Category Archives: Agriculture

A major limitation to this study is that the reference dataset was not entirely accurate

The plotted detections in Figure 10 and Figure 12 are counted in the overall AP and AR metrics as successful detections because the IoU of the detection and reference label is greater than 50%. Yet, many pixels that belong to individual center pivot fields are not included in the detection. Across similar scenes, Mask R-CNN does not appear to have the consistent boundary eccentricity bias that FCIS has . Because Mask R-CNN showed better boundary accuracy along with comparably high performance metrics relative to the FCIS model, further comparisons and visual examples comparing accuracy across different field size ranges and with different training dataset sizes used Mask R-CNN instead of FCIS. Many scenes are more complex than the arid landscape with fully cultivated center pivots shaped like full circles in Figure 10. Figure 11 is a representative example of a scene with more complex fields. These include non center pivot fields, center pivots containing two halves, quarters, or other fractional portions in different stages of development , and partial pivots, which is semicircular not because the rest of the circle is in a different stage of development or cultivation, but because of another landscape feature that restricts the field’s boundary . In this scene, at least 25% of the detections in this scene are below the 90% confidence threshold, and many atypical pivots are missed based on this threshold . Figure 12 is a simpler scene that like Figure 12, has a high density of center pivot fields. In this case the detections more closely match the reference labels and detection confidence scores are higher, either because of the FCIS model’s tendency to produce higher confidence scores or because the scene has less variation in center pivot field type. Figure 13 highlights another common issue when testing both models,vertical farming technology that reference labels are truncated by the tiling grid used to make 128 by 128 sized samples from Landsat 5 scenes.

These tend to be missed detections based on the 90% confidence threshold since they are not mapped with a high confidence score. There are cases where no high confidence detections above 90% are produced, such as in Figure 14. In this scene, No Data values in the Landsat 5 imagery, partial pivots near non-center pivot fields, and mixed pivots with indistinct boundaries all result in a scene that is not mapped with high confidence. However, in cases where there is high contrast between center pivot fields and their surrounding environment, they are mapped nearly perfectly by the Mask R-CNN model with high confidence scores . Figures 16 through 18 were selected to illustrate the impact that size range has on detection accuracy, since center pivots can come in various semicircular shapes and sizes. Figure 16 shows that in a scene with no reference labels, no high confidence detections were produced for any size category. The highest confidence score associated with an erroneous detection in this case was at most ~0.66, which is relatively low for both models. Figure 17 shows a case where a large center pivot is mapped accurately, whereas smaller and medium center pivots in the scene are not. This example shows the scale invariance of the Mask RCNN model in that it can accurately map the large center pivot because it looks similar to a medium sized center pivot, only larger. On the other hand, smaller center pivots, partial pivots, and mixed pivots are detected with lower confidences or not detected. Figure 18 highlights a case where a large center pivot is not mapped with a high confidence score above 90%. Unlike Figure 17 where the large center pivot is uniform in appearance, the large center pivot in Figure 18 has a mixture of three different land cover types. This indicates that the inaccuracies from large center pivot detection may come from large center pivots that were partially cultivated or divided into multiple portions with different crop types or cultivation stages. Many false negatives in this scene and others are the result of partial pivots, mixed pivots or pivots that had not been annotated yet in the 2005 dataset.

Small fields are more difficult to detect than large ones and so as expected, removing 50% of the training data available to train the Mask R-CNN model caused large drops in performance. Having more training data available to improve features that are attuned to detect small fields is particularly important with regard to overall model performance. The metric results for small fields is likely biased toward a worse result because many full pivots overlapped a sample image boundary, leading to small, partial areas of pivot irrigation at scene edges being over represented after Landsat scenes were tiled into 128×128 image chips. Since these fields have a less distinctive shape, some full pivots at scene edges were missed. However, since small fields make up a minority of the total population of fields and the medium and large category were more accurate by 20 or more percentage points for both AR and AP, this shows that both the FCIS and Mask R-CNN models can map a substantial majority of pivots with greater than 50% intersection over union. Zhang et al. tested their model on the same geographic locations at a different year and used samples produced from two Landsat scenes to train their model over a 21,000 km^2 area versus the Nebraska dataset which spans 200,520 km^2. These results extend upon the work by Zhang et al. , as the test set is geographically independent from both the training and validation set and 32 Landsat 5 scenes across a large geographic area were used to train and test the model. Furthermore, Zhang et al. ’s approach produces bounding boxes for each field, while Mask R-CNN produces instance segmentations that can be used to count fields and identify pixels belonging to individual fields.While comparing metrics is useful, they don’t indicate how performance varies across different landscapes or how well the quality of the boundary matches reference given that a detection is determined to be correct by having an IoU over 50%. While the FCIS model slightly outperformed the Mask R-CNN model in terms of the medium size category average precision, it also exhibited poorer boundary fidelity. Figure 10 demonstrates arbitrarily boxy boundaries that appear to be truncated by the position sensitive score map voting process that is the final stage of the FCIS model.

The Mask R-CNN model’s high confidence detections that had a confidence score of 0.9 or higher matched the reference boundaries much more closely, showing that this model can be usefully applied to delineate center pivot agriculture in a complex, humid landscape. While the FCIS model could also be employed with post processing to assign a perfect circle to the center of a FCIS detection ,vertical tower planter this would lead to further errors that would overestimate the size and wrongly estimate the shape of partial center pivots. The results from Mask R-CNN on medium sized fields are encouraging because it indicates that the model could potentially generalize well to semi-arid and arid regions outside of Nebraska . In addition, the results from Figures 10, 12, and 15 indicate that where many uniform center pivots are densely colocated and not many non center pivots are present, a higher quantity will be mapped correctly. This is encouraging, since in many parts of the world, center pivots are densely colocated, or are cultivated in semiarid or arid environments, where contrast is high. Therefore, the model can be expected to generalize well outside of Nebraska, though it remains future work to test the model in other regions. False negatives are present for many scenes that are heavily cultivated, and in many cases it is ambiguous whether the absence of a high confidence detection is due to the absence of a center pivot or because of Landsat’s inability to resolve fuzzier boundaries between a field and its surrounding environment . A time series based approach similar to Deines et al. could improve detections so that only pivots that exhibited a pattern of increased greenness would be detected in a given year. However, this requires multiple images within a growing season from a Landsat sensor, which are not always available due to clouds, and also precludes the use of traditional CNN methods and pretrained networks which ease the computational burden of training and detection. Furthermore, this approach is difficult to incorporate into a CNN based method for segmentation, as it precludes the use of pretraining.

Another alternative is to develop higher quality annotated datasets which make meaningful semantic distinctions between agriculture in different stages of cultivation. For example, in Figure 11, brown center pivots that are not detected could instead be labeled as “fallow” or “uncultivated”, and this information could be used to refine the training samples used to train a model to segment pivots within specific cultivation stages. With 4 hours of training on 8 GPUs, the original implementation of Mask R-CNN achieved 37.1 AP using a ResNet-101-FPN backbone on the COCO dataset, a large improvement over the best FCIS model tested, which achieved 33.6 AP . This amounts to a difference of 3.5 AP percentage points, with Mask R-CNN performing better on the COCO dataset. On the Nebraska dataset, for the medium size category, the difference in AP was 3.2 AP percentage points, with the FCIS model outperforming Mask R-CNN. However Mask R-CNN outperformed FCIS in terms of AR by 5.1%. These results indicate that COCO detection baselines are not necessarily reflective of overall metric performance, given that FCIS outperformed Mask R-CNN in the more numerous size category. The improvements on the COCO baseline do reflect the improved boundary accuracy of MAsk R-CNN relative to the FCIS model. The AR and AP results on the Nebraska center pivot dataset are higher relative to Reike , which is to be expected since center pivots are a simpler detection target than fields in the Denmark dataset, which come in more various shapes and sizes. What’s especially notable is that even though Rieke trained the FCIS model on approximately 11 times the training data compared to the training data used in this study, the AP results and AR results on the Nebraska dataset were about 10 to 20 points higher for each of the size categories. The difference for the AP for the small category was 0.42-0.28 = 0.14 , the difference for the medium category was 0.732 – 0.473 = 0.259, and the difference for the large category was 0.734 – 0.51 = 0.224 . Rieke used 159042 samples compared to 13625 samples used in this study. These samples were equivalently sized to this study, at 128×128 pixels. Even though the size categories used in this study and Rieke are not exactly the same, the fact that each of the categories saw substantially better performance for the FCIS model on the Nebraska dataset indicates that the relative simplicity of the center pivot detection target played a substantial role in the jump in performance. Given that Rieke used 11 more training samples, this indicates that the simplicity of the detection target played an even larger role in the performance difference. This is an important lesson for remote sensing researchers looking to use CNN techniques to map fields or other land cover objects, the feasibility of mapping the detection target can be even more important than using an order of magnitude more training data to improve a model’s ability to generalize. These results are comparable to other results achieved for other detection targets. Wen et al. applied a slightly modified version of Mask R-CNN which can produce rotated bounding boxes to segment building footprints in Fujian Province, China from Google Earth imagery. The model was trained on manually labeled annotations across a range of scenes containing buildings with different shapes, materials, and arrangements. Though an independent test set was not used that was separate from the validation set , the model was tested on half of the imagery collected, while the other half was used to train the model, providing a large amount of samples to test the model. The total dataset used to split between training and testing/validation amounted to 2000 500×500 pixel images containing 84,366 buildings .

Citizens of Member States do not have standing to bring WTO-based complaints

As of July 2012, the GENERA database listed 583 scientific studies on the safety of GMO crops and their food ingredients. In addition, the experiential evidence of billions of meals consumed by persons around the world since commercial release of genetically-engineered crops in 1996 supports the safety of genetically-modified foods. Since 1996, there has not been one verified health complaint to humans, animals or plants from genetically-engineered crops, raw foods, or processed foods. Despite some published attempts to deny this overwhelming scientific evidence in support of genetically engineered foods, the scientific consensus is clear —genetically-engineered crops, foods, and processed ingredients do not present health and safety concerns for humans, animals, or plants. SPS Agreement Article 3 sets forth provisions that could save Proposition 37. Paragraph 3.2 affirms a SPS measure that conforms to international standards relating to health and safety. However, Paragraph 3.2 does not protect Proposition 37 because there are no international standards that categorize genetically-engineered raw or processed foods as unsafe or unhealthy. Comparing Proposition 37 to the legal standards in the SPS Agreement shows that Proposition 37 almost assuredly is not compliant with the SPS Agreement. Indeed, the WTO SPS claim against Proposition 37 is so strong that its proponents are probably not going to defend it as meeting the legal standards of the SPS Agreement. Despite its textual language and the electoral advertising emphasizing food safety and health concerns,vertical farming aeroponics proponents will argue that Proposition 37 cannot properly be characterized as a labeling requirement “directly related to food safety.” Proponents of Proposition 37 will seek to have it classified as a technical barrier to trade in order to avoid the SPS Agreement and its scientific evidence standards.

