Category: hydroponic growing

A related anaerobic process is nitrate-dependent iron oxidation; a recent review has highlighted, in the context of this process, how the simultaneous presence of nitrate-reducing and iron-reducing areas can potentially be important to nitrogen cycling.Under anaerobic conditions, iron can also be linked to ammonium oxidation.If reactions that generate N2O are active in any of the above processes, they may be stimulated or suppressed by different forms of iron, such as the two indices examined in this study.The degree of this influence under different conditions will then determine the importance of iron relative to other soil properties.Our treatments consisted of two contrasting values for soil moisture and addition of amendments.This was done in order to explore the importance of iron across a wide range of conditions while at the same time avoiding a cumbersome dataset.It is clear from Figure 1 that the importance of iron can change between the two limits of each treatment variable.For example, between 50 and 100% WHC under ammonium fertilization, iron moves from a position of modest relevance to become the highest-ranked driver.Since our results show the importance of iron only at two distinct values, we do not know how its importance under intermediate conditions changes between the two end values.Even without such intermediate data, the differences between contrasting treatments can aid in understanding the mechanisms at work in generating N2O.In the above example, the importance of iron rises markedly under ammonium fertilization as soil moisture increases from 50 to 100% WHC; FeP surpasses FeA in strength as well.

As mentioned earlier, ammonia is oxidized to hydroxylamine, and this can react with iron to produce N2O.In a wetter soil,dutch buckets solutes are more mobile, which can lead to greater production of hydroxylamine as well as greater contact of hydroxylamine with iron.FeP is also likely to be more soluble than FeA.Any combination of these effects might elevate the importance of iron and change which form is more relevant in explaining the associated N2O data.The overall position of iron among other drivers of N2O emission is determined by both its reactivity and the presence of processes subject to its influence.Ample opportunity for inquiry exists for defining the extent of the relationship between iron and N2O in managed as well as unmanaged ecosystems, and this can provide useful practical and theoretical information.For example, including iron in current models of N2O emission may strengthen their predictive ability.In addition, inasmuch as certain indices of iron can be related to its physical or chemical characteristics, observing the relationship between a given index and N2O production, and how this changes under different conditions, may provide insight into the specific reactions at work.As stated earlier, production of N2O is generally accepted to be a microbial affair, and it is logical to assume that the factors that regulate the activity of N2O-producing microorganisms should be the same factors that regulate N2O production.This is not incorrect, but is perhaps a somewhat restrictive rendering; a more accurate framework might include ‘‘biotic-abiotic reaction sequences’’that generate N2O, such as those outlined above.Indeed, ‘‘the complex interactions that occur between microorganisms and other biotic and abiotic factors’’ have been suggested to be a key part of further understanding greenhouse gas production and improving predictions.Pesticide drift, which is the off-target movement of pesticides, is recognized as a major cause of pesticide exposure affecting people as well as wildlife and the environment.In the United States in 2004, > 1,700 investigations were conducted in 40 states because of drift complaints, and 71% of the incident investigations confirmed that drift arose from pesticide applications to agricultural crops.Pesticide drift has been reported to account for 37–68% of pesticide illnesses among U.S.agricultural workers [California Department of Pesticide Regulation 2008; Calvert et al.2008].Community residents, particularly in agricultural areas, are also at risk of exposure to pesticide drift from nearby fields.

Alarcon et al.reported that 31% of acute pesticide illnesses that occurred at U.S.schools were attributed to drift exposure.The occurrence and extent of pesticide drift are affected by many factors, such as the nature of the pesticide 2010], equipment and application techniques , the amount of pesticides applied, weather , and operator care.Pesticide applicators are required to use necessary preventive measures and to comply with label requirements to minimize pesticide drift.Pesticide regulations such as the Federal Insecticide, Fungicide, and Rodenticide Act and EPA’s Worker Protection Standard require safety measures for minimizing the risk of pesticide exposure , and many states have additional regulations for drift mitigation.Better understanding about the magnitude, trend, and characteristics of pesticide poisoning from drift exposure of agricultural pesticides would assist regulatory authorities with regulatory, enforcement, and education efforts.The purpose of this study was to estimate the magnitude and incidence of acute pesticide poisoning associated with pesticide drift from outdoor agricultural applications in the United States during 1998–2006 and to describe the exposure and illness characteristics of pesticide poisoning cases arising from off-target drift.We also examined factors associated with illness severity and large events that involved five or more cases.Participating surveillance programs identify cases from multiple sources, including health care providers, poison control centers, workers’ compensation claims, and state or local government agencies.They collect information on the pesticide exposure incident through investigation, interview, and medical record review.In California, on some occasions, such as large drift events, active surveillance is undertaken for further case finding by interviewing individuals living or working within the vicinity affected by the off target drift.Although the SENSOR-Pesticides program focuses primarily on occupational pesticide poisoning surveillance, all of the SENSOR-Pesticides state programs except California collect data on both occupational and nonoccupational cases.In California, PISP captures both occupational and nonoccupational cases.SENSOR Pesticides and PISP classify cases based on the strength of evidence for pesticide exposure, health effects, and the known toxicology of the pesticide and use slightly different criteria for case classification categories.This study restricted the analyses to cases classified as definite, probable, possible, or suspicious by SENSOR-Pesticides and definite, probable, or possible by PISP.

