The effects of ethnic divisions are of particular importance in the Kenyan context

The best method for determining incorporation timing is to walk the field with a shovel and dig numerous holes and “feel” soil moisture at various depths throughout the field. In medium- and heavy textured soils you want to be able to form a ball of soil in your hand and then break it apart easily. For this exercise it is important to get soil from at least 8 inches deep. If the soil “ribbons” easily when squeezed between your thumb and index finger it is probably still too wet to work . Optimum soil moisture is critical for good incorporation and breakdown; in average rainfall years early April is commonly the best time for incorporation in the Central Coast region. After flail mowing the residue needs to be mixed with the soil to enhance microbial breakdown and facilitate seedbed formation. The best tool for this is a mechanical spader. Spaders are ideal for cover crop incorporation for many reasons. When operated in optimal soil moisture conditions spaders have minimal impact on soil aggregation and create almost no compaction compared to other primary tillage tools. Spaders are capable of uniformly mixing the cover crop residue into the tilled zone while at the same time leaving the soil lofted and well aerated, allowing for ideal conditions for microbial breakdown of the residue. Spaders also have two major drawbacks: they are expensive, cannabis grow equipment and they require very slow gearing and high horse power to operate; 10 horse power per working foot of spader is the basic requirement depending on soil conditions and depth of operation. They run at a very slow ground speed, often in the range of 0.6 to 0.8 mph. Thus a 7-foot wide spader requires 70 HP and takes between 3 and 4 hours to spade an acre.

Although time consuming, the results are impossible to replicate with any other tillage options now available. If a mechanical spader is not available the next best and probably most commonly used tool for cover crop incorporation is a heavy offset wheel disc. Depending on the size and weight of the disc multiple passes are often required for adequate incorporation. Chiseling after the first several passes will facilitate the disc’s ability to turn soil and will also help break up compaction from the disc.Rapid urbanization, compounded by globalization, has had lasting effects on the agricultural sector and on both urban and rural communities: As urban populations increase, they place more demands on a shrinking group of rurally-based food suppliers. And as the movement for locally based food systems grows to address this and other food system problems, urban agriculture has become a focal point for discussion, creativity, and progress. Indeed, the production of food in and around densely populated cities bears much promise as part of any solution to food supply and access issues for urban populations. Growing Power, Inc. , a Milwaukee-based organization, exemplifies the way that urban agriculture can address some of the needs of rapidly growing urban communities, particularly those with poor, minority populations. Will Allen, Growing Power’s founder—born to former sharecroppers in 1949—was drawn back to agriculture after a career in professional basketball. Aside from his love for growing food, he saw that the mostly poor, black community near his roadside stand in North Milwaukee had limited access to fresh vegetables or to vegetables they preferred. Confirming his observation, a 2006 study found that more diverse food options exist in wealthy and white neighborhoods than in poor and minority neighborhoods.2 Allen decided that he would serve this unmet demand by growing fresh food in the neighborhood where his customers lived and involve the community in the process. In 1993, long before urban agriculture bloomed into the movement it is today, Growing Power began. The importance of equal access to fresh food cannot be overestimated.

While ever more exotic fruits and vegetables from around the world stock health and natural foods stores in wealthy and predominantly white neighborhoods, poor and minority communities face fewer and less healthy food choices in the form of convenience stores, fast food restaurants, and disappearing supermarkets. This lack of access can lead to higher rates of diet related illnesses . Growing Power has been working to create an alternative food system based on intensive fruit and vegetable production, fish raising, and composting, in order to make healthy food available and affordable to the surrounding community, and to provide community members with some control over their food choices. But as anyone who has initiated an urban agricultural project knows, fertile, uncontaminated land is often difficult to find in a city. Even if land with soil is available, most empty lots are in former industrial areas where toxic contamination often renders land unusable . In Milwaukee, Growing Power sat on a lot with no soil and five abandoned greenhouses. Compost became the foundation for all of Growing Power’s activities. The raw materials needed to produce it were in abundant and cheap supply in the city— food waste, brewery grains, coffee grounds, newspaper waste, grass clippings, and leaf mold are all by-products of urban life destined, in most places, for the landfill. Businesses will often donate these materials to urban agriculture projects, saving the cost of garbage hauling services. Compost, and vermicompost in particular, also provides a renewable source of fertilizer that doesn’t rely on fossil-fuel inputs and can itself be used as a growing media. With a healthy compost-based system, Growing Power discovered a low-cost, renewable, and easy to-duplicate solution to one of the biggest hurdles people face when growing food in cities. Since 1993, Growing Power has grown in size and scope, starting gardens in Chicago as well as Milwaukee, and training centers in 15 cities, and including youth training, outreach and education, and policy initiatives in its mission. Interest in urban agriculture has also blossomed into a movement that includes commercial urban farms, scores of community farms and gardens, and educational gardens and training programs growing food and flowers and raising chickens, bees, goats, and other livestock for local consumption.