The TBT Agreement applies to technical regulations, including “marking or labelling requirements as they apply to a product, process or production method.” As Proposition 37 imposes mandatory labels, Proposition 37 is a technical regulation under the TBT definitions. TBT Article 2 sets forth several provisions against which to measure technical regulations for compliance with the TBT Agreement. It states, “Members shall ensure that technical regulations are not prepared, adopted or applied with a view to or with the effect of creating unnecessary obstacles to international trade. For this purpose, technical regulations shall not be more trade-restrictive than necessary to fulfill a legitimate objective, taking account of the risks non-fulfillment would create. Such legitimate objectives are, inter alia, … the prevention of deceptive practices; protection of human health or safety, animal or plant life or health, or the environment. …” Article 2.2 expressly lists three legitimate objectives: national security requirements; protection of human health or safety, animal or plant life or health, or the environment; and prevention of deceptive practices. As for health and safety, Proposition 37 does not provide a label giving consumers information about how to use a product safely or a safe consumption level or any other health and safety data—unless the warning-style label against genetically-modified food itself is considered a valid warning. But, as discussed with regard to the SPS Agreement, there is no scientific evidence available to indicate that genetically modified foods have negative health or safety implications for humans, animals, or the environment. Proposition 37 does not assert a legitimate health and safety objective under TBT Article 2.2.Proposition 37 can be defended as upholding the third legitimate objective—prevention of deceptive practices. Indeed, the Proposition is titled the “California Right to Know Genetically Engineered Food Act,” indicating that labels will assist California consumers in knowing what they are purchasing and avoiding purchases that they desire to avoid. Those who would challenge Proposition 37 for noncompliance with the TBT Article 2.2 will argue that Proposition 37 is not a protection against deceptive practices. Opponents can point to the structure of the proposed Act and its exemptions to provide evidence that Proposition 37 will actually confuse consumers more than inform them accurately.

Proposition 37 exempts foods that lawfully have the USDA Organic label. Under the USDA National Organic Program , organic foods can contain traces of unintentional genetically-modified crops or ingredients without losing the organic label. Simultaneously, those California consumers still will be eating unlabeled food products containing genetically modified crops or ingredients at trace levels, except those products will carry the label “USDA Organic.” In other words, opponents of Proposition 37 will argue that Proposition 37 is itself the deceptive labeling practice and, thus, fails to promote a legitimate objective under TBT Article 2.2. Proponents of Proposition 37 will respond by citing to the recent WTO Dispute Resolution Appellate Body relating to the challenge of Canada and Mexico against the United States country-of-origin label for meat. The WTO Panel ruled against COOL on the grounds of a violation of TBT Article 2.2 because the COOL law would confuse consumers. But the WTO Appellate Body reversed this Panel ruling and determined that COOL did provide information as a legitimate objective under Article 2.2.Aside from “legitimate objectives,” TBT Article 2.2 also requires that technical regulations not be “unnecessary obstacles to international trade” and “not more trade-restrictive than necessary.” Opponents of Proposition 37 will argue that it violates these TBT obligations primarily because consumers already have labels that provide the same level of consumer protection from deception. Opponents will point to the existence of the Non-GMO label and the USDA-Organic label that allow consumers to choose foods which will have minimal levels of genetically-engineered content. These Non-GMO and USDA-Organic labels are voluntary labels that do not impose legal and commercial burdens upon other food products in international trade. TBT Article 2.1 also provides a standard against which to measure Proposition 37 by stating, “Members shall ensure in respect of technical requirements, products imported from the territory of any Member shall be accorded treatment no less favorable than that accorded like products of national origin and to like products originating in any other country.”TBT Article 2.1 requires Members to treat “like products” alike and to refrain from favoring either domestic or other international “like products” as against the products of the Member bringing the Article 2.1 complaint.

Obviously, proponents of Proposition 37 consider genetically-engineered agricultural products as fundamentally different than organic and conventional agricultural products. Proponents will argue that Proposition 37 deals with genetically-engineered agricultural products that constitute a class of products of their own.Opponents of Proposition 37 will respond with two arguments. Opponents can argue that regulatory agencies around the world have considered genetically-engineered raw agricultural products to be substantially equivalent in every regard to conventional and organic agricultural products. Opponents will argue that the substantive qualities of genetically-engineered agricultural products are “like products” and that the process producing the “like products” does not create a separate product classification. Opponents will argue “product” over “process” as the appropriate TBT Article 2.1 interpretation. Opponents of Proposition 37 will also present a second argument. More precisely, opponents of Proposition 37 will highlight the fact that Proposition 37 imposes labels, testing, and papertrail tracing on vegetable oils even though the oil has no DNA remnants of the crop from which the oil came. Soybean oil is soybean oil regardless of what variety of soybean the food processor crushed to produce the oil. With regard to the TBT Article 2.1 arguments,vertical indoor hydroponic system opponents of Proposition 37 may gain support from the Canada and Mexico WTO complaints against the U.S. COOL law. Both the WTO Panel and the WTO Appellate Body determined that Canadian and Mexican meat was a “like product” to United States meat. As a “like product,” the WTO reports ruled that the U.S. COOL law violated TBT Article 2.1 by imposing discriminatory costs and burdens on meat imported into the United States.TBT Articles 2.4 and 2.5 provide a safe harbor for technical regulations if those technical regulations adopt international standards. However, the Codex Alimentarius Commission, the international standards body for food labels, has not created an international standard which proponents of Proposition 37 can claim as its origin and safe harbor.SPS Agreement Article 11 and TBT Agreement Article 14 are both titled “Consultation and Dispute Settlement.” Thereby the SPS Agreement and the TBT Agreement make explicit that Member States to these agreements can complain using the WTO Dispute Settlement Understanding Agreement. For example, Argentina or Brazil or Canada—all likely to be affected by Proposition 37 for the export of soybeans and canola, especially for cooking oils—have the treaty right to file a complaint within the WTO dispute resolution system. Bringing a WTO complaint is fraught with difficulties. Members must think politically and diplomatically about whether it is worthwhile to bring a complaint—even a clearly valid complaint. Members must be willing to expend significant resources in preparing, filing, and arguing WTO complaints. Finally, even if a Member prevails in the Panel or Appellate Body reports, Members recognizes that its WTO remedies are indirect and possibly not fully satisfactory. Although the United States is a Member of the WTO Agreements, the United States, in contrast to Argentina, Brazil and Canada, is not an exporting Member to California.

Consequently, the United States cannot file a WTO complaint invoking the DUS Agreement against California. But by being a Member of the WTO Agreements, the United States has ratified these treaties as part of the law of the United States, transforming these treaties into the supreme law of the land under the U.S. constitution. Moreover, under the WTO Agreements, the United States has the duty to ensure that local governments comply with the WTO Agreements. Therefore, the United States has the legal authority to challenge Proposition 37 in order to protect its supreme law of the land and to avoid violating its WTO obligations.Opponents of Proposition 37 are likely to challenge Proposition 37 immediately if California voters adopt it in November 2012. As indicated in the introduction, these opponents are likely to bring challenges on three different grounds under the U.S. Constitution. These opponents have non-frivolous grounds upon which to pursue these U.S. constitutional challenges. Whether these opponents can add a claim challenging Proposition 37 based on alleged violations of the SPS Agreement or the TBT Agreement is much less clear. TBT Agreement Article 14.4 highlights that the opponents will have difficulty in bringing a WTO-based challenge. TBT Article 14.4 makes clear is that Member States have the legal status to bring WTO-based complaints.Proponents of Proposition 37 will challenge the standing of those opponents who seek to challenge Proposition 37. Proponents will seek to have this WTO-based claim dismissed because the opponents do not have a right to make a legal claim based on the WTO. Proponents will argue that standing to bring a WTO-based claim resides solely in exporting Member States or the United States. By contrast, opponents bringing the immediate challenge containing a WTO-based claim will argue that they are not invoking the WTO Agreements directly. Opponents will argue that they are challenging Proposition 37 to enforce the supreme law of the United States. By invoking the supreme law of the United States, opponents will hope to blunt the standing issue and to avoid dismissal of the WTO-based claim.Assuming that the United States does not file a lawsuit against California and that other opponents are blocked, by the doctrine of standing, from raising WTO-based challenges, Proposition 37, if adopted in November 2012, would become California law. Thus, the first lawsuits related to Proposition 37 would come through either administrative action or a consumer lawsuit against food companies and grocery stores alleging failure to label or misbranding. When facing administrative actions or consumer lawsuits, food companies and grocery stores will want to respond with all possible legal challenges to Proposition 37. Food companies and grocery stores will want to raise the issues of whether Proposition 37 complies with the SPS Agreement and the TBT Agreement as defenses to being found liable for administrative penalties or consumer damages. The agency or consumer bringing the lawsuit against the food company or grocery store will argue that the food company or grocery store does not have standing to raise the WTO-based challenges. The plaintiff likely has to concede that the defendant faces an actual injury. However, the plaintiff will contest vigorously that the defendant is not within the zone of interests that the WTO Agreements mean to protect. In other words, the plaintiff will argue that the WTO Agreements only mean to protect sovereign interests and not private commercial interests.

Nitrous oxide emissions alone accounted for approximately 26% of the total

However if the timing and controls on hot moments are unknown or sporadic, less frequent sampling may significantly underestimate N2O emissions . Our results suggest that roughly 8,000 randomized individual chamber flux measurements would be needed to accurately estimate annual N2O budgets from these agricultural peat lands with a 95% confidence interval and 10% margin of error, assuming the drivers of hot moments were not well understood. Approximately 500 individual measurements would yield a 50% margin of error. Given the more sporadic nature of CH4 hot moments, our results suggest that it is even more difficult to accurately estimate CH4 fluxes with periodic sampling in these ecosystems. Analyses found that at least 17,000 and 2,500 individual flux measurements would be needed to estimate annual CH4 budgets within a 10% and 50% margin of error, respectively. The agricultural maize peat land soil studied here was a much larger source of soil GHG emissions than other maize agroecosystems. While agricultural peat soils are highly productive, average annual GHG emissions were 3.6-33.3 times greater on an area-scaled basis and 3-15.6 times greater on yield-scaled basis relative to other agricultural maize emissions estimates. We conducted an upscaling exercise as a first approximation of the potential impacts of maize peat land fluxes on regional GHG budgets. Our estimates suggested that maize agriculture on similar peat soils in the region could emit an average of 1.86 Tg CO2e y-1 .This value is significantly higher than previous estimates for the region and highlights the importance of including high frequency N2O measurements to capture hot moments in N2O fluxes,plastic pots 30 liters the disproportionate impact N2O emissions have on agricultural peat land GHG budgets, and that these agricultural peat lands are significant N2O sources.

We also found that irrigation timing and duration, not fertilization, was the predominant driver of N2O and CH4 emissions and a significant source of the total GHG budget. Determining management strategies that reduce soil N2O and CH4 emissions, particularly changes in flood irrigation timing and duration, could have a disproportionate impact on reducing total agricultural peat land GHG emissions .Although legends of humans using coffee in Ethiopia date back as early as 875 A.D., the earliest verifiable evidence of human coffee consumption occurs in Yemen in the 15th century. At this time, it was illegal to bring unroasted coffee out of Arabia, and strict measures were taken to ensure that viable coffee seeds did not leave the country. The birth of coffee production in India is attributed to the Indian Muslim saint Baba Budan, who, on his return from a pilgrimage to Mecca, allegedly smuggled seven coffee beans out of Arabia by hiding them in his beard. In 1670 he planted these seeds in Karnataka, and cultivation soon spread throughout the state and into neighboring regions. The first large-scale plantations arose with British colonization and spread rapidly throughout South India, fueled by increasing demand for export to northern latitudes. The proliferation of coffeehouses in Western Europe during this era proved to have substantial social consequences. Also known as “Penny Universities” since the price of entry and a cup of coffee was commonly one penny, coffeehouses in 17th century Britain came to play an important role in social and political discourse. In a society with such a rigid socioeconomic class structure, coffeehouses were unique because they were one of the only places frequented by customers of all classes.Thus they became popular establishments for discourse and debate, open to all classes and unfettered by the structure of academic universities. Intellectuals found in “the hot black liquor a curious stimulus quite unlike that produced by fermented juice of grape.”English coffeehouses “provided public space at a time when political action and debate had begun to spill beyond the institutions that had traditionally contained them,” and because of this, are widely accepted as playing a significant role in birthing the age of Enlightenment in Europe.While coffee was bringing the Enlightenment to Western Europe, the commodity was having opposite effects in the regions where it was being produced.