We also performed analyses restricted to definite and probable cases only.Because the findings from these restricted analyses were similar to those that included all four classification categories , only the findings that used the four classification categories are reported here.In this study, a drift case was defined as acute health effects in a person exposed to pesticide drift from an outdoor agricultural application.Drift exposure included any of the following pesticide exposures outside their intended area of application: a) spray, mist, fumes, or odor during application; b) volatilization, odor from a previously treated field, or migration of contaminated dust; and c) residue left by offsite movement.Our drift definition is broader than U.S.EPA’s “spray or dust drift” definition, which excludes post application drift caused by erosion, migration, volatility, or windblown soil particles.A drift event was defined as an incident where one or more drift cases experienced drift exposure from a particular source.Both occupational and nonoccupational cases were included.An occupational case was defined as an individual exposed while at work.Among occupational cases, agricultural workers were identified using 1990 and 2002 Census Industry Codes : 1990 CICs, 010, 011, 030; 2002 CICs, 0170, 0180, 0290.Figure 1 presents the process of case selection.We selected cases if exposed to pesticides applied for agricultural use including farm, nursery, or animal production, and excluded cases exposed by ingestion, direct spray, spill, or other direct exposure.We then manually reviewed all case reports and excluded persons exposed to pesticides used for indoor applications , persons exposed within a treated area , and persons exposed to pesticides being mixed, loaded, or transported.Drift cases therefore represented the remaining 9% and 27% of all pesticide illness cases identified by the SENSOR-Pesticides and PISP, respectively.We also searched for duplicates from the two programs identifying California cases.Because personal identifiers were unavailable, date of exposure, age, sex, active ingredients, and county were used for comparison.A total of 60 events and 171 cases were identified by both California programs.These were counted only once and were included only in the PISP total.Drift events and cases were analyzed by the following variables: state, year, and month of exposure, age, sex, location of exposure, health effects, illness severity,grow bucket pesticide functional and chemical class, active ingredient, target of application, application equipment, detection of violations, and factors contributing to the drift incident.U.S.EPA toxicity categories ranging from toxicity I to IV were assigned to each product.

Cases exposed to multiple products were assigned to the toxicity category of the most toxic pesticide they were exposed to.Illness severity was categorized into low, moderate, and high using criteria developed by the SENSOR Pesticides program.Low severity refers to mild illnesses that generally resolve without treatment.Moderate severity refers to illnesses that are usually systemic and require medical treatment.High severity refers to life-threatening or serious health effects that may result in permanent impairment or disability.Contributing factors were retrospectively coded with available narrative descriptions.One NIOSH researcher initially coded contributing factors for all cases.Next, for SENSOR-Pesticides cases, state health department staff reviewed the codes and edited them as necessary.Any discrepancies were resolved by a second NIOSH researcher.For PISP cases, relatively detailed narrative descriptions were available for all incidents.These narratives summarize investigation reports provided by county agriculture commissioners, who investigate all suspected pesticide poisoning cases reported in their county.After initial coding, the two NIOSH researchers discussed those narratives that lacked clarity to reach consensus.Data analysis was performed with SAS software.Descriptive statistics were used to characterize drift events and cases.Incidence rates were calculated by geographic region, year, sex, and age group.The numerator represented the total number of respective cases in 1998–2006.Denominators were generated using the Current Population Survey micro-data files for the relevant years.For total and nonoccupational rates, the denominators were calculated by summing the annual average population estimates.A nonoccupational rate for agriculture-intensive areas was calculated by selecting the five counties in California where the largest amounts of pesticides were applied in 2008.For occupational rates, the denominators were calculated by summing the annual employment estimates including both “employed at work” and “employed but absent.” The denominator for agricultural workers was obtained using the same 1990 and 2002 CICs used to define agricultural worker cases.Moreover, in California, where data on pesticide use are available, incidence was calculated per number of agricultural applications and amount of pesticide active ingredient applied.Incidence trend over time was examined by fitting a Poisson regression model of rate on year and deriving the regression coefficient and its 95% confidence interval.Drift events were dichotomized by the size of events into small events involving < 5 cases and large events involving ≥ 5 cases.This cut point was based on one of the criteria used by the CDPR to prioritize event investigations.Illness severity was dichotomized as low and moderate/high.Simple and multi-variable logistic regressions were performed.Odds ratios and 95% CIs were calculated.To our knowledge, this is the first comprehensive report of drift-related pesticide poisoning in the United States.We identified 643 events involving 2,945 illness cases associated with pesticide drift from outdoor agricultural applications during 1998–2006.Pesticide drift included pesticide spray, mist, fume, contaminated dust, volatiles, and odor that moved away from the application site during or after the application.

