing model on AVIRIS imagery to decompose the scene into its fractional makeup of green vegetation , non-photosynthetic vegetation , and soil. Next, I model the expected temperature of each pixel as the fractional linear sum of its thermal components. I then calculate a thermal residual for each pixel as the difference between its measured temperature from MASTER and the modeled temperature. This method strips away thermal variability due to air temperature, time of day, fractional cover, structure, and moisture to allow for direct thermal comparisons between pixels and crop species. Thermal variability within agricultural fields is quantified and crop health is assessed. In Chapter 4, I evaluate spatiotemporal patterns of water vapor as they occur over agricultural fields in the Central Valley to evaluate the potential of this imagery to assist with agricultural applications. I use pixel-level column water vapor estimates derived from AVIRIS radiance imagery, surface characteristics obtained from AVIRIS reflectance imagery, and interpolated maps of wind to investigate relationships between the atmosphere and the surface. I propose and test a set of hypotheses for how water vapor will interact with the landscape in a diverse and complex agricultural scene at the pixel, field and scene scales. Results and analysis further knowledge of opportunities and limitations for using water vapor imagery to better understand crop water use. Although California faces substantial variability in inter annual precipitation and is accustomed to multi-year dry periods, the 2012 to 2016 drought was exceptional in its severity, and may be emblematic of greater shifts in California’s climate associated with anthropogenic warming .

Climate projections for California indicate that mean and extreme temperatures are likely to increase over the next century, which will increase the risk of experiencing future droughts of the severity of the 2012–2016 event . Future droughts will undoubtedly continue to put strain on water supplies, but the magnitude and extent to which these events impact water resources will depend not only on the characteristics of the drought, but also on the adaptive responses of people . In California, where the agriculture sector uses roughly 80% of the state’s managed water , agriculture simultaneously shows high vulnerability to a warming climate while also offering the greatest opportunity to mitigate the intensity of future drought impacts through adaptation strategies . Consequently, it is critical to study how we can monitor crop management response in real-time in order to assist with policy making during drought and analyze the ways in which the long-term sustainability of food and water security can be improved. This research used annual hyperspectral remote sensing imagery to assess the accuracy at which imaging spectroscopy can be used to map crops into categories of similar water demand and analyze changes in cropping patterns in a portion of the Central Valley. The study takes advantage of data collected over three years of a multi-year drought as a unique opportunity to measure agricultural response and adaptation in times of drought. Climate change is likely to significantly affect regional agricultural patterns and crop yields , in part due to management decisions such as fallowing fields or switching crop varieties or species . Therefore, monitoring how crop patterns change during droughts is a direct measure of adaptive response. Cropping decisions impact society in multiple ways by altering regional water requirements , food yields , economic production , and pesticide exposure . Consequently, accurate and timely crop maps are necessary to support long-term adaptation planning for a broad range of sectors, and are of use to farmers, managers, policymakers, and scientists.

Remote sensing has the potential to map crops and monitor changes in crop area more efficiently and frequently than time and labor-intensive on-the-ground crop accounting. Hyperspectral imagery, which samples hundreds of spectrally contiguous wavelengths, has the potential to identify crops at a single time point with a single sensor at higher accuracies than a broadband sensor . This ability is critical to enabling managers and scientists to stay abreast of rapidly changing planting choices and assess current risks, which is a need that current mapping initiatives with remote sensing are unable to fulfill. Most remote sensing mapping initiatives in the United States rely on satellites such as Landsat and the Moderate Resolution Imaging Spectrometer because of their large spatial and temporal coverage, ease of accessibility, and free availability . The National Agricultural Statistics Service ’s Cropland Data Layer is the most comprehensive current agricultural mapping initiative for the United States with an easily accessible crop map published at yearly intervals at a 30-m resolution . It relies on data from Deimos-1,raspberry container the United Kingdom’s Disaster Monitoring Constellation 2 , and Landsat 8 Operational Land Imager and produced an overall accuracy of 81.1% in California in 2016, with accuracies of crop groups ranging from a low of 32.8% for berries to 77.6% for forage crops. Although widely used and highly useful, the CDL has limitations concerning reproducibility and timeliness. First, by using three sensors, not all of which produce publicly available data, reproducing this map or using this methodology on a different study area or at a different time would not be possible. Furthermore, with maps published at the end of each year, the CDL does not offer near real-time or mid-growing season assessments of crop area. Another method of crop mapping uses multi-temporal MODIS imagery to classify crops using annual crop phenology for identification . These studies illustrate the ability of time series datasets to produce detailed and accurate crop classification maps at the end of an agricultural year in a single study area, but this methodology also faces challenges that hinder its practical and scientific usefulness in California. First, the spatial resolution of MODIS is not fine enough to individually classify many fields. For example, the average size of a field in the area of this study is approximately 0.2 km2 . Therefore, even at its finest resolution of 250 m, most MODIS pixels will result in mixtures of different fields or crop types, and are therefore best suited for croplands at larger scales . Second, multitemporal crop mapping is limited in its spatial scope due to a spatial variation in phenology that would decrease the accuracy if it was applied over a large spatial area . Third, the co-registration of multiples images and the need for cloud-free images create challenges for time-series analysis that single-data hyperspectral analyses do not face .