Urban agriculture has grown so rapidly in the last two decades that in 2012, the USDA granted $453,000 to Penn State University and New York University for a nationwide survey of the “State of Urban Agriculture”3 with an eye toward providing technical assistance, evaluating risk management, and removing barriers for urban farmers. The federal government’s interest in urban agriculture comes on the heels of state and local initiatives to encourage urban agriculture in numerous cities, including Milwaukee, Chicago, New York and San Francisco. The driving force behind these initiatives and the urban agriculture movement as a whole has always been groups of committed individuals in urban communities in search of food, community, opportunity, security, and access. What makes the Growing Power model work is not just its innovative techniques and creative use of urban spaces, but the partnership with its neighbors who not only receive the program’s services, vertical grow rack but contribute significantly to its success.There is evidence to suggest that ethnic heterogeneity may impede economic growth. A negative influence on decision-making in the public sphere has been documented: public goods provision is lower and macroeconomic policies of lower quality in ethnically fragmented societies . The possibility of an additional direct effect on productivity in the private sector has long been recognized, however. Individuals of different ethnicities may have different skill-sets and therefore complement each other in production, but it is also possible that workers of the same ethnic background collaborate more effectively . Evidence from poor countries on the productivity effects of ethnic diversity is largely absent. This paper provides novel microeconometric evidence on the productivity effects of ethnic divisions. I identify a negative effect of ethnic diversity on output in the context of joint production at a large plant in Kenya where workers were quasi-randomly assigned to teams. I then begin to address how output responds to increased conflict between ethnic groups, how firms respond to lower productivity in diverse teams, and how workplace behavior responds to policies implemented by firms to limit ethnic diversity distortions. A model of taste-based discrimination at work explains my findings across these dimensions. I study a sample of 924 workers working in teams at a plant in Kenya. The workers package flowers and prepare them for shipping: productivity is observed and measured by daily individual output. Tribal competition for political power and economic resources has been a defining character of Kenyan society since independence . Workers at the flower plant are almost equally drawn from two historically antagonistic ethnic blocs – the Kikuyu and the Luo . Production takes place in triangular packing units. One upstream “supplier” supplies and arranges roses that are then passed on to two downstream “processors” who assemble the flowers into bunches, as illustrated in figure 1a. The output of each of the two processors is observed. During the first period of the sample, processors were paid a piece rate based on own output and suppliers a piece rate based on total team output. Inefficiently low supply of roses to downstream workers of the rival ethnic group was thus costly for suppliers. I show that the plant’s system of assigning workers to positions through a rotation process generates quasi-random variation in team composition.

A worker’s past productivity and observable characteristics are orthogonal to those of other workers in her assigned team. The productivity effect of ethnic diversity can thus be identified by comparing the output of teams of different compositions. Two natural experiments during the time period for which I have data allow me to go further. During the second period of the sample, in early 2008, contentious presidential election results led to political and violent conflict between the Kikuyu and Luo ethnic groups, but production at the plant continued as usual. In the third period of the sample, starting six weeks after conflict began, the plant implemented a new pay system in which processors were paid for their combined output . By taking advantage of the three periods observed, I identify the source of productivity effects of ethnic diversity in the context of plant production in Kenya; how the economic costs of ethnic diversity vary with the political and social environment; and how managers responded to ethnic diversity distortions at the plant, and how workplace behavior changed as a consequence of the policies implemented in response. I model ethnic diversity effects as arising from a “taste for discrimination” among upstream workers: suppliers attach a potentially differential weight to coethnics’ and noncoethnics’ utility, a formulation that follows Becker , Charness and Rabin and others. The model predicts that discriminatory suppliers in mixed teams will “misallocate” flowers both vertically – under supplying downstream workers of the other ethnic group – and horizontally – shifting flowers from non-coethnic to coethnic downstream workers.1 The impact of horizontal misallocation on total output will depend on the relative productivity of favored and non-favored downstream workers. If conflict led to a decrease in non-coethnics’ utility-weight, a differential fall in mixed teams’ output in early 2008 is predicted. Under team pay, a positive output effect of a reduction in horizontal misallocation is expected to offset negative free riding effects, in teams in which the two processors are of different ethnic groups. The reason is that suppliers can no longer influence the relative pay of the two processors through relative supply under team pay. Quasi-random assignment led to teams of three different ethnicity configurations. About a quarter of observed teams are ethnically homogeneous, another quarter are “vertically mixed” teams in which both processors are of a different ethnic group than the supplier, and about half are “horizontally mixed” teams in which one processor is of a different ethnic group than the supplier. The ethnicity configurations are displayed in figure 1b. I test the model’s predictions by comparing the average output of teams of different ethnicity configurations within and across the three sample periods. In the first main result of the paper, I find that vertically mixed teams were eight percent less productive and horizontally mixed teams five percent less productive than homogeneous teams during the first period of the sample.