Although the incidence for cases involved in small drift events tended to decrease over time, the overall incidence maintained a consistent pattern chiefly driven by large drift events.Large drift events were commonly associated with soil fumigations.Occupational exposure.Occupational pesticide poisoning is estimated at 12–21 per million U.S.workers per year.Compared with those estimates, our estimated incidence of 2.89 per million worker-years suggests that 14–24% of occupational pesticide poisoning may be attributed to off-target drift from agricultural applications.Our study included pesticide drift from outdoor applications only and excluded workers exposed within the application area.Our findings show that the risk of illness resulting from drift exposure is largely borne by agricultural workers, and the incidence was 145 times greater than that for all other workers.Current regulations require agricultural employers to protect workers from exposure to agricultural pesticides, and pesticide product labels instruct applicators to avoid allowing contact with humans directly or through drift.Our study found that the incidence of drift-related pesticide poisoning was higher among female and younger agricultural workers and in western states.These groups were previously found to have a higher incidence of pesticide poisoning.It is not known why the incidence is higher among female and younger agricultural workers, but hypotheses include that these groups are at greater risk of exposure, that they are more susceptible to pesticide toxicity, or that they are more likely to report exposure and illness or seek medical attention.However, we did not observe consistent patterns among workers in other occupations.This finding requires further research to identify the explanation.

Gender harassment was reported by 30% of female crew members, of which 9% also reported unwanted sexual attention and 1% reported sexual coercion. The relative prevalence of these SH categories mirrored the pattern in prior California studies, although the rates of workers reporting SH in our study were considerably lower than the rates reported in those studies . This may be explained by regional and crop-specific differences. For example, working conditions in Napa vineyards are generally considered better than those in other agricultural sectors, with workers offered above average wages and benefits . Additionally, we considered harassment only at a worker’s current company, not throughout the worker’s overall agricultural or working career, which could have resulted in a lower reporting rate compared to previous studies. The low rates of unwanted sexual attention and sexual coercion in our study were far lower than those found in other studies. Such low rates reflect well on the Napa industry, but they may also, despite the anonymity of responses, indicate a reluctance among women to admit severe harassment when participating alongside co-workers and in a study coordinated as we did this one. The small number of women reporting unwanted sexual attention or sexual coercion meant we were not able to consider an analysis of the relationship between the severity of SH with the other variables measured. Instead, we focused on two types of group comparison based on the presence or absence of SH: women reporting any type of harassment versus women reporting no harassment, and crews where SH was reported versus crews where SH was absent . We analyzed average scores or counts except for crew gender ratio, SH awareness training and relatives in crew. For these three variables, we classified female participants into additional groups based on the percentage of females in a crew, the percentage of crew members that were SH-trained and the presence or absence of relatives in a crew. Thus, female participants were assigned either to a low-female group or a high-female group and either to a low-SH-trained group or to a high-SH-trained group , using a median split.

Descriptive data for harassed and non-harassed female participants show that harassed women in our study differed on two antecedent variables. As in other industries ,what is a vertical farm harassed women were significantly younger than non-harassed women; women under 40 years of age accounted for two-thirds of reported harassment cases in our study. Second, 89% of women reporting the more severe categories of harassment were seasonal employees. More female seasonal workers than permanent workers reported gender harassment, although this relationship was not statistically significant . Harassed and non-harassed women did not differ significantly in the presence of relatives on their crews, the duration of their employment, crew size, crew gender ratio or the number of members in their crew that had received SH awareness training . Harassed women had significantly higher turnover intentions and lower overall job satisfaction compared to non-harassed women, supporting prior research on the negative impact of SH on morale and worker productivity. We compared descriptive data for SH+ and SH− crews on hostile sexism and male work outcomes. Mean scores for hostile sexism were significantly higher in SH+ crews compared to mean scores in SH− crews, supporting the theory that sexist attitudes contribute to a climate of SH tolerance . This complemented our finding of a higher incidence of gender harassment over other types of SH. The hostile sexism questionnaire can thus be considered an attitudinal measure of the behavioral gender harassment component of the SEQ, as hostile sexist attitudes appeared to be enacted as behavioral harassment towards women workers. Turnover intentions for male members of SH+ were significantly higher and job satisfaction was lower than they were for males in SH− crews. We could not determine whether dissatisfied male workers were more likely to perpetrate SH or if witnessing SH adversely affected male workers; however, the latter has previously been concluded in other research .

We identified several variables associated with the presence of SH in agricultural work crews, and we demonstrated that SH is associated with a decline in work outcomes. The type of design we employed in this study cannot verify causation between variables, only association. However, these statistical associations, together with consideration of the literature on SH in other industries, provides grounds for healthy speculation as to how agricultural companies might address SH among their workers.The oldest woman reporting SH was 47; most harassed women in this sample were 40 years or younger. Despite the lack of statistical differences in SH incidence between seasonal and permanent female workers, the severe forms of SH were overwhelmingly reported by seasonal workers. While recognizing that all workers are at risk of SH, companies should therefore be especially vigilant of the risk to young and seasonal female workers.Changing the structure of work crews is unlikely to reduce SH. In our study, harassed women worked in crews that were large and small, with or without relatives, and with considerable variation in gender ratio. Harassed women were just as likely to be working on crews with a high percentage of females as on crews with a low percentage of females . This was unexpected, as meta-analyses have demonstrated gender ratios to be a significant predictor of SH . However, the gender ratio effect may be small, and as SH occurs in a range of organizational settings , the characteristics of SH perpetrators may be more important. For example, perpetrators in male dominated workplaces tend to be co-workers, whereas perpetrators in female-dominated workplaces are more likely to be supervisors . The questionnaire we used in our study did not ask women about the perpetrators, but the unimportance of crew gender ratio indicates the possibility that SH may have originated not only from inside the crews but from outside, such as from supervisors or other company employees. Our presumption that the crew level is the most relevant company unit for SH was too optimistic. We often observed multiple crews working in the same vineyard, and they often mixed during work breaks; SH could therefore have originated from other crews, especially as the SH reported in our study was primarily verbal and gestural in nature.