Finally, the need for multiple images throughout time obviates the ability to conduct real-time crop assessments. Hyperspectral imagery can act as a complement to these current crop-mapping initiatives, as it has the potential to identify crops at a single time point with greater accuracy than broadband sensors, and therefore can provide mid-season assessments of crop area without a yearly time-series . Discriminating crop types is challenging due to differing biophysical traits, development stages, variable management practices, regional weather and topography, and the timing of plantings . Despite these complications, various studies have successfully shown the ability to use hyperspectral imagery to classify crops and cultivars . By discriminating crop types with a single image from one time point, hyperspectral imagery can serve as a time-critical agricultural management tool, providing scientists, farm managers, and policymakers with improved information regarding the agricultural landscape and on-the-ground food and water needs. This study uses airborne hyperspectral imagery over a portion of the Central Valley to assess the accuracy of imaging spectroscopy for agricultural classifications and conducts a case study to display the utility of these classifications for analyzing changes in farming decisions. The results of this study, while limited in their spatial scope due to the use of airborne imagery, are salient in light of recently available Sentinel-2 data and the proposed HyspIRI mission, which would provide repeat, global hyperspectral imagery. In order to separate soil or fallow pixels from those of agricultural plant matter, a spectral mixture analysis was run on each of the three images to obtain fractional green vegetation cover. Multiple End member Spectra Mixture Analysis uses a linear mixture model to unmix pixels into fraction images while allowing the number and types of end members to vary on a per-pixel basis, thus better accounting for end member variability. Pixels were modeled as a mixture of green vegetation , soil, nonphotosynthetic vegetation , and shade. Image end members were chosen from each of the three images from 2013, 2014, and 2015 by selecting pixels with high overall reflectance from each of the three end member categories that were well-distributed spatially throughout the image in order to capture the variability from north to south along the flight line. A combined library of all of the chosen end members, consisting of eight NPV, 10 Soil, and 21 GV endmembers, was used for analysis in order to obtain consistent results throughout the years. MESMA was partially constrained by requiring shade fractions to vary between 0–0.8, and setting a maximum allowable root mean squared error of 0.025. The spectral mixture result was then shade normalized by dividing each non-shade component, GV, NPV, and soil by the sum total of all of the non-shade components in that pixel to obtain physically realistic fraction estimates . Only those pixels that contained 50% or more shade-normalized GV were chosen for training and validation, as this was decided as the threshold for classifying a pixel as a crop.Due to the high diversity of crop species in the Central Valley, we focused on a smaller set of crop classes that would be of the most practical use to stakeholders such as water managers, farmers, and scientists. Crops were classified into categories defined and used by the California Department of Water Resources to estimate water use . The crops within each category have similar rates of development, rooting depths, and soil characteristics, and are therefore presumed to have similar water requirements. Categories were included in the classification if they were prominent in the area, defined as ≥20 fields of that category, each of which contained ≥50% green vegetation, in the validation layers .

July 7, 2023

The subsistence technology type that has probably sparked the most controversy is hunter-gatherers

Imagine that an STI which leads to infertility occurs in the female population with probability k. Then, an uninfected man and his wives can avoid infertility if and only if none of his wives have the infection. This occurs with probability: n . If we assume an empirically plausible rate for this STI, like 0.07 , then a monogamous man would be paired with fertile woman 0.93 of the time, and a man in 2-polygyny would be paired with fertile women 0.86 of the time. An ultra-wealthy man in 10-polygyny, however, would be more likely than not to have infertile wives—a prospect that could lead to diminishing fitness returns to, and hence, demand for, additional wives. A similar argument holds even if marriage is considered sequentially—as in serial monogamy—though the effect would be smaller. Finally, impediments to cooperation or even outright conflict among co-wives can be greater as the number of wives increases. Interference competition among co-wives could impose significant fitness costs in settings where effective child rearing benefits from cooperation. It could well be that incumbent wives resist incorporation of additional wives to the wealth sharing pool, perhaps with greater effectiveness as their numbers grow. Empirically exploring these and other possible explanations for the unexpectedly substantial diminishing fitness returns to additional wives that are not explainable by the division of rival wealth among wives would be a valuable next step, but one that would take us beyond the formal modelling, database and comparative statistical methods that we have presented.Recent anthropological research on the causes of war falls roughly into two schools : one which concludes that sociopolitical factors are the primary determinants of war ,large plastic pots and another which argues that environmental and technological factors are primary . Both “schools” have developed testable hypotheses about the conditions making a society more likely to go to war .