In India, the age of British plantations was rife with suffering and oppression, as slavery and forced labor were common practice. Historical research reveals that “during Europe’s industrial revolution and rise of bourgeois society, slavery, coffee production, and plantations were inextricably linked.”Historical records indicate that in the 1830s, the East India Company held over 247,000 slaves in Wayanad the Malabar coast alone.Even after slavery was officially abolished in 1861, so-called “agricultural slavery” and indentured labor on plantations continued. 8 According to historical accounts, indentured laborers were treated almost identically as they were during the height of slavery. To this day, an estimated 18.3 million people in India and 46 million people worldwide live in conditions of modern defacto slavery, such as bonded labor, human trafficking, and forced marriage. The global coffee market has always been volatile. Plagued by unpredictable harvests, susceptibility to weather events, and massive disease outbreaks, regional coffee production has risen and fallen dramatically over the centuries. For example, in the late 19th century in Sri Lanka an outbreak of the fungal pathogen known as “coffee rust” caused 90 percent of area under coffee cultivation on the island to be abandoned. 11 This past century has been no different for India. As the Great Depression affected coffee exports around the world in the 1930s, the Coffee Board of India was established to protect farmers and promote consumption of coffee. The Coffee Board of India, run by the federal government’s Ministry of Commerce and Industry, pooled farmers’ coffee for export at a set price. This provided price stability for farmers but also eliminated incentives to improve quality. From 1991 – 1996 a series of economic reforms relegated the coffee market in India entirely to the private sector. Immediately thereafter, the price of coffee fell from its 1997 levels of around $2.50 per pound to a staggering 45 cents per pound in 2002, the lowest it has been in over fifty years.India was not alone in this plight. While certainly not the only cause of financial insecurity among farmers, the spread of neoliberalism and free trade in the global commodity market has historically been associated with large increases in price volatility and overall downward trends in price, which has had deleterious effects for small-scale producers who depend on these markets for their livelihoods.

Especially in the 1980s and 1990s, growth and consolidation among multinational commodity traders led to a relative loss of market power among producing nations, while foreign pressure from international donors forced many of those nations to privatize their commodity export authorities against their own best interests.This has led to income instability and poverty for many coffee farmers around the world. The coffee farmers of Kerala are facing many of the same challenges that currently plague coffee farmers all over the world. In recent years the global price of coffee has fell drastically from $2.88-per-pound in 2011 to 93 cents-per-pound as of May 2019.While maintaining its downward trend over the past decade, the price continues to fluctuate wildly, making it impossible for farmers to budget their yearly expenses. It is not unheard of for the price to even dip below an individual farmer’s production costs,round plastic pots leaving powerless farmers forced to sell their harvest at a loss, or let it spoil in the fields and get nothing at all. How is it possible that coffee farmers are selling their harvest for less than what it cost them to produce it? While this seems paradoxical to the very basis of economics, it is a common situation facing farmers of many different cash crops, where prices are determined by what are called “buyer-driven supply chains.” While many factors go into the creation of buyer-driven supply chains, some of the few largest factors are discussed below. All this to say, farmers do not have the capacity to determine the price they get for their own products. Prices are driven by market conditions, speculation, futures contracts, and corporate interests who control the majority of world-market shares. With the growth of powerful commodities traders and the liberalization of international markets, prices for coffee and incomes for farmers have reached historic lows. This has led to an increasingly tenuous existence for those who already struggle to get by. Historically, coffee cultivation consisted of only one plant species, Coffea arabica. Today, Coffea arabica still makes up most of the world’s coffee production , but cultivation of another species, Coffea canephora, also known as robusta coffee, is growing due to its higher levels of hardiness and productivity.In addition, a very small amount of a third species Coffea liberica is grown. Although modern coffee production is currently limited to the scope of these three species, a large diversity of sub-varieties and hybrids are grown throughout the world, each with their own unique flavors and characteristics. Coffea arabica is widely lauded as having the best cup quality, and consistently fetches a higher price on the global commodity market. It also tends to grow better in slightly shaded conditions, making it conducive to traditional inter cropping methods.

In India, Coffea arabica is usually grown under the shade of other cultivated trees, such as jackfruit and areca nut, or under the shade of native forest trees, which are used to support vines of black pepper.In the under story below the coffee plants ginger, clove and turmeric are grown. In addition to sustaining families of farmers for generations, a recent study has shown that these multi-species farms support much higher levels of animal biodiversity than conventional monocultures, and that they sequester soil carbon at the same rate as surrounding rain forests. However, the rise of C. canephora as a cash crop has changed things in Kerala. Due to its higher yields and tolerance to pests such as coffee rust C. canephora plantations have replaced multi-species C. arabica farms over huge swaths of India in recent decades. Today, nearly 80% of coffee grown in Wayanad and surrounding regions is C. canephora. Since this robusta species prefers full-sun conditions, this shift away from C. arabica is associated with the removal of shade trees and a proliferation of full-sun monoculture coffee plantations. This has had substantial consequences for biodiversity, erosion, watershed management, and other ecosystem services.This has the potential to negatively impact the small amount of C. arabica that remains in Kerala. Studies indicate that deforestation can lead to a hotter and drier local climate.Coffea arabica is a finicky plant, thriving in a narrow temperature range between 18˚ – 21˚ Celcius.It follows that this pattern of tree removal could lead to conditions in Kerala becoming less ideal for Coffea arabica. This would suggest the potential for a feedback loop, in which robusta production and the associated deforestation lead even more farmers to convert to robusta in order to cope with changing environmental conditions. If climate change is occurring in Kerala, it would not only be threatening cultivated coffee, but also a multitude of wild species. At least six species of wild coffee are known to occur in India.According to a recent study there are now 124 known species of wild coffee, each with their own under-studied and potentially useful characteristics, such as drought or pest resistance, unique flavor profiles, or naturally decaffeinated beans.Of these, an estimated 60% are threatened with extinction due mostly to climate change and habitat loss.The following analysis examines the local climate of Wayanad in recent decades to determine if any changes are occurring. Farmers interviewed during a field visit to Kerala assert that local conditions have become hotter and drier, especially during specific times of the year that are important to the life cycle of the coffee plant. The farmers of Wayanad have suggested an increasingly unpredictable monsoon season, a failure of the “blossom rains” in early spring, and a decrease in November showers. The following study was conducted to corroborate the personal experience of these farmers, and, in the event that trends are found, to determine if causal factors point to global-scale or local forcings. The district of Wayanad in the State of Kerala, India is a mountainous tropical region with altitudes ranging from 700 to 2100m above sea level, daily temperature minimums from 14˚ – 20˚ C, and daily temperature maximums from 25 – 32˚ C.

The application of an appropriate photochemical model could answer this unknown

Although some microorganisms also fix nitrogen, they do not represent significant sources of atmospheric NH3 on Earth. Likewise, the associated detection of N2O and other nitrogen-containing species would provide confidence that the production of NH3 is associated with industrial disruption of a planetary nitrogen cycle. It is worth emphasizing that NH3 or N2O alone would not necessarily be technosignatures, as either of these species could be false positives for life or could arise from nontechnological life . Rather, it is the combination of NH3 and N2O that would indicate disruption of a planetary nitrogen cycle from an ExoFarm, which may also show elevated abundances of NOx gases as well as CH4. The short lifetime of NH3 in an oxic atmosphere implies that a detectable abundance of NH3 would suggest a continuous production source. Although NH3 could be produced abiotically by combining N2 and H2, an atmosphere rich in H2 would be unstable to the O2 abundance required to sustain photosynthesis. The technosignature of an ExoFarm would therefore require the simultaneous detection of both NH3 and N2O in the atmosphere of an exoplanet along with O2, H2O, and CO2.Large-scale agriculture based on Haber–Bosch nitrogen fixation could be detectable through the infrared spectral absorption features of NH3 and N2O as well as CH4. A robust assessment of the detectability of such spectral features in an Earth-like atmosphere would ideally use a three-dimensional coupled climate–chemistry model to calculate the steady-state abundances of each of these nitrogen-containing species a function of biological and technological surface fluxes. But as an initial assessment,hydropopnic barley fodder system we consider a scaling argument to examine the spectral features that could be detectable for present-day and future Earth agriculture.

We define four scenarios for considering agriculture on an Earth-like planet, with the corresponding atmospheric abundances of nitrogen-containing species listed in Table 1. The present-day Earth scenario is based on recent measurements of NH3, N2O, and CH4 abundances . The choice of 10 ppb for NH3 is toward the higher end for Earth today and corresponds to regions of intense agricultural production. The preagricultural Earth scenario serves as a control, where the agricultural and technological contributions of NH3, N2O, and CH4 have been removed. Note that this approach assumes that eliminating the technological contributions to the atmospheric flux of these nitrogen-containing species will reduce the steady-state atmospheric abundance by a similar percentage; this approach is admittedly simplified, but the results can still be instructive for identifying the possibility of detectable spectral features. The third and fourth scenarios project possible abundances of NH3, N2O, and CH4 for futures with 30 and 100 billion people, respectively. Earth holds about 7.9 billion people today, and population projections differ on whether or not Earth’s population will stabilize in the coming century . These two population values were selected because they correspond approximately to the maximum total allowable population using all current arable land and all possible agricultural land . Most published estimates of Earth’s carrying capacity range from about 8 to 100 billion, although some estimates are less than 1 billion while others are more than 1 trillion . Theoretically, an extraterrestrial population with the energy requirements of up to 100 billion calorie consuming humans could sustain Haber–Bosch synthesis over long timescales, as long as sustainable energy sources are used . These scenarios also follow a scaling argument by assuming that the per-person contributions of these three nitrogen-containing species will remain constant as population grows. This again is a simplifying assumption that is intended as an initial approach to understanding the detectability of such scenarios.

We consider the detectability of all four of these scenarios using the Planetary Spectrum Generator . PSG is an online radiative transfer tool for calculating synthetic planetary spectra and assessing the limits of detectability for spectral features that can range from ultraviolet to radio wavelengths. The ultraviolet features of NH3, N2O, and CH4 are strongly overlapping and only show weak absorption, but mid-infrared features of all these species could be more pronounced. The mid-infrared spectral features of NH3, N2O, and CH4 calculated with PSG for preagricultural, present-day, and future Earth scenarios are plotted in Figure 1, which shows the relative intensity and transmittance spectra for observations of an Earth-like exoplanet orbiting a Sun-like star. The spectra shown in Figure 1 show the strongest absorption features due to NH3 from 10 to 12 μm, while N2O shows absorption features from 3 to 5 μm, 7 to 9 μm, and 16 to 18 μm. Absorption features due to CH4 overlap some of the N2O features from 3 to 5 μm and 7 to 9 μm. The change in peak transmittance between 10 and 12 μm for NH3 compared to the preagricultural control case is about 50% for the future Earth scenario with 100 billion people and about 25% for the scenario with 30 billion people. For N2O, the change in peak transmittance between 16 and 18 μm compared to the preagricultural control case is about 70% for 100 billion people and 50% for 30 billion people. The change in relative intensity for the 100 billion people scenario is up to about 10% compared to the preagricultural control case between 7 and 9 μm and 10 and 12 μm. Present-day Earth agriculture would exert a weakly detectable signal that might be difficult to discern from the preagricultural control case, but future scenarios with enhanced global agriculture could produce absorption features that are easier to detect. The spectral features of NH3, N2O, and CH4 could be detectable in emitted light or as transmission features for transiting planets. Specifically, the N2O line at 17.0 μm shows a strong dependency with the N2O volume mixing ratio and to a second order the NH3 line at 10.7 μm. For the future 100 billion case, both display strong enough absorption to bdetectable by the Large Interferometer for Exoplanets , Origins and Mid-InfraRed Exo-planet CLimate Explorer infrared mission concepts.