Crew membership was also probably more fluid than our study design conceived. Women were asked about SH only during their current employment, but these women did not necessarily work continually in the same crew configuration. If gender ratio is an important antecedent of SH in agriculture, we predict it will be at the level of the company rather than at the level of the work team.Our results, as supported by the literature , indicate that an improvement in organizational climate is a more effective method for tackling SH than a restructuring of work crews. The hostile sexist attitude of both men and women in a crew was significantly associated with the presence of SH. Companies can expect to reduce SH by changing or neutralizing these attitudes. However, shifting these attitudes may be difficult to accomplish, as indicated by our finding that previous SH awareness training was not related to a decrease in reported SH. Similar poor efficacy of SH awareness training has been reported in prior research , suggesting that improvements are needed to the structure and administration of awareness training for agricultural workers. Unless these changes are made, other organizational climate variables, such as the internal management of complaints and the overall social climate of a company , are more likely to be effective in reducing SH. There is still value in conducting training, as it has been shown to make women more likely to report SH and it makes workers more aware of what is acceptable behavior . Since we did not collect details on which training programs the workers received, we cannot comment on the efficacy of one training program over another. Harassed females reported lower job satisfaction and higher intention to quit their jobs, illustrating that SH is likely resulting in companies losing female workers and experiencing other negative effects associated with poor worker satisfaction. The same reduced outcomes were reported by male workers in crews where harassment was occurring, suggesting that SH may be impacting not only the targets but also the co-workers. Dissatisfaction among men as a result of SH thus also has the potential to negatively affect company performance. The current study demonstrated that workplace sexual harassment of female vineyard workers affects the well being and retention of all workers in an agricultural sector where there is a paucity of quantitative data on the issue. Furthermore, this study illustrated that female workers in entry positions to the industry are most at risk of SH, illustrating that SH is a barrier for women seeking to enter the agricultural workforce. Thus, SH has the potential to significantly affect the stability of the labor pool in a time of labor shortage and to incur economic costs not only for workers but also for agricultural organizations seeking to train and retain stable work crews. Incidence of SH in our study was lower than that previously reported for farm workers, but our results should be treated with some caution; there may have been some under reporting due to our method of data collection and our relatively small sample size. This study also measured SH in one region and one crop only, and incidence rates may not generalize to other agricultural regions and sectors. Workplace policies and practices that reduce or eliminate hostile sexist attitudes appear to have the most promise for reducing SH in agriculture. However,vertical strawberries vertical system accomplishing these goals with limited resources and within a company’s traditional organizational structure may be challenging.

Future studies may seek to consider in more detail how organizational climate can be effectively addressed in the agricultural sector, the effectiveness of different SH awareness programs and the characteristics of perpetrators of SH towards women. In response to a shift toward specialization and mechanization during the 20th century, there has been momentum on the part of a vocal contingent of consumers, producers, researchers, and policy makers who call for a transition toward a new model of agriculture. This model employs fewer synthetic inputs, incorporates practices which enhance biodiversity and environmental services at local, regional, and global scales, and takes into account the social implications of production practices, market dynamics, and product mixes. Within this vision, diversified farming systems have emerged as a model that incorporates functional biodiversity at multiple temporal and spatial scales to maintain ecosystem services critical to agricultural production. This essay’s aim is to provide an economists’ perspective on the factors which make diversified farming systems economically attractive, or not-so-attractive, to farmers, and to discuss the potential for and roadblocks to widespread adoption. The essay focuses on how a range of existing and emerging factors drive profitability and adoption of DFS, and suggests that, in order for DFS to thrive, a number of structural changes are needed. These include: 1) public and private investment in the development of low-cost, practical technologies that reduce the costs of production in DFS, 2) support for and coordination of evolving markets for ecosystem services and products from DFS and 3) the elimination of subsidies and crop insurance programs that perpetuate the unsustainable production of staple crops. This work suggests that subsidies and funding be directed, instead, toward points 1) and 2), as well as toward incentives for consumption of nutritious food. Each year, more than 50,000 people in the U.S. die from hospital-acquired bacterial infections, millions experience episodes of food borne illness, and reported cases of “superbugs” such as Methicillin-resistant Staphylococcus aureusand vancomyc in-resistant enterococci are on the rise. For those who acquire a resistant infection in their food, in their community, or in a hospital, resistance is associated with a longer duration of treatment, the use of more potent antibiotics, and longer hospital stays. This, in turn, means increased health care costs and costs to society due to antibiotic-resistant infections. Antibiotic resistance is contributing to the scope and severity of this health care crisis, and at least some of the responsibility for antibiotic resistance sits on the shoulders of industrial livestock production. In livestock operations, low or sub-therapeutic doses of antibiotics are used to promote growth, in addition to their use to prevent and control disease. Today, more antibiotics are used in livestock production and the production of milk and eggs than in humans. While the use of sub-therapeutic doses of antibiotics is regulated less stringently in the United States than in the European Union, there is movement toward and potential for such regulation.