In this study we retest two hypotheses initially tested by Nolan : first, more productive subsistence technology leads to more war; and second, higher population density leads to more war. Both hypotheses come from ecological-evolutionary theory, which asserts that subsistence technology is the single most important factor affecting how societies are organized and how they interact with one another . Ecological evolutionary theory predicts warfare is more frequent in societies with more productive subsistence technology for three main reasons. First, armies need to eat; a society with unproductive subsistence technology would not have the food stock to sustain soldiers in a prolonged conflict. Second, food stores make for attractive targets; hence, societies with more productive subsistence technology are more likely to be attacked. And third, fixed investments in fields and structures give a society with more productive subsistence technology a strong incentive to defend its territory from attackers.Some have claimed that, compared to other societies, hunter-gatherers are relatively peaceful while others disagree . Ecological-evolutionary theorists tend towards the ‘relatively peaceful’ side of the debate. Nolan , for example, argues that hunter-gatherers usually lack the resources to sustain them during long periods of warfare. Likewise, they have little to be plundered and find it feasible to walk away from a confrontation and move to a new area. Ecological-evolutionary theorists expect the frequency of warfare to be higher among horticulturalists than among hunter-gatherers. Horticulturalists own more resources than hunting-gathering societies, and are more attractive targets. While hunter gatherers can with relative ease walk away from an attack and move to a new location , horticulturalists are more likely to defend their lands and structures . And since metal tools make horticulturalists more productive, and metal weapons make warriors more deadly, horticulturalists are expected to have even higher rates of warfare if they have learned to use metal .

Slavery has a similar effect in increasing the frequency of war: armies can capture slaves, which can then be used to produce food, which can be used to feed armies, which can then capture more slaves – thus creating a positive feedback warfare-slavery system . Ecological-evolutionary theorists expect plow agriculturalists to have even higher frequencies of external warfare than horticulturalists , since they have more productive subsistence technology, which compounds the warfare incentives affecting horticulturalists. Agrarians are even more reliant on their land than horticulturalists and therefore even less likely to walk away from a confrontation; they can produce even larger and more diverse food stores – further incentivizing plunderers; their larger food stores can feed larger armies; and slavery becomes even more profitable as more productive technology increases the returns to labor. For these reasons, most previous studies of the ecological-evolutionary theory of warfare have supported the idea that as subsistence technology becomes more productive, the frequency of warfare increases. The hypothesis of a positive correlation between population density and warfare is based upon the notion that, within a given subsistence technology type, increases in population density will increase the pressure on a society’s resources, thus motivating a society to plunder the resources and conqueror the arable lands of its neighbors. Hence, some studies maintain that the frequency of warfare will increase as population density increases. Other studies argue for a cubic relationship – claiming that warfare increases, decreases, and finally increases again across categories of increasing population density. In his study of precontact Polynesia, Younger finds a negative relationship between population density and violence. Thus, there is by no means a consensus within the literature about this relationship. In a 2003 paper published in Sociological Theory, Patrick D. Nolan sets out to test whether certain modes of subsistence are “structurally conducive” to warfare . Using variables from the Standard Cross-Cultural Sample , Nolan produces a number of contingency tables, examining whether the frequency of warfare varies across four subsistence modes and by population density.