The James Webb Space Telescope Near Infrared Spectrograph could potentially detect CH4 within the 0.6–5.3 μm range for transiting exoplanets . However, the detection of CH4 alone would provide no basis for distinguishing between technological, biological, or photochemical production. The detectability of these spectral features do not necessarily directly correspond to the peak transmittance, and a full accounting of the detectability of each band would need to account for the observing mode and instrument parameters. It is beyond the scope of this present paper to present detectability calculations for specific missions, as any missions capable of searching for mid-infrared technosignatures are in an early design phase, at best. One of the goals of this Letter is to highlight the importance of examining mid-infrared spectral features of exoplanets,livestock fodder system as many potential technosignatures could be most detectable at such wavelengths. Also, it demonstrates the duality of the search for bio-signatures and technosignatures. The search for passive, atmospheric technosignatures does not require the development of a dedicated instrument but can leverage the capability of instruments dedicated to the search for bio-signatures.The calculations presented in this Letter indicate the possibility of detecting a technosignature from planetary-scale agriculture from the combined the spectral features of NH3 and N2O, as well as CH4. The signature of such an ExoFarm could only occur on a planet that already supports photosynthesis, so such a planet will necessarily already show spectral features due to H2O, O2, and CO2. The search for technosignatures from extraterrestrial agriculture would therefore be a goal that supports the search for bio-signatures of Earth-like planets, as the best targets to search for signs of nitrogen cycle disruption would be planets already thought to be good candidates for photosynthetic life. A better constraint on the detectability of the spectral features of an ExoFarm would require the use of an atmospheric photochemistry model. This Letter assumed simple scaling arguments for the abundances of nitrogen containing species, but the steady-state abundance of nitrogen containing atmospheric species will depend on a complex network of chemical reactions and the photochemical impact of the host star’s UV spectrum. In such future work, the increases of NH3 and N2O, and CH4 from agriculture would be parameterized via surface fluxes instead of arbitrary fixed and vertically constant mixing ratios. A network of photochemical reactions would then determine the vertical distribution of those species in the atmosphere. A photochemical model could also capture the processes of wet and dry deposition of NH3, which is the major sink in Earth’s present atmosphere, as well as aerosol formation from NH3 and SO2/N2O that can occur in regions of high agricultural production. Past studies have predicted more favorable build-up of bio-signature gases on oxygen-rich Earth-like planets orbiting later spectral type stars due to orders of magnitude less efficient production of OH, O, and other radicals that attack trace gases like CH4 .

The photochemical lifetime of N2O and therefore its steady-state mixing ratio will be enhanced by less efficient production of O radicals that destroy it. However, because deposition is the major sink of NH3, it is not clear whether a different stellar environment would alter the atmospheric lifetime of NH3, and if so, to what extent.Examining the four scenarios in this study with such a photochemical model would require additional development work to extend the capabilities of existing models to oxygenrich atmospheres. Past photochemical modeling studies that have included NH3 considered anoxic early Earth scenarios where the focus was determining the plausible greenhouse impact of NH3 to revolve the faint young Sun paradox . More recent studies have considered NH3 bio-signatures in H2-dominated super-Earth atmospheres, which would greatly favor the spectral detectability of the gas relative to high molecular weight O2-rich atmospheres . On H2 planets with surfaces saturated with NH3, deposition is inefficient, and sufficient biological fluxes can overwhelm photochemical sinks and can allow large NH3 mixing ratios to be maintained . These “Cold Haber Worlds” are far different from the O2–N2 atmosphere we consider here, where surfaces saturated in NH3 are implausible and photochemical lifetimes are shorter. Ideally, future calculations would use a three-dimensional model with coupled climate and photochemical processes suitable for an O2–N2 atmosphere to more completely constrain the steady-state abundances, and time variation, in nitrogencontaining species for planets with intensive agriculture. Future investigation should also consider false-positive scenarios for NH3 and N2O as a technosignature. One possibility is that a species engages in global-scale agriculture using manure only; such a planet could conceivably accumulate detectable quantities of NH3 and N2O without the use of the Haber–Bosch process. The distinction between these two scenarios might be difficult to resolve, but both forms of agriculture nevertheless represent a technological innovation. Whether or not similar quantities of NH3 and N2O could accumulate on a planet by animal-like life without active management is a possible area for future work. External factors such as stellar proton events associated with flares could also produce high abundances of nitrogen-containing species in an atmosphere rich in NH3 , so additional false-positive scenarios should be considered for planets in systems with high stellar activity. This Letter is intended to present the idea that the spectral signature of extraterrestrial agriculture would be a compelling technosignature. This does not necessarily imply that extraterrestrial agriculture must exist or be commonplace, but the idea of searching for spectral features of an ExoFarm remains a plausible technosignature based on future projections of Earth today. Such a technosignature could also be long-lived, perhaps on geologic timescales, and would indicate the presence of a technological species that has managed to coexist with technology while avoiding extinction. Long-lived technosignatures are the most likely to be discovered by astronomical means, so scientists engaged in the search for technosignatures should continue to think critically about technological processes that could be managed across geologic timescales. J.H.M. gratefully acknowledges support from the NASA Exobiology program under grant 80NSSC20K0622. E.W.S. acknowledges support from the NASA Interdisciplinary Consortia for Astrobiology Research program. T.J.F and R.K.K. acknowledge support from the GSFC Sellers Exoplanet Environments Collaboration , which is supported by NASA’s Planetary Science Divisions Research Program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of their employers or NASA.

Detractors warned consumers of substantial food cost increases due to the extremely low threshold

As a result of a practical labeling scheme, the Japanese consumer can purchase non-GM products that are not organic, an option that would all but disappear with Prop 37 in California. Furthermore, in Japan, like in Australia, highly processed products such as canola oil, produced with GM crops, are exempt from labeling. In contrast, the same canola oil would have to bear a cautionary label under Prop 37, in spite of difficulties testing whether the oil has indeed been derived from GM canola. Earlier this year, the American Medical Association formally opposed the mandatory labeling of GM food. The National Academy of Sciences and the World Health Organization previously reached similar conclusions–there is no science-based justification for mandatory labeling of GM food because there is no evidence that such foods pose any risks to human health. Because it will be interpreted as a warning, mandatory labeling would imply a food safety risk that does not exist, and this in itself would be misleading to consumers. If passed, the full economic effects of Prop 37 are uncertain but there is no doubt that the measure would remove most of the certified non-GM processed foods from the California market because of the zero tolerance criterion for low levels of unintended material. Food manufacturers and retailers would be unwilling to supply a large number of both GM and non-GM processed food products due to litigation risk. For instance, there would be a change in the selection of corn flakes boxes on the food shelf. The consumers’ choice would be either organic corn flakes or corn flakes labeled as possibly containing GM. It is believed that 70–80% of processed food intentionally contain some corn,macetas cuadradas canola or soy ingredients, so these products would have to be labeled, reformulated with non-GM substitutes, or removed.

Other processed food products that do not use soy, corn, or canola could also be affected and require labeling, because they might contain unintended trace amounts of corn, canola or soy. As a consequence, Prop 37 would result in many products on the food shelf carrying a GM label. It might get to the point where there are so many products with GM labels that most consumers would just ignore the labels because they would be everywhere. For foods that contain a relatively small amount of corn or soy ingredients, the food industry could either label their products as GM or look for alternative, and possibly inferior, non-GM substitute ingredients to avoid labeling. For instance, food companies would have an incentive to use alternative ingredients such as imported palm oil to replace soybean or canola oil, despite potential health problems associated with palm oil and environmental concerns due to palm oil expansion in Asia. Mandatory labeling requirements could inhibit further development of GM technology in California’s food industry. The United States has criticized the EU’s mandatory GM labeling as being nothing more than international trade protection from foreign competition. In fact, over the last twenty years, the USDA, the FDA and the State Department, under successive administrations from both sides of the political spectrum, have publicly opposed this type of regulation at the international level because of its market distorting effects. Prop 37 may also be interpreted as an attempt to stifle competition and distort markets. In this article we outline the economic implications of GM food labeling programs to provide insight into the likely effects of introducing mandatory labeling of GM foods in California under Prop 37. Supporters of Measure 37 argue that labeling provides California consumers additional information and allows them to avoid consuming GM food. But California food consumers have that choice now. They can purchase from three different food categories: 1) conventional foods , 2) organic foods , or 3) voluntarily labeled non-GM food that is not organic. Compare this current situation to the likely outcome under Prop 37 . For targeted food products derived from GM grains, Prop 37 will most likely replace the existing three food categories listed above with just two categories: 1) organic, or 2) products labeled as “may be produced with genetic engineering.” In other words, there will be numerous GE labeled products.

For highly processed food products, a non-labeled option will remain but may only make sense using either lower grade or more expensive alternative ingredients. In general the organic suppliers will gain market share because the producers of most certified non-GM foods will have to change their label to read “may contain GM,” whereas the organic label will not be forced to change, even if the organic product has the same trace amount of GM as the non-GM counterpart. Since the perunit cost of producing non-GM crops is less than organic crops, overall food prices will rise on average as non-GM food products lose market share.Table 1 summarizes the key features of Prop 37–The California Right to Know Genetically Engineered Food Act. If passed, it will require retail labeling of some raw agricultural GM commodities as being “genetically engineered” and processed foods containing GM ingredients as “ partially produced with genetic engineering.” Exemptions from labeling would be granted to alcoholic beverages, restaurant and ready-made food, foods “entirely” derived from animals, and any food certified as USDA Organic. Also exempt would be any raw agricultural commodity that could be certified that it was produced without the intentional use of GE seed. Furthermore, Prop 37 would prohibit food labels with the message “natural,” “naturally grown,” or anything similar. The initiative charges the California Department of Public Health with enforcement, which the Legislative Analyst Office predicts will cost $1 million annually. Prop 37 sets purity standards for non-GM food that are much higher than existing standards for organic food. Organic certification is “process based,” which means that as long as the farm is an approved organic farm, following the prescribed agronomic practices, there is less industry concern over accidental contamination and therefore no regular testing for GM. Unlike Prop 37, USDA organic standards do not have a strict “zero tolerance” standard for accidental presence of GM material. In fact, the USDA has not established a threshold level for adventitious presence of GM material in organic foods. Organic growers are listed among the coalition of supporters of Prop 37, which is understandable because of the exemption provided to them by Prop 37. If Prop 37 passes, a food product could be labeled as organic and escape the testing and litigation issues facing a similar non-organic product even if both products contained identical accidental trace amounts of GM material. Mandatory labeling is unnecessary because voluntary labeling now gives California consumers a choice to purchase food products that do not contain GMOs . One existing voluntary “GM-free” labeling program is the Non-GMO Project, a verification process organized by food retailers such as Whole Foods Market.