The implication is that increases in agricultural production have to be met through increases in agricultural productivity, and less through expansion of cultivated area. Another worsening factor is the climate change and global warming. Some studies predict that global warming will significantly and negatively affect African agriculture. They also indicate that the use of irrigation reduces the harmful impact of global warming. In addition, irrigation use is a catalyst of improved technology adoption, which will have a substantial impact on food security.The author’s understanding of food security is informed by Sen’s entitlement theory. Farmer’s access to food can be seized either through the output markets or through increases in productivity levels and improvements in food storage. As elicited by the “sell low, buy high” puzzle, the mark-up is usually very high and a significant number of households in rural Mozambique may not afford to purchase food during the lean season. Therefore, it becomes crucial to enhance both agricultural productivity and farmer’s ability to store food. Selective mechanization, improved storage, and other improved agricultural technologies play an essential role in ensuring farmers’ food entitlements. Previous attempts to mechanize the agricultural sector in the post-colonial period have failed, one of the reasons being the 16-year civil war that started a year after the independence in 1975. Moreover, the government established tractor-hire schemes had serious planning,vertical grow shelf management, and training problems, denting the image of agricultural mechanization in general. Agricultural mechanization is also mistakenly perceived as tractor mechanization.

This includes the use of tractors, animal-powered and human-powered implements and tools , as well as irrigation systems, food processing and related technologies and equipment. Although not addressed in this paper, the use of jab planters has been shown to significantly reduce labor requirements. Information on the economic impact of selected improved agricultural technologies is needed to target interventions efficiently and equitably, and to justify investment in such technologies.This paper assesses the impact of improved agricultural technologies by constructing a counterfactual comparison group. In this setting, a comparison of the outcome variable is made between farmers using a given technology and their counterparts with similar observable co-variates .The use of tractor mechanization is significantly correlated with road infrastructure. The distance to the nearest tarred road is three times higher among households who did not use tractors, relative to their counterparts. Remarkably, among the 2 percent of the population that used a tractor, 49 percent accrues to Maputo province, and 32 percent to Gaza province, both located in the south, a region of relatively lower agricultural potential, but of better road infrastructure. The remaining 19 percent are distributed across the other 8 provinces, which includes agro-ecological zones of higher agricultural potential, but relatively poorer road infrastructure. Unsurprisingly, adoption rates rise with increases in both landholding size and livestock flocks for all four improved technologies. Households with larger landholdings will potentially have higher production and thus feel compelled to invest in improved granaries. The use of animal traction or tractor mechanization is also cost-effective in larger fields. Additionally, the adoption of animal traction and tractor mechanization require some initial investment, and asset endowment is positively and significantly correlated with household welfare.

With regard to access to credit, the difference between treated and untreated households was only significant for the adoption of tractor mechanization, and marginally significant for the use of animal traction. This result, however, is an artifact of a low data variation as not many households could access the emerging rural credit market. Furthermore, a tractor can be used as collateral, a bottleneck for many rural households in accessing to the credit market. Membership to farmers’ association is also significantly correlated with the use of improved agricultural technologies. The number of farmers using tractor mechanization is three times higher among members of an association. Similarly, there are twice as many farmers using improved seeds among members of a farmers’ association.Figures 1A through 4A show the distribution of propensity scores for all four technologies. Treated and untreated households overlap very well, suggesting that the overlap assumption is plausible. Additionally, the assessment of the overlap assumption was complemented by the analysis of normalized differences. The results are presented in Table 2, and they show that normalized differences are in general smaller than 0.25 . Exceptions are the variables on head’s age and tropical livestock units. However, this outcome did not affect the estimation results because these two variables were dropped from the stepwise logit model due to their low explanatory power. The results on the stepwise logit model are not reported to save space, but are available from the author upon request.Table 4 presents the estimation results of the impact of selected improved agricultural technologies, contrasting the results obtained through three econometric approaches. With the exception of animal traction, the impact of improved agricultural technologies is consistently positive and significantly different zero. The impact is greater for tractor mechanization, followed by the use of improved seeds, and finally the use of improved granaries. Farmers that used animal traction and experienced losses in 2004/05 agricultural season may be enticed to abandon such technology, especially if they rented the animals and the implements. This is probably one of the reasons why “adoption rates” of improved agricultural technologies are usually very low: some farmers abandon the technology after some unsuccessful adoption attempts. Policies to sustain adoption of improved agricultural technologies should be put in place. Irrigation investments fall in that category.The significance of improved granaries underscores the relevance of post-harvest losses, and reducing these losses potentially results in higher household income in light of opportunities for inter-temporal price arbitrage; and improved food entitlements and farmer’s nutritional status. The author speculates that the benefits from an improved granary might outstrip by far its construction costs, considering that it will be used for more than a year.