He finds that societies with advanced horticulture or agrarian subsistence engage in warfare much more frequently than those with foraging or simple horticulture. He also finds that high population densities are associated with more war in societies with foraging or agrarian subsistence, but not in those with horticulture. While we find much of value in Nolan’s theoretical discussion, there are serious problems with his empirical analysis. First, in adopting a contingency table approach, Nolan chooses a method that requires reducing the variation found in the original SCCS variables, in order to have relatively few cells in each table. Thus he takes a measure of frequency of warfare , available in the SCCS with 18 discrete values, and turns it into a dummy variable, with only two discrete values. Likewise, he employs only four general subsistence categories, a feat he manages by lumping hunting with gathering, and discarding 54 of the 186 SCCS societies that subsist as mounted hunters, fishers, pastoralists, or rely equally on two or more subsistence modes . Particularly problematic is the removal of relatively warlike pastoralists and mounted hunters from his sample. Table 1 compares Nolan’s categories with the categories given in SCCS v858. The four subsistence taxonomies presented in the SCCS provide much richer detail: variable v246 has seven categories; variable v833 has eight; variable v858 has 11; and combining variables v833and v834gives 28 categories. But even more variation can be found by using variables v814–v819, which provide actual ordinal measures of the percentage dependence on each category of agriculture, domestic animals, fishing, hunting, gathering, and trade. Variation is the great friend of any statistical analysis, and the SCCS contains variables that provide abundant variation on subsistence technology. That Nolan reformulates his variables to reduce variation is due simply to his choice of technique— contingency tables work well only when there are relatively few cells in the table. Contingency tables also limit the analysis to pairwise relationships between variables, and at best can be modified to fit a three-way relationship. When there are confounding variables, as there always are,plastic pots for plants the results from a pairwise analysis will be biased. It is on this count that multivariate models provide their greatest advantage: one can control for the effects of other variables and thus produce unbiased estimates. And because multivariate methods consider a large set of variables, one can gain a sense of how important a particular relationship is in the grander scheme of things. Galton’s problem—the confounding effect of cultural transmission—is a major methodological issue in empirical studies using cross-cultural survey data. There are no effective ways to control for Galton’s problem within a contingency table framework and Nolan wisely does not try. Dow has developed an effective way of modeling Galton’s problem within a multivariate model framework, which provides yet another advantage to multivariate methods over contingency tables. Finally, there is the problem of missing data. As shown by Dow and Eff , dropping observations for which data values are missing can lead to bias, even within a multivariate modeling context. Nolan dropped observations that were not even missing—removing 54 societies that did not fit neatly into his subsistence taxonomy categories .

The appropriate way to handle missing data is the technique of multiple imputation . Nolan’s empirical work does not do justice to his theoretical discussion. In what follows, we investigate the role of subsistence technology and population density in causing war, using contemporary best-practice statistical methods. Wealth accumulation provides the means for elites to enhance their status, and this status-enhancement strategy is both an alternative to the strategy of war and can be disrupted by war, making war less attractive to elites. SCCS variable v17 was included as a scale for the use of money and credit. Small communities are less likely to have powerful elites , and are therefore less likely to be driven by elites to war. In fact, Younger’s study of Polynesian war found that smaller communities were more peaceful. We therefore include a scale for community size . Elites will also be less able to drive a society to external war when their power is less secure; variable v575 was included as an “unstable political power index.” If local political structures are complex, elite power may be fragmented, and it may be difficult for all relevant actors to agree upon a course of action, which would make war less frequent. For this reason, v236, a measure of the number of levels in the jurisdictional hierarchy of the local community, is included. Similarly, we introduce variable v757, which measures whether political authority is simultaneously religious authority, reasoning that more heterogeneous elites are less likely to find common cause in external war. Military historians speak of the three “C’s” in the analysis of war: causes, conduct, and consequences . Our analysis focuses on the causes of war, but features of a society facilitating its conduct will also serve to make a society more likely to go to war, and in this way the feature can be seen as a cause. For example, when females contribute a great deal to subsistence, the opportunity cost of sending a man to war is lower, and the choice of war therefore is less costly. For this reason, we include variable v826, “average female contribution to subsistence.” Certain kinds of technology facilitate the conduct of war. For example, the development of metal weapons makes warfare more practicable and increases its incidence . Thus, we create the variable metal as the composite for four SCCS variables concerning the use of metal tools and weapons.4 Similarly, Nolan notes that limited communications and transportation technology “were the only real constraints” horticulturalists faced in using war to control more land and subject peoples. We employ here v149, which measures the degree to which a society utilizes writing and records. A prior study by Brown and Eff found the frequency of external warfare and the presence of moralizing gods to be inversely related; suggesting that morality reinforced by supernatural forces may serve to constrain warlike behavior. We investigate this by including SCCS variable v238 in our model. The bottom panel in Figure 3 plots the total effect on the ordinate and pdens on the abscissa. Since pdens is standardized, the total effect will be positive for societies with below-mean population density, and negative for societies with above-mean population density. The resulting plot is nearly linear—fitting a negatively sloped straight line with an R 2 of 0.796. Our model’s negative relationship between population density and the frequency of war is consistent with Younger’s findings but not those of Nolan or Keeley .