The Non-GMO project uses the same 0.9% threshold as the EU and under this scheme, retailers receive a price premium for selling non-GM products. Whole Foods carries numerous Non-GMO products under its private label, 365 Everyday Value®, and many of these products are also organically produced. Similarly, all food products sold at Trader Joe’s with the Trader Joe’s label are sourced from non-GM ingredients , but they are not part of the Non-GMO project. Like Whole Foods, Trader Joe’s is not actively supporting mandatory labeling of GM foods under Prop 37, perhaps because it would disrupt their product lines. Several processed food products in Trader Joe’s stores that are not privately branded would likely require the new cautionary label under Prop 37,maceta cuadrada plastico not to mention all of the products under the Trader Joe’s line that will not meet the zero tolerance . The issue surrounding Prop 37 is similar to an earlier debate that took place in the 1990s over dairy products from cows treated with rBST . The U.S. FDA ruled that no mandatory labeling of products derived from cows receiving the growth hormone was necessary because the milk was indistinguishable from products derived from untreated herds. Then the state of Vermont passed a law requiring that milk from rBST treated cows be labeled to better provide consumers information. The Vermont legislation was based on “strong consumer interest” and the “public’s right to know.” Dairy manufacturers challenged the constitutionality of the Vermont law under the First Amendment and they won. The Second Circuit Court of Appeals struck down the Vermont law, ruling that labeling cannot be mandated just because some consumers are curious. The court ruled “were consumer interest alone sufficient, there is no end to the information that states could require manufacturers to disclose about their production methods”… “Instead, those consumers interested in such information should exercise the power of their purses by buying products from manufacturers who voluntarily reveal it.” . Instead of mandatory labeling, a non-rBST standard was voluntarily developed by the industry with specifications from the FDA. It has been largely applied to dairy products, giving consumers a choice; but unlike mandatory labeling, producers voluntarily responded to consumer demand for non-rBST milk, following a bottom-up process—it was not a mandate imposed on them by top-down regulations. There are a variety of international mandatory GM labeling programs differing by the products to which they are applied, the mandated adventitious threshold, and whether they apply to the “product” as a whole or to the “process” .

Table 3 summarizes the mandatory labeling laws of a select group of developed nations. As shown in the table, mandatory labeling of GM food exists and is enforced in places like Japan, the EU, South Korea, Australia, and New Zealand. Some developing or transition economies also have mandatory labeling but without strict enforcement. With mandatory labeling, consumers are not necessarily provided with greater choice at the food store. Furthermore, there is a substantial amount of GM food eaten in the EU and Japan that does not have to be labeled. These products include certain animal products, soya sauce and vegetable oils , among others. Internationally, the Codex Alimentarius Commission, an international standards-setting body for food, examined and debated GM food labeling for over twenty years without reaching any consensus. In 2011 a decision was eventually made, but the final text approved by all countries does not provide any recommendation as to the labeling of GM food. It only calls on countries to follow other Codex guidelines on food labeling . This non-endorsement means that countries using mandatory labeling could face legitimate claims of unfair trade restrictions resulting in a World Trade Organization dispute. A labeling initiative similar to California’s Prop 37 appeared on the ballot in Oregon in 2002. This initiative also proposed mandatory labeling, but defined an adventitious threshold of 0.1% per ingredient. Despite a claim of an overwhelming level of public support for GM labeling, the initiative ultimately failed with 70% voting “no.”Additionally, even if the measure had passed, it was unlikely that producers would have segregated GM foods from non-GM, non-organic, as the costs would have been prohibitive—especially for a relatively small state with a population fewer than four million. The bulk of private costs incurred as a result of labeling requirements are from efforts to prevent or limit mixing within the non-GM supply chain, known as identity preservation programs. The cost of any IP program depends critically on the level of the adventitious presence threshold specified in the labeling program. In the case of Prop 37 these costs would be incurred throughout the processed food industry. For instance, a firm marketing a wheat food product would incur costs to ensure its product did not contain trace amounts of soy, canola, or corn, because these grains all use the same grain handling and transport system. The goal of providing consumers with additional information and choice is only met when both product types are carried in food stores. In the EU, companies resorted to substituting ingredients to avoid the label, using lower quality and/or higher priced inputs, something that could also happen in California for processed products. EU consumers were not offered much new information, since no products carried a GM label after the introduction of mandatory labeling. In fact, the EU proponents of labeling are not satisfied with the existing EU regulations because of its exemptions and they have asked for an extension of labeling to include animal products.

Neither of those sources of water is subsidized to any significant degree

This highlights that the method used to define θfc in our study, while objective and tied strictly to soil moisture retention parameters, produced θfc estimates that are relatively conservative from a flow-based definition of θfc, as they are based on how a 1-cm slice of soil would drain. In a soil profile that has been deeply wetted, such as those used in this Ag-MAR modeling study, the defined θfc cannot be achieved by drainage alone within a reasonable time-frame, even at 10-cm depth in a 200-cm sandy loam profile . Thus, the corresponding time-to trafficability estimates should be interpreted as relatively conservative, especially for those soils with low plasticity indices such as sands and sandy loams. This is not to say, however, that the definitions used in this study are outside the norms of soil science. θfc is often defined with a standard tension . All textures but silt loam have estimated θfc values that correspond to this tension range . Finer-textured soils may still have some risk of compaction at the thresholds defined in this study, given their high plasticity indices and the relatively high Ksat estimates produced by the ROSETTA pedotransfer function for these textures . Similarly, while presence of a Bt horizon underlying various surface textures did not consistently delay time-to-trafficability, ROSETTA may overestimate the permeability of 2:1 clay enriched sub-soils occurring on, for example, stable river terraces above current floodplains,hydroponic container system especially in the eastern uplands of the San Joaquin Valley . Thus, these landscapes should be treated more cautiously if used for Ag-MAR during periods when trafficability is required, especially during low PET conditions.

For example, in an Australian study of Vertisol trafficability under irrigated cotton production, researchers concluded that risk-free trafficability only really existed at water contents near wilting point , which is equivalent to about 60% of the mean θfc for clays in our study . On the other hand, this contrasts sharply with a field study in the Netherlands which found that a heavy clay soil under pasture was trafficable at just 90 cm soil moisture tension based on observation of compaction patterns and tensiometer readings , which is moister than the wettest, commonly used tension-based definition of θfc . An additional uncertainty in trafficability and work ability research is the extent to which surficial trafficability and work ability moisture thresholds are sufficient to prevent detrimental subsoil compaction that requires more effort to ameliorate . Field validation studies that include modeling of soil moisture to predict suitable days for agricultural operations and that simultaneously examine full soil profile effects of wheel traffic occurring at or below these moisture thresholds have not yet been reported. A study of controlled traffic farming systems in California cotton production highlights this need, since bulk density increased to 25-cm depth while penetrometer resistance increased to at least 100-cm soil depth under wheel traffic in a sandy loam soil , but no operational decisions were guided by trafficability soil moisture thresholds in their study. Finally, the time-to-trafficability estimates are meant to guide operational decisions when crops are dormant or fields are fallow, given that root water uptake was intentionally neglected and only drainage and bare soil evaporation were considered in H1D simulations. For major perennial crops, end of dormancy typically ranges from mid February to late-April , spanning the time period addressed by this study. There are several reasons for omitting scenarios when root water uptake is active. First, Ag-MAR is recognized to be a risk to many actively growing crops due to the possibility of developing anoxic soil conditions . Second, for deep wetting events more generally, accurate root water uptake modeling requires knowledge of root depth distribution and crop canopy coverage. Third, the need for irrigation water may arise before the soil moisture trafficability threshold during active root water uptake for more sensitive crops or during specific periods of growth.

All of these considerations complicate the ability to provide a generalizable time-to-trafficability tool to growers that also accounts for crop root water uptake.A relationship between global warming and increased concentrations of greenhouse gases such as carbon dioxide , produced by the burning of fossil fuels, is suggested by much accumulating evidence. As far back as 1992, more than 150 governments attending the Rio Earth Summit signed the Framework Convention on Global Climate change. Article 2 states that the ”ultimate objective of this Convention … is to achieve … stabilization of greenhouse gas concentrations that would prevent dangerous anthropogenic interference with the climate system.” More than ten years later, the questions remain: how ”dangerous” are the consequences of anthropogenic interference, and how much ”stabilization” is justified? The economics literature so far has given mixed results with regards to the impact on agriculture.1 In the remainder of this section we give a brief overview of previous approaches to set the stage for our study. These can be divided into three broad categories, beginning with the agronomic approach, based on the use of agronomic models that simulate crop growth over the life cycle of the plant and measure the effect of changed climate conditions on crop yield and input requirements. For example, Adams relies on crop simulation models to derive the predicted change for both irrigated and rainfed wheat, corn, and soybeans. The predicted changes in yields are then combined with economic models of farm level crop choice, using linear or nonlinear programming.The analysis, however, usually considers variable but not fixed costs of production. It often turns out to be necessary to add artificial constraints to make the programming model solution replicate actual farmer behavior in the baseline period. Moreover, the analysis focuses on the agricultural sector, and ignores the linkages with the remainder of the economy which would make the input prices and input allocations to agriculture endogenous.

This is remedied in the computable general equilibriumapproach, which models agriculture in relation to the other major sectors of the economy and allows resources to move between sectors in response to economic incentives. An example is FARM, the eight-region CGE model of the world agricultural economy by the United State Department of Agriculture. However, while a CGE model has the advantages of making prices endogenous and accounting for inter-sectoral linkages, these come at the cost of quite drastic aggregation in which spatially and economically diverse sectors are characterized by a representative farm or firm. In summary, on the one hand the agronomic models do not fully capture the adaptation and mitigation strategies of farmers in the face of climate change, while on the other the CGE models are only appropriate to highly aggregated sectors of the economy. Mendelsohn, Nordhaus and Shaw provide an interesting middle ground, proposing what they call a Ricardian approach, essentially a hedonic model of farmland pricing, based on the notion that the value of a tract of land capitalizes the discounted value of all future profits or rents that can be derived from the land. The advantage of the hedonic approach is that it relies on the cross-sectional variation to identify the implicit choices of landowners regarding the allocation of their land among competing uses instead of directly modeling their decision. Further,planter pots drainage the hedonic function also allows one to calculate the direct impact on each farmer, county or state, in contrast to the highly aggregated structural CGE models. This is the approach we adopt, though with a number of innovations indicated below and explained in detail in succeeding sections. In this paper we resolve some of the differences in previous studies by estimating a hedonic equation for farmland value east of the 100th meridian, the boundary of the region in the United States where farming is possible without irrigation. The main contributions of the paper are: First, we incorporate climate differently than previous studies, by using transformations of the climatic variables suggested by the agronomic literature. The relationship between climatic variables and plant growth is highly nonlinear and our approach yields results that are consistent with the agronomic evidence. Second, we develop a new data set that integrates the spatial distribution of soil and climatic variables within a county with the help of a Landsat satellite scan of the contiguous United States. Third, we allow the error terms to be spatially correlated to obtain a more efficient estimator and correct t-values . Fourth, we present several sensitivity checks, and show that our results are robust to both different specifications and census years. We show that results remain similarly unchanged when we include state fixed effects to control for the influence of state-specific factors unrelated to climate, such as property taxes and crop subsidies. Finally, we evaluate potential impacts of warming using new climate projections from the most recent runs of two of the major global climate models. The paper is organized as follows. Section 2 outlines a model of farmland value with attention to issues raised by irrigation. Section 3 addresses spatial issues that arise in the definition and measurement of climatic and soil variables and in the correlation of error terms.