The impact of improved seeds on maize is about 2 000 Meticais/ha, and 5 180 Meticais/ha for tractor mechanization . The estimates of the impact can also be regarded as shadow prices. Specifically, during the 2004/05 agricultural season, the use of tractor mechanization would be profitable for the farmer whenever the market cost of hiring a tractor was below $212/ha. Likewise, the market price of improved maize seeds required to sow 1 hectare of maize should be lower than $80. Taking into account that mean household income in 2004/05 was about $137 per adult equivalent , and that less than 5 percent had access to credit, understanding why adoption of improved technologies is extremely low becomes trivial. Even if improved agricultural technologies were riskless, a bulk of farmers would not be financially capable of investing in such technologies, much less irrigation. There is certainly an ample scope to enhance the impact of improved seeds and tractor mechanization, considering that less than 5 percent use irrigation or inorganic fertilizers, and about half of the tractors used in Mozambique are located in Maputo province, and more than 3/4 of all tractors are located in the south. If the Mozambican government wants to achieve the much talked-about green revolution, then huge investments on basic infrastructure and irrigation may pave the way for higher adoption rates and profitability of improved agricultural technologies. The bad news is that climate change and global warming is a translucent reality, potentially with severe implications to African agriculture. In the Mozambican agriculture context, the implication is that any effort to foster adoption of animal traction, improved seeds, tractors, and other improved technologies should be accompanied by investments on irrigation or water conservation technologies. Furthermore, drought-tolerant improved seeds will also significantly increase both agricultural production and productivity amidst low irrigation use and recurrent drought spells across the country.Hundreds of reports and articles begin with a variation on the same apocalyptic exhortation: The combination of population growth, food price volatility,vertical hydroponic and climate change demands a new agricultural revolution to expand and secure the global food supply. The bio-technologies frst deployed in the Green Revolution are still being constantly improved; food prices, however, stay stubbornly high and many fear a yield plateau. The new revolution, they argue, is digital technology. In a recent article about the use of artifcial intelligence in agriculture, for example, Wired gushed about “an explosion in advanced agricultural technology, which Goldman Sachs predicts will raise crop yields 70 percent by 2050” . Goldman, for their part, estimate that digital agricultural technologies will become a $240 billion market by 2050 . X, Google’s “moonshot” venture, recently hailed the arrival of “the era of computational agriculture” . Traditional agribusinesses have found themselves competing with Silicon Valley giants, venture capitalists, scrappy startups, intergovernmental organizations, non-governmental organizations , and research institutions to develop and market a dizzying array of new technologies to feed “the next two billion” and save the world. “Digital agriculture” is a heterogeneous suite of information-rich, computationally-complex, and often capital-intensive methods for improving the efficiency of agricultural land and the profit margins of sectoral actors.

Digital technologies have come to play a role in every stage of the agricultural cycle under capitalism, from input management to marketing produce, pricing commodities futures to pest control. However, while it is true that these technologies increase efficiency, we contest the notion that they will provide a long-term solution to the looming crises of the global food system. For what the narrative of an agricultural techno-revolution elides is how the methods of industrialized food production create these challenges in the frst place. We interpret the rise of digital technologies in agriculture as the continuation of a process dating back to the Green Revolution, namely, to reconfigure agrarian life in a manner amenable to increased profits, especially for actors further up the value chain. For the proponents of digital agriculture, the transition is between two technologically-paved pathways to profit: innovations in high dimensional computing supersede innovations in breeding. A purely technological perspective is insufficient and depoliticizes analyses of far-reaching changes to agricultural production, changes which have an effect on the rest of the capitalist economy . Nevertheless, this has not stopped digital agriculture’s boosters from frequently claiming that it heralds a “fourth agricultural revolution.”1 However, digital agriculture has received limited critical attention from social scientists. The vast majority of critical work on the ascendancy of global technology mega-firms and new information-centric accumulation strategies looks at their effects in non-agrarian industrial and service sectors. However, the generation of profits in these sectors depends in part on keeping inputs for production and reproduction— like food—artifcially cheap . By perpetuating an unsustainable regime of cheap food, digital agriculture technologies support the continued expansion of an equally unsustainable global urban system.We argue that the rise of digital agriculture is emblematic of an intensifying relationship between zones of agrarian production and extraction on the one hand, and zones of agglomeration, industrial production, and service provision on the other. A body of neo-Lefebvrian scholarship describes these apparently distinct zones as co-constitutive, entangled in a dialectic of extended and concentrated urbanization . In this framework, the growth imperative of capitalism requires the transformation of vast landscapes beyond the ‘city’ to increase extraction and agricultural output, the product of which is drawn back inward to fuel growth. In this reading, the socio-metabolic process of urbanization is increasingly generalized, to the point that some have argued for thinking of contemporary urbanization as a ‘planetary’ process. With this in mind, this article interrogates the political economy of digital agriculture and reinterprets the digitalization of the food system through the lens of extended–concentrated urbanization. We begin by introducing digital agriculture and the limited social scientific literature on the topic. Next, we critique the mainstream rhetoric surrounding digital agriculture, which makes a Malthusian argument for the need to feed a burgeoning global population in the face of climate change. Then, beginning from the observation that the crucial role of information is under-analyzed in the extended–concentrated urbanization framework, we build a theoretical argument for how digital agriculture challenges the urban–rural binarism. We locate the framework’s origins as a reaction to earlier threads of globalization theory, which emphasized the supposedly immaterial nature and deterritorializing effects of information and communications technologies .

Apprehensions by the U.S. border patrols dropped from 876,803 in 2007 to 556,032 in 2009. Because immigrants often send money home, we can use remittances from the United States to Mexico to infer whether the number of immigrants changed substantially during a recession. Figure 2 shows quarterly remittances to Mexico in millions of U.S. dollars as reported by Banco de México . The figure shows that remittances increased during the relatively mild 2001 recession but decreased substantially during the 2008–2009 Great Recession. These data again support the view that the number of Mexican immigrants to the United States fell during the Great Recession but not during the previous, milder recession. Moreover, Warren and Warren estimated that the net change of undocumented immigrants was negative during the Great Recession, which was related to a sharp decrease of new undocumented immigrants. The United States Department of Agriculture, Economic Research Service estimated number of full- and part-time agricultural workers fell from 1.032 million in 2007 to 1.003 million in 2008 and 1.020 million in 2009, before rising to 1.053 million in 2010.5 That is, the number of workers in 2008 was 3% to 5% lower than in the years before and after the Great Recession. Presumably the share of workers dropped by even more in seasonal agriculture, which employs most of the undocumented workers.Our agricultural workers data comes from the National Agricultural Workers Survey.The NAWS is a national, random sample of hired seasonal agricultural employees, who work primarily in seasonal crops.The NAWS is an employer-based survey. That is, it samples worksites rather than residences to overcome the difficulty of reaching migrant farm workers in unconventional living quarters.

These employers are chosen randomly within the U.S. Department of Agriculture’s 12 agricultural regions .Surveyors randomly select 2,500 employees of these growers to obtain a nationally representative sample of crop workers. Surveyors interview the more than 2,500 crop workers outside of work hours at their homes or at other locations selected by the respondent. The NAWS has a long,vertical growers visible history within farming communities, and the survey design incorporates questions aimed at data validation about legal status. Respondents receive a pledge of confidentiality and a nominal financial incentive for participation. As a result, only one to two percent of workers in the overall sample refuse to answer the legal status questions. The NAWS contains extensive information about a worker’s compensation, hours worked, and demographic characteristics such as legal status, education, family size and composition, and workers’ migration decisions. We dropped workers from the sample who were missing any relevant variable, 23% of the original survey sample. The NAWS is conducted in three cycles each year year to match the seasonal fluctuations in the agricultural workforce. Unfortunately, the public-use data, which we use, suppresses information about the cycle and aggregates the 12 regions into 6 regions. As a result, our data set consists of repeated annual cross sections of workers from 1989 through 2012. Column 1 of Table 1 presents national summary statistics for the variables used in our empirical analysis. Columns 2 and 3 provide data for California and for the rest of the country, because 37% of the sample works in California. Compared to workers in the rest of the country, Californian workers tend to have less education; have more farm experience; are more likely to be non-native, Hispanics; and are more likely to work in fruit and nut crops and less likely to work in horticulture.

After analyzing the effects of recessions on agricultural workers, we replicate the analysis for workers in construction, hotels, and restaurants, which also employ many immigrants.In March of each year, workers in the basic CPS sample are administered a supplemental questionnaire in which they are asked to report their income such as hourly wage rate and additional labor force activity such as hours worked in the previous week.8 Because information on immigration is available only since 1994, our sample period is 1994–2013. We include all workers who are 18 years and older.Three recessions occurred during our 1989–2012 sample period . The economy recovered quickly from the first of these recessions in 1990–1991. The second, 2001 recession was also relatively mild. However, the third recession, the 2008–2009 Great Recession, was much more severe and had longer-lasting economic and labor market effects than the first two. We analyze the effects of recessions on hourly earnings, the probability of receiving a bonus, and weekly hours of work of employed workers. For workers paid by time, hourly earnings are a worker’s hourly wage. For piece-rate workers, we use the workers’ reported average hourly earnings. The bonus dummy equals one for workers who receive a money bonus from an employer in addition to the wage, and zero otherwise. Weekly hours of work are the number of hours interviewees reported work at their current farm job in the previous week. The explanatory variables in all these equations are the same. The explanatory variables include all the usual demographic variables: age, years of education, years of farm experience, job tenure , gender, whether the workers is Hispanic, whether the worker was born in the United States, and whether the worker speaks English.The specification uses a legal status variable to capture the bifurcated labor markets for documented and undocumented workers. It also includes crop and regional dummies.