Section 4 presents our empirical results, including tests for spatial correlation and estimates of the hedonic regression coefficients and discusses a variety of tests of robustness of the results. Section 5 uses the results to generate estimates of regionally differentiated impacts of climate change on agriculture. Section 6 summarizes our conclusions. In this framework, climate variables play two different roles. Temperature is an exogenous shift variable in the production function; increases in temperature increase the demand for water as an input and they can raise or lower yield, depending on the size of the increase.Precipitation has a different role in irrigated areas than in dryland areas. In dryland areas, the water supply for crops comes from precipitation falling on the field before and during the growing season; in this case, the water supply is fixed by nature in any given year, and it comes with a price of zero. In irrigated areas, by contrast, the water supply is man-made, using local groundwater or surface water imported from somewhere else, it comes at a cost, and the quantity is endogenously determined. In terms of location, since the time of John Wesley Powell it has been common to take the 100th meridian as a rough approximation of the rainfall line in the US. To the east, rainfall generally exceeds 20 inches per year while, to the west, rainfall is generally less than 20 inches per year. Since virtually all traditional US crops require at least 20 inches of water to grow, the 100th meridian marks the boundary of the arid West, where farming is generally possible only with use of irrigation.3 Thus the 17 western states account for about 88% of the 150 million acre feet of irrigation water used annually in the U.S. The economic implications of the distinction between dryland and irrigated farming are discussed in detail by Cline , Darwin , and Schlenker et al. , and will be summarized briefly here. In addition to the fact that precipitation does not measure water supply in the arid West, the other distinctive feature is that, in irrigated areas, future changes in water costs, unlike other input costs, are not likely to be capitalized in future land prices in the same way as past cost changes were capitalized in past land values. Many of the major surface water supply projects in the western United States were developed by the US Bureau of Reclamation or the Army Corps of Engineers and involved a substantial subsidy to farmers. Depending on the age of the project, there is substantial variation in federal irrigation charges across different projects, and these are clearly capitalized into farmland values. Failure to account for subsidies could bias other regression coefficients, especially climatic coefficients that in turn are correlated with the access to irrigation. Aside from the federal projects, the remainder of the irrigation supply in the western states comes from groundwater or from non-federal surface water storage projects.Nevertheless, in the case of irrigation with non-federal surface water it still would be misleading to predict the economic cost of a change in precipitation on the basis of a hedonic regression of current farmland values.

Climate change also has an impact on seasonal changes and timing of precipitation

San Diego’s landscape has historical and cultural importance, with more than 18 federally recognized tribes which is more Indian reservations than any other county in the United States . The combination of these natural open and agricultural lands, pristine coastal areas, diverse urban neighborhoods, and rich cultural history makes San Diego a vibrant and unique region that supports a variety of human communities and industries.Agricultural rangelands and croplands are an important feature within San Diego’s landscape, constituting 5.11% of the county’s total land with more than 250,000 acres and 5,000 farmers . These working lands are deeply rooted in the county’s landscape, holding historic, economic, environmental, and social significance while providing a multitude of local benefits. Not only are these working lands important to the county for providing the public with local products and counteracting urban growth, they have significant economic value. Ranked 12th largest in the nation, San Diego agriculture has an estimated $2.88 billion annual value to the economy . The region’s agriculture encompasses rangeland, pastureland, and cropland, used for growing annual, perennial, nursery, and field crops . Top crops include nursery products and crops, avocados, citrus, and miscellaneous vegetables . While the relatively moderate Mediterranean climate, in addition to a range of micro-climates, makes San Diego an ideal place to grow agricultural crops and livestock products , there are many challenges associated with farming in the region. San Diego’s current farmers face constraints on water-use efficiency and water availability that limits crop selection and efforts efforts to maximize production while also making a profit. From high irrigation demand,blueberry plant container increasing water costs and land prices, to pervasive pest and plant diseases, San Diego farmers have no choice but to utilize innovative farming techniques and choose smart crop choices .

Due to historic development patterns in San Diego, agriculture is often embedded within urban areas, with more small farms than any other county in the nation. Because of the average size of farms, the agricultural sector is spatially scattered throughout the unincorporated county, which can be difficult for identifying and monitoring existing agricultural land and practices. Nonetheless, San Diego’s agricultural production remains more valuable than many other urbanized areas of California, including San Francisco, Orange County, and Los Angeles combined . San Diego’s agricultural landscape is composed of diverse lands, with varying terrain, vegetation, and agricultural use. These lands provide valuable and beneficial services for the region’s food supply and ecosystems, including creation of wildlife, habitat, food for people and pollinators, and water filtration .At a latitude of approximately 32 degrees North, San Diego is situated in the heart of the subtropical climate zone. The region encompasses a unique landscape, positioned between the coastal zone of the Pacific Ocean to the west and the foothills, interior mountains, valleys, and deserts to the east. Like most areas in California, the region is known for its Mediterranean climate in which it experiences hot, dry summers, and mild winters . San Diego’s climate is characterized seasonally by latitudinal climate influences that cause this subtropical dryness in the summer and midlatitude storm-tracks in a concentrated wet season from October through April . Additionally, coastal low clouds and fog are a defining characteristic of San Diego’s climate. CLCF typically persist throughout early summer months, helping moderate heating, buffer dryness and solar insolation, while also providing cooling and water for the region’s coastal ecosystems . The combination of complex topography, coastal effects, and wide altitudinal ranges coupled with subtropical and midlatitude influences results in a range of diverse micro-climates throughout the region . In addition to impacting temperatures and humidity on the coast and further inland, the combination of these factors produce variability in monthly precipitation during the winter months . With annual precipitation totals varying from as little as 50% to greater than 200% of long-term averages, California experiences the largest yearly variations in precipitation compared to any other region in the U.S .

In particular, the year-to-year variability in southern California is higher than anywhere else in the U.S . The average annual precipitation for San Diego is 10.34 inches , however, historical averages reaching as low as 3.3 inches in 2002 and as high as 22.60 inches in 2005 highlight the region’s large inter-annual variability. Variability in precipitation is primarily tied to the number of extreme precipitation events, known as Atmospheric Rivers . ARs contribute to 68% of extreme-rainfall accumulations in southern California . Figure 4 illustrates the correlation between the number of these top 5% of rainy days and precipitation variability. Given that the occurrence of a few AR events each year dictate floods, droughts , and water availability , understanding these extreme events are important for regional weather forecasting, infrastructure planning, and resource management. The San Diego County Water Authority has served as the wholesale supplier for San Diego since its creation in 1944, working to secure reliable water supply for the region. SDCWA’s water supply sources have changed throughout San Diego’s unique historical periods. Despite these changes, SDCWA has consistently relied on imported water in some capacity . Currently, San Diego County imports around 80% of its water supply, using both local and imported sources . In the past, San Diego relied heavily on a single supplier of water, the Metropolitan Water District of Southern California , which includes water from Northern California and the Colorado Basin. Since the enactment of the Colorado River Compact in 1922, allowing for the diversion of water from the river to surrounding states, Colorado has been a major supplier for San Diego . In 1991, the MWD constituted 95% of San Diego’s water supply . In the last two decades, after an extensive drought that caused MWD to reduce water delivery to San Diego, SDCWA has developed several strategies and long-term plans to diversify the region’s water supply portfolio. These strategies aim to improve the region’s water infrastructure, promote water-use efficiency, and ultimately secure reliability of supply . In 2017, supply from MWD had significantly declined to 40%, allowing for inclusion of other sources. Agreements made with the Imperial Irrigation District, and the Coachella and All-American canals, which source water from the Colorado Basin, contributes another 40% of imported water to San Diego’s current supply portfolio. Local sources contribute the remainder of supply, including groundwater, recycled water, and desalination . Agriculture is one of the many sectors that is greatly dependent on these water resources. With water pricing escalating since the early 1990’s, water costs have been the primary water concern for San Diego farmers .

As drought conditions increasingly threaten the region’s imported water sources, farmers have shifted their focus towards water availability as well . While SDCWA has worked to ensure reliable and diversified water sources over the last few decades, new water sources have proven to be expensive . In the last 12 years, the price of water has tripled, while the revenue from farm products are generally consistent, creating challenges for farmers across the region. Water alone constitutes the largest monthly expense for many farmers . Thus, farmers are eager to adopt strategies that maximize water-use efficiency, minimize use and overall costs, and increase financial returns. For farmers who choose to participate in SDCWA’s special agricultural water pricing, water charges are priced at discounted rates. Nonetheless, costs per acre foot remain high, and much of the sector, specifically nursery, flower, fruit, and livestock farmers, do not participate . Not all of San Diego receives the imported water supplied by MWD and geographically,30 plant pot the majority of the unincorporated area is reliant on groundwater-dependent districts or private wells that are managed separately from SDCWA . Thus, these areas are completely reliant on groundwater resources and are impacted by its availability. The agricultural sector also relies on groundwater resources, and is considered one of the “large quantity” groundwater users . These groundwater resources are often limited due to unfavorable geology, resulting in aquifers with limited groundwater in storage volume and/or groundwater recharge. Several areas throughout the county that are groundwater-dependent, specifically the unincorporated county, face groundwater hydrology issues. Given that agricultural users are not regulated or metered for water quantity, these large quantity users can create localized groundwater problems throughout the groundwater dependent areas . It is clear that water resources, availability, and supply are major focuses for the county, especially the agricultural sector. With the need to limit water use to allow for profits, water concerns continue to be a driving force for the conservation efforts of San Diego’s farming community. It is projected that over the next several decades, California will continue to experience several changes associated with climate change, including sea level rise, precipitation patterns, and temperatures. Amid historic coastline and mountains, San Diego region encompasses many diverse climate zones. In turn, the region will likely experience a myriad of changes with dynamic, complex, and compounded effects. As a result, the county will face several challenges that could ultimately threaten the natural and human landscapes that it supports. While the region’s diverse ecological systems, industries and communities have adapted to San Diego’s variable and seasonal climate, climate change could exacerbate these conditions and ultimately threaten the survival of these valuable systems . As one of the most “climate-challenged” regions in North America, it is critical that the county understand these regional variations in climate impacts and vulnerability .

In the region, climate change will significantly increase yearly average temperature over the next several decades, with projections ranging from 5-10° Fahrenheit depending on the Representative Concentration Pathway greenhouse gas concentration and region . San Diego and neighboring areas will face varying changes in the average hottest day per year, daily maximum temperature and daily minimum temperature because of the region’s diverse topography and distinct micro-climates. Under RCP 8.5, representing a high concentration scenario, the average hottest day per year will increase from the historic range of 90-100° F to 100-110° F in coastal zones, and from 105-115° F to 110-125° F in desert regions . Temperature extremes are projected to increase, with climate warming increasing duration, frequency, and intensity of heat waves compared to historic climate . The probability of heat waves varies regionally, with some locations expected to have a greater probability of increase in the number of extremes, and in either daytime or nighttime heat waves. Extreme temperature events and increasing Tmax will further intensify the impacts of drought . Although it is projected that there will be fewer total wet days and a decrease in the number of ARs globally, these wet events will likely increase in width and length by 25%, in addition to intensity . With a Mediterranean climate that is uniquely balanced between both mid-latitude storms and expanding subtropical zones, projections for California’s precipitation regime show more uncertainty and variability compared to most other Mediterranean climates around the world. While models consistently project future drying over Mediteranean climates globally, projections for California diverge from these trends, becoming wetter in winter aggregate and experiencing increases in mean precipitation . As a result, the region will likely experience wetter winters yet longer, dryer warm seasons, contributing to increased year-to-year variability. With intensified extreme precipitation events, climate models indicate that the variable character of Southern California’s precipitation will continue to increase .It is projected that precipitation will increase during the region’s concentrated wet winter season, while decreasing in both autumn and spring . Warmer temperatures are causing winter precipitation to fall in the form of rain rather than snow, meaning that the snow pack that acts as a natural reservoir for the state’s water supply will be diminished . As less precipitation is stored in these snow pack reservoirs, compounded with warming temperatures, the state is experiencing earlier springtime snow melt . These projected changes in snow pack, precipitation and springtime snow melt will continue to challenge many regions of California, defining the state’s current and future water resources . Although local snow pack is not significant, loss of snow pack in the state overall will negatively impact the imported water supplies that San Diego relies upon.Coastal low clouds and fog that migrate along the West Coast fluctuate on annual and decadal scales, as a response to a combination of naturally occurring climate and weather patterns .