We have seven main explanatory variables: dummies for each of the three recessions, the recession dummies interacted with the legal status dummy , and regional unemployment rates for workers in all sectors of the economy. We use separate dummies for each recession to allow for differential effects across the recession . The interaction terms capture whether employers treat undocumented workers differently than legal workers during a recession. We include the unemployment rate because it peaks after the end of each recession . We do not report the unemployment rate interacted with the undocumented dummy because we cannot reject that its coefficient is zero in any equation. We treat all these variables as exogenous to the compensation and weekly hours of individual agricultural workers. We start by examining the effects of recessions on NAWS workers’ hourly earnings. Column 1 of Table 2 presents regression estimates for the ln hourly earnings equation. The coefficients on the demographic variables have the expected signs and are generally statistically significantly different from zero at the 5% level. Undocumented workers’ hourly earnings are 2.1% less than those of documented workers. Females earn 6.4% less than males. Hispanics earn 4.9% less than non Hispanics. Unlike most previous studies, we find a statistically significant effect of education. English speakers earn 3.9% more than non-English speakers. The coefficients on the recession dummies reflect the effect of the recession on documented workers. Documented workers’ hourly earnings rose 1.8% during the 1990–1991 recession, 4.2% during the 2001 recession, and 6.9% during the Great Recession. We draw two conclusions about the effects of recessions on documented workers. First, the hourly earning effect of the Great Recession was larger than that of the relatively minor recessions, which is consistent with literature on business cycles and the farm labor market in the 1970s . Second, in all recessions, documented workers’ wages rose, which suggests that recessions cause the hired-agricultural-worker supply curve to shift leftward relatively more than did the demand curve. The sum of the coefficients on the recession dummy and its interaction with the undocumented dummy captures the effect of a recession on undocumented workers. The 1990–1991 recession did not have a statistically significant effect on undocumented workers.

Hourly earnings for undocumented workers rose by 3.4% during the 2001 recession and 1.9% during the Great Recession. In contrast to the pattern for documented workers, the undocumented workers’ earnings rose by less during the Great Recession than during the 2001 recession. Thus, not only do undocumented workers earn less than documented workers do in general, but their hourly earnings rise less during recession than do the earnings of documented workers. That is, the wage gap between documented and undocumented workers widens during recessions. In addition to hourly earnings, 28% of the workers in our sample receive bonus payments , which supplement relatively low wage payments . These deferred payments play a similar function to that of efficiency wages in other sectors . We use a binary indicator equal to one if a worker receives a money bonus. Column 2 of Table 2 shows the results of a regression using a linear probability model . For documented workers, the probability of receiving a bonus did not rise during the two relatively minor recessions but increased by 5.8 percentage points during the Great Recession. Thus, the Great Recession not only raised documented workers’ hourly earnings, but it raised the probability that they received a bonus substantially. For undocumented workers, the probability of receiving a bonus fell by 2.9 percentage points during the 1990–1991 recession and rose by 9 percentage points during the Great Recession. Again, this result is consistent with the theory that the Great Recession caused a large supply side shock. Thus,vertical grow for both documented and undocumented workers, the Great Recession had a larger, positive effect on the probability of receiving a bonus than did earlier recessions. The unemployment rate has a statistically significant effect on the probability of receiving a bonus payment. A one percentage point increase in the unemployment rate raised the probability of receiving a bonus by 0.9 percentage points.Because our data set includes information about only employed workers, we cannot directly observe the effect of a recession on total employment. However, we can examine the effect on workers’ weekly hours. When employers have difficulty recruiting workers, they have employees work more hours per week to compensate for an unusually small workforce. For documented workers, weekly hours fell by 2.2 hours during the 1990–1991 recession, but rose by 1.1 hours during the 2001 recession, and 2.3 hours during the Great Recession. For undocumented workers, weekly hours were not statistically significantly affected during the two relatively minor recessions, but rose by 2.6 hours during the Great Recession—more than for documented workers. An increase in the overall unemployment rate by 1 percentage point lowered the weekly hours by 0.3 hours. Thus, an increase in the overall unemployment rate lowered weekly hours, but weekly hours rose during relatively large recessions.We conducted five robustness checks. First, we estimated all three equations separately for documented and undocumented workers. That is, we allowed all the coefficients to vary between these two groups instead of only the recession dummies.

The coefficients on our seven key recession variables were virtually unchanged . Second, we estimated all three regressions eliminating all newcomers , about 3,300 people or 7.5% of the sample, to see if compositional changes in the workforce during recessions are driving our results. However, the coefficients were virtually unchanged . Third, we estimated all three regressions leaving out the unemployment rate. Doing so had negligible effects on the other recession variable coefficients . Fourth, we excluded the crop dummies, in case they are endogenous. The recession variable coefficients were unaffected .Do recessions have different effects in agriculture than in other sectors of the economy that employ many undocumented immigrants, such as construction, hotels, and restaurants? To answer this question, we constructed a comparable data set based on the March Current Population Survey for 1994–2013. We can look at the effects from only two recessions, 2001 and the Great Recession, because the CPS does include certain key variables prior to 1994. It also lacks a variable on bonus payments. In contrast to the NAWS, the CPS data does not record whether an immigrant is undocumented. Therefore, we focus on immigrants in general and form interaction terms between immigrant status and the recession dummies. Otherwise, we use as similar a set of demographic variables as possible. Table 4 presents the regression results for the ln wage and weekly hours in the three sectors. In none of these three sectors did either recession affect the wages of non-immigrants or of immigrants. Presumably, wages are sticky in these sectors, partially due to union and other contracts and minimum wage laws. The unemployment rate had a statistically significant effect only in the construction sector, and that positive effect is small, as in the agricultural sector. The 2001 recession did not affect the weekly hours in these sectors.