Are Plastic Pots Bad For Plants

Plastic pots are commonly used for gardening due to their affordability, lightweight nature, and durability. However, there are some considerations to keep in mind when using plastic blueberry pots for plants:

Advantages of Plastic Pots:

Durability: Plastic pots are long-lasting and can withstand outdoor conditions without deteriorating as quickly as some other materials.
Lightweight: Plastic pots are lightweight, making them easy to move around, especially for larger plants.
Cost-Effective: Plastic pots are generally more affordable than pots made from other materials like ceramic or terracotta.
Moisture Retention: Plastic pots tend to retain moisture better than some other materials, which can be beneficial for plants that prefer consistent moisture levels.
Variety: Plastic pots come in a wide range of sizes, shapes, and colors, allowing you to choose containers that suit your aesthetic preferences and the needs of your plants.

Considerations:

Aeration: Plastic pots may not provide as much breathability for plant roots compared to porous materials like terracotta. This can potentially lead to waterlogged soil and root rot if proper drainage is not maintained.
Heat Absorption: Dark-colored plastic pots can absorb and retain heat, which might raise the temperature of the root zone and impact plant health, especially in hot climates.
Degradation: Over time, plastic pots can become brittle and fade due to exposure to sunlight, which may reduce their overall lifespan and appearance.
Root Circulation: Some plastic pots have smooth sides that can lead to root circling, where roots grow in circles around the interior of the pot. This can negatively affect plant health in the long term.
Environmental Impact: Many plastic pots are made from petroleum-based plastics, which have environmental implications due to their production, use, and disposal.

Tips for Using Plastic Pots Effectively:

Choose Proper Drainage: Ensure that your plastic pots have adequate drainage holes to prevent waterlogging and root rot. Elevating pots slightly can also improve drainage.
Monitor Root Health: Regularly check the roots of your plants for signs of circling or compacted growth. If necessary, repot plants to prevent root-bound issues.
Consider Light Color: If you’re concerned about heat absorption, choose lighter-colored plastic pots that reflect more sunlight and heat.
Use Saucers: Place plastic pots on saucers to catch excess water and prevent staining of surfaces. This can also help maintain proper moisture levels.
Recycling: Look for pots made from recycled plastic or consider recycling your old pots to reduce their environmental impact.

In summary, best indoor plant pots can be suitable for many plants, especially when proper care is taken to address potential issues like drainage and aeration. However, it’s also important to consider the needs of your plants, your local climate, and the environmental impact of using plastic materials. If you’re concerned about these factors, you might explore alternative pot materials like terracotta, fabric, or biodegradable options.

Several mechanisms can cause the formation of a water layer

A ‘water layer’ in the field of ISE research refers to a small water layer that can form between the conductor and transducer. This water layer then acts as an unintentional electrolyte reservoir that re-equilibrates with any change in the bulk sample composition.If the ISM and transducer layer do not have good contact with the subsequent layers and do not form a hydrophobic seal, then it is possible for the bulk solution to ‘fill in’ the space by capillary force, not unlike water soaking into a napkin or paper towel. However, if there is a good seal in different layers, it is still possible for a water layer to form. For example, if the micro-structure of the ISM contains ‘pinholes’ , water can likewise transport through these channels to the layers below. Pinholes can be avoided by careful deposition techniques or by making thicker ISM layers. For the latter, the likelihood of forming a pinhole penetrating through the entire membrane is inversely proportional to the membrane thickness. Finally, even if there is a hydrophobic seal and there are no pinholes, water will still diffuse through the membrane to some degree, as the diffusion coefficient of a typical PVC membrane is on the order of 10−8 cm2/s. This is why PVC and other hydrophobic polymers are frequently chosen as the polymer matrix – their high level of hydrophobicity and small diffusion coefficients make it so the water diffusion rate through the ISM is negligible. A simple test to determine if a water layer is forming within an ISE was designed by Fibbioli et al.and is now widely used within the field of polymeric ISE research.

As it has come to be known,draining pot the’ water layer test’ is a relatively simple three-part potentiometric measurement. First, the ISE is conditioned in a concentrated solution of its primary analyte. Then, the electrodes are moved to a concentrated solution of a known interfering analyte. Finally, the electrodes are placed back in the concentrated solution of the primary analyte. The electrode potential is continuously recorded against a commercial Ag/AgCl RE following each exposure to the different solutions. The duration that the electrodes need to be soaked in each solution depends on the thickness of the membranes and the ISE response. Each exposure lasts several hours, and some experiments lasting up to 45 hours have been reported. A schematic describing the water layer test for a nitrate ISE is shown in Figure 4.14. Figure 4.15 shows the water layer test performed on the nitrate ISE. In this water layer test, 100 mM NaNO3 was used as the primary solution, and 100 mM NaCl was the interfering solution. First, the ISE was conditioned in 100 mM NaNO3 until it was stable. The final hour of stable output in NaNO3 is shown, followed by two hours in the interfering solution, and returning to NaNO3 for 24 hours. The potential shows some drift during both the NaCl step and the NaNO3 return, which could indicate the presence of a water layer on the electrode’s surface, which is not unexpected for this type of coated-wire electrode. However, the electrode’s stability is on par with values reported in the literature, which involved specific modifications for stability. The difference between the potential immediately before and the potential immediately after the NaCl step is 15 mV, the same as found by Chen et. al. for electrodes using gold nanoparticles and Polypyrrole to improve stability. Another technique for investigating the stability of an ISE is current-reversal chronopotentiometry. Recall that in Equation 4.10, potential drift is inversely proportional to the capacitance of the ISE.

Current-reversal chronopotentiometry is a technique that allows one to find the capacitance of an ISE. Current-reversal chronopotentiometry is a three-electrode electrode technique with the ISE as the working electrode , a commercial Ag/AgCl electrode as the RE, and a glassy carbon electrode as the counter electrode . The WE is polarized with a few nanoamps of current while the electrode potential is recorded. Rearranging Equation4.10 allows one to solve for the capacitance from the rate of potential change and the current input. After a short period of time, the current flow is reversed, and the bulk resistance of the electrode can be calculated from the ohmic drop when the current is reversed by rearrangement of Equation 4.9. A nitrate ISEs was configured into the three-electrode system described above and submerged in 100 mM NaNO3. A +1 nA current was applied for 60s, at which point the current was reversed to -1 nA for another 60s. The potential is plotted over time in Figure 4.16. EIS is an electrochemical technique that provides in-depth information about the dielectric properties of solid-state ISE sensors. EIS can also identify water layers, pockets of water in membrane pores, and pinholes. Finally, EIS characterizes the contact resistance of the boundaries between layers, which should be minimized to ensure a hydrophobic seal and reduce the ISE impedance. The nitrate ISEs were configured in a three-electrode system, with the ISE as the WE, a commercial Ag/AgCl electrode as the RE, and a glassy carbon electrode as the CE. The three electrodes were immersed in 100 mM NaNO3 solution and the impedance spectra were recorded in the frequency range of 0.5 Hz – 200 kHz. The Bode plot is shown in Figure 4.17A, and the Nyquist plot is shown in Figure 4.17B. The electrode demonstrated a bulk impedance of 1.72 MΩ. Higher bulk resistance of ISEs with PVC and DBF-based membranes has been previously reported, which could be accounted for by membrane thickness and the lack of a transducer layer in our device.Precision agriculture offers a pathway to increase crop yield while reducing water consumption, carbon footprint, and chemicals leaching into groundwater.

Precision agriculture is the practice of collecting spatial and temporal data in an agricultural field to match the inputs to the site-specific conditions. While industrial agriculture seeks to maximize crop yield, there is also the consideration of maintaining a healthy ecosystem. Fortunately, these are not competing interests; Numerous case studies have demonstrated that adopting precision agriculture techniques increases crop yield while lessening detrimental environmental effects. Consider first the use of irrigation in agriculture, which accounts for approximately 36.7% of the freshwater consumption in the U.S., 65% in China, and77% in New Zealand. Part of why so much water is used in agriculture is, quite simply, because crops need a lot of water to grow. For example, high-production maize crops require 600,000 gallons of water per acre per season – that’s an Olympic swimming pool’s worth of fresh water per acre! Adopting precision agriculture practices – such as variable-rate irrigation – have proven to reduce water consumption by 26.3% . Meanwhile, fixing nitrogen from the air to produce fertilizers is an extraordinarily energy-intensive process and accounts for nearly 2% of the U.S.’s annual CO2 emissions. Crops recover only 30-50% of nitrogen in fertilizers, which means that over half of the nitrogen becomes a potential source of environmental pollution, such as groundwater contamination, eutrophication, acid rain, ammonia redeposition, and greenhouse gases. Fortunately, precision agriculture practices have demonstrated an increase in nitrogen use efficiency, thereby reducing both the production volume of fertilizer as well as the amount that is polluted into the environment. We began this exploration from the ground-up. First, we investigated how many sensors are needed to inform a precision agriculture system. The results of that work informed the design of nitrate sensor nodes to fulfill those specifications,round plastic planters and lab-scale versions of those nodes were fabricated and tested in greenhouse experiments. After these WiFi-enabled nitrate sensor nodes were validated, we replaced the components of the nitrate sensor node with naturally-degradable alternatives to realize a no-maintenance version of the sensor node. The fabrication methods were scalable and low cost, while the sensors were comparable to their non-degradable twins. Such sensors could be widely distributed throughout a landscape to map nitrate movement through the watershed, inform the efficient application of fertilizer, or alert residents to elevated nitrate levels in drinking water.Accurate soil data is crucial information for precision agriculture. In particular, the moisture content and the concentration of various chemical analytes in soil have a significant influence on crop health and yield.

These properties vary considerably over short distances, which begs the question: What spatial density does soil need to be sampled to capture soil variability? Half of the spatial range, referred to hereafter as the ‘half-variogram range’, can be used as a “rule-of-thumb” to account for the spatial dependency of agricultural measurements.Similar to how an agricultural field can be defined in the real world as a geographic area at a location, a digital representation – or ’simulation’ – of an agricultural field can be defined as many discrete pixels, where each pixel’s position corresponds to a geographic coordinate and its size to an area. Here, we briefly discuss three methods of expressing an agricultural field in a digital format. For agricultural fields that a simple geometric shape can approximate – such as a rectangular farm or a central-pivot farm – expressing the farm digitally is trivial. For a rectangular-shaped field, we discretize the space into a grid of uniform pixels with dimensions proportional to the length and width of the physical domain. For a central-pivot field, we bound the field in a square grid of uniform pixels, loop through each pixel in the grid, and add the pixel to a list if that pixel’s coordinates are equal to or less than the field’s radius. This technique is demonstrated in Figure 5.2A for a rectangular-shaped field and in Figure 5.2B for a central-pivot field. When the boundaries of the agricultural field are not regularly shaped, we define the field by a list of consecutive coordinate points that, when piece wise connected by polynomial curves, form an enclosed shape. Here, we adopt a simple ray tracing algorithm to determine whether or not a pixel is inside or outside of this boundary. Given an enclosed boundary and a point in space, if one were to draw an infinite vector in any direction originating from that point, it will intersect the boundary an odd-numbered amount of times if-and-only-if the point is within the enclosed space, which is shown in Figure 5.2C. This holds for all points in space except for points on the boundary, which must be determined explicitly. In this way, we use the coordinates of each pixel as a point to determine if a pixel is inside the boundary and append it to a list. Finally, satellite or drone visible-spectra images of agricultural land are already stored in a digital, pixelized format. Such images and datasets are widely available from Google Earth, NASA Earth Observatory, or the USDA cropland data layer. Computer vision techniques can differentiate the arable land on a field from obstructions and store those pixels in a list. This process is visualized in Figure 5.2D. In all cases, it is essential to note the physical dimensions that a single pixel represents. It should also be noted that because each method requires discretization of the field, the results are approximations whose accuracy increases proportionally to the number of pixels used.The optimal layout of sensors in an agricultural field is achieved when, using the fewest number of sensors possible, all points in the field are statistically represented by the data collected by sensors in that field. For a given sensor, the data collected from that sensor is statistically significant for all points within a radial distance equal to the half-variogram range of that sensor. Thus, if we consider an agricultural field a two-dimensional collection of pixels, we can model sensors as circles with a radius equal to the half-variogram range. Using this definition for placement, our problem is similar to the circle packing problem. Circle packing is a well-researched area in mathematics that has many practical applications. Object packing aims to fit as many of some objects within a domain as possible without any overlap. There are several algorithms that aim to optimize object packing, such as random sequential addition, the Metropolis algorithm, and various particle growth schemes. The limit of packing efficiency for equal-size circles in two dimensions is about 91% for a hexagonal grid.

Most economic models make simple profit maximization assumptions

Management data tend to be sparsely available and representing continuity of plant populations is challenging. Advancing our ability to understand how grasslands are managed – to understand, for example, what species are planted, what inputs are provided, what grazing management is applied – is centrally important for improving our ability to model pasture and range land systems. Planted pastures and native grazing lands both contain a variety of species, some of which are more palatable, nutritious, grazing-resistant, or fire-resilient than others. A more open, data-rich environment could facilitate evaluation of a variety of approaches for representing long-term dynamics, which could address several important grassland management and assessment issues. Managing grass swards to maintain desirable plants is a primary goal of grassland management, but one for which modeling tools have offered limited assistance. Models that represent vegetation dynamics are also desirable for understanding longer-term changes in species that can impact productive capacity, sensitivity to degradation, and carbon dynamics . Year-to-year variability is a key component for understanding potential utility and risk of relying on grassland forage resources. Next generation models that enhance our ability to forecast this risk would mark a substantial and meaningful advance. There is a need for better links between the agricultural modeling communities and ecological researchers studying long-term vegetation dynamics. The primary use of forage resources is for grazing animals, yet most grassland models are only loosely coupled with grazers . Better integration through grazing effects on grasslands, grazer distributions across landscapes,plastic pots plants forage demand/consumption, livestock/wildlife movement, etc., would enhance the ability of models to contribute to important emerging issues.

For example, holistic grazing management, in which several aspects of management vary in response to a variety of different cues from the land and expectations about future conditions, can be impossible to evaluate with current modeling frameworks. A system that integrated user demand into the model development process could lead to implementation of new data-management feedback loops within models. Such interactions between users and producers of information could direct data collection to facilitate model use. Models that better represent grazer-grassland interaction are also crucial for understanding how efficiently livestock use forage resources, what is necessary to sustain wildlife populations, and how much grassland output might be available for other uses .The Intergovernmental Panel on Climate Change has reviewed the existing evidence for how climate change may affect weeds, pests, and diseases . One issue with this evidence base is that there is a clear publication bias towards reports of increased threats – people often do not bother to write up no-effect results. There is a general recognition that we need good models to help tease out different effects that changing weather will have simultaneously on both crops and the organisms that compete with or attack them. There has already been some work applying crop physiology-type models to weeds, and developing more mechanistic models of the effect of temperature on insect pests. There is an opportunity and need for more integrated models that include interactions between organisms, for example between weeds and crops, and between pests and the predators and parasites that attack them. A variety of different approaches are possible, and there is a need for an AGMIP-type approach to help the community decide how best to move forward.Highly contagious diseases of livestock present a major threat to agriculture, both in the developed and developing worlds.

Diseases may be chronic in livestock populations, emerge from wildlife reservoirs, or possibly be introduced deliberately by man as an act of bio-terrorism. Models are required to help understand how a disease will spread, and to help policymakers design optimal interventions. These models must encompass not only the epidemiology of the disease but also how it is affected by agricultural practices and in particular the movement of livestock by farmers. There have been significant recent advances in this area, often building on work on human diseases. For example, it is now possible to take livestock movement data and use it to parameterize an epidemiological model . There are the beginnings of a model comparison movement in human epidemiology; livestock disease epidemiology would also benefit from this approach.There is intense current research activity into novel genetic methods of insect control. Most of this work is currently directed at the insect vectors of human diseases such as malaria, though the same methodology can be applied to insect pests of crops and of course the vectors of livestock diseases. The greatest advantage of these approaches is that they involve self-sustaining interventions that spread naturally through a pest population, although because they are nearly all classified as genetically modified, the regulatory issues surrounding them are complex. Cutting-edge modeling work in this field involves joint population and genetic dynamic models, many of which are explicitly spatial. This topic is likely to be one of the most important and exciting areas of modeling as applied to agriculture over the next few decades.Integrated agricultural technologies, defined as the integration of improved genetics, agronomic input, information technology, sensors, and intelligent machinery, will play a pivotal role in agriculture in the years to come. These innovations will be driven by economic forces, by the need to produce more food with limited land and water for the increasing population, and at the same time by the push to save resources to reduce the environmental impact associated with food production. While these changes are occurring now in the commercial scale industrialized agricultures of the world, many of these technologies have the capability to be adapted to conditions in other parts of the world.

The cell phone now allows farmers in rural areas almost everywhere in the world to have low-cost information about prices, for example. Similarly, it is likely that unmanned aerial vehicles will rapidly be adapted to conditions around the world and used to carry out activities such as monitoring crop growth and pest occurrence, and improve management decisions. In large-scale, capital-intensive agricultural systems, these technologies are rapidly leading to the automation of many production activities, particularly machinery operation and decisions about input application rates. The automation of agriculture began in the mid-nineties, resulting in large amounts of data available to farmers and agribusiness companies. Farm machinery are now often equipped with high precision global positioning system controllers, which allow all activity on the farm to be recorded, geo-referenced, and stored on remote computers: “in the cloud.” All modern tractors collect data on a continuous basis and are equipped with wireless connectivity for data transmission. Harvesters record the yield at a particular location, planters can vary the plant spacing or type of seed by location, and sprayers can adjust quantity and type of fertilizer, fungicide or pesticide by location; all to a granularity of just a few square meters. Yield monitoring can now be linked to unmanned aerial vehicle imagery to produce a prescription map for the farmer to implement. These private data could also provide tremendous benefit to the researcher community, should access be increased. Producers in some regions of the world now have historical crop yield data for their fields at very high resolution. Combined with advanced satellite-based imagery, high-resolution spectral and thermal data obtained from UAVs,plastic nursery pots and weather forecasts, growers have most of the critical inputs required to convert this “big data” into an actionable management plan with equipment that can vary fertilizer and other inputs spatially within a field. Despite these rapid advances in the sophistication and automation of farm equipment, a vital piece of the equation is still lacking: the analysis of the vast amount of newly available data in order to provide the farmer with a map of what action to take where and when. Most variable rate application is currently managed by farmers, using rule-of-thumb and empirical approaches, and not by using a systems approach that accounts for the interaction of soil, crop, management, and weather. Thus much of the power of automation remains unexploited. In order to realize the full potential of more sophisticated equipment, new modeling systems for precision agriculture are needed. These systems could be based on comprehensive predictive crop yield models that combine publicly available data, such as soil type, weather, and prices, along with location-specific data from farmers’ yield maps of their fields, to provide a prescriptive crop management plan at high spatial resolution, as in Fig. 2. This type of system could deliver automated crop simulations, crop management strategy recommendations, process-based variable rate prescriptions, risk assessments, continual in season simulations, integration of in-season crop scouting UAVs flight information, pest management prescriptions and accurate harvest recommendations via simple-to-use apps, websites, or smart phones. In addition to the farm-to-landscape scale analysis represented in Fig. 2, there will be a growing demand for agricultural systems models to simulate and integrate the different components of the agricultural value chain, to meet both policy requirements and corporate sustainability goals . Genetics, agronomic management , weather, soil, information technology and machinery will need to be linked in a system approach to address these informational needs. This is a new frontier for agricultural system modeling that would extend to the broader food system and raise additional data and analytical challenges.

As illustrated in Fig. 2 and discussed in the previous section, various management and production data are becoming available through mobile technologies . An example of this analytical capability is the AgBizLogic™ software developed by several university extension programs, which allows managers to calculate short-term profitability and rates of return on long-term investments . Similar proprietary software tools are being developed and used. These analytical tools could be linked with modules that track or predict environmental outcomes such as soil erosion and net greenhouse gas emissions . Low-bandwidth versions of these tools need to be developed for use in areas where mobile phone technology is a limiting factor. Analytical tools need to be adapted to fit small-holder systems as indicated by the NextGen Use Cases. The flood of data on physical land-use, water availability and use, and yields coming from mobile devices and remote sensing systems suggest that both the biophysical and behavioral aspects of farm production at specific locations can be estimated by sequential learning processes. The use of advances in computational methods such as machine learning and remote sensing data is illustrated by analysis of the impact of the 2009 and 2014 droughts on California agriculture, which demonstrated the advantages of better data .To facilitate the use of models for various locations and systems, and to link to crop and livestock system simulation models, economic models need to be incorporated into modules with standardized inputs and outputs. Various types of economic models are available in the literature, including farm-level optimization models, regional positive quadratic programming models, econometric land-use models, and regional impact assessment models . User needs should dictate which types of models should be used depending on informational needs. Methods and protocols are required to link regional economic models with market equilibrium models . Some progress has been made on this front but much more development is needed to address various aggregation and dis-aggregation issues . Generalization of behavioral assumptions and investigation of their effects on investment and policy analysis is also needed.There is a rich literature on risk modeling which could be incorporated. Recent advances in the expectations formation literature and the behavioral economics literature could be investigated for use in agricultural systems models.The application of different farm improvement methods has explicit winners but also unintended ‘casualties’ and perverse incentives. From a development standpoint, it is essential to understand these dynamics to ensure that appropriate policies are developed to maintain equal opportunities for all sectors of society. For example, in many cases, rich farmers are the ones who adopt technologies early. This factor could potentially disrupt power relationships in markets, thus affecting poorer farmers. In this case it is essential to design alternative options and safety nets for poorer farmers to prevent widening the gap and making them more vulnerable. New models should improve our understanding of these processes, as we move from single farm models to multi-farm and regional models. Methods utilizing population-based data are providing improved capability to represent distributional impacts and vulnerability .Current agricultural system models typically operate at the point/ field scales with an emphasis on vertical fluxes of energy, water, C, N and nutrients between the atmosphere, plant and soil root zone continuum.

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