Food production, defined as the total yield of crops measured in tons, started at 3.4 million tons in 2000 for both scenarios, decreased to 1.9 million tons in 2009, and then increased to 2.9 million tons in 2015. For the future projections, from 2016 to 2020, the optimized scenario delivered more food than the baseline given the available land, but the baseline scenario showed steadily higher amounts of food production from 2022 to 2040 due to the fixed acreages and available water in that scenario. Food production decreased 8.5% under the optimization scenario.Groundwater has been over drafted in Pajaro Valley for decades . The modeling results from this study illustrate aquifer depletion, where 49 years of historical annual time series of inflows and outflows are estimated through a GBM and compared to PVHM . GBM results show an estimated annual average overdraft of −14.8 million m3=year from 1966 to 2009, close to the PVHM result of −15.9 million m3=year. Comparing the simulation models of GBM and PVHM has its limitations. GBM does not include certain inflows [landward underflow and stream flow infiltration, subtotal 17,930 thousands acre-ft ð TAFÞ=year on average] and outflows [storage flow depletion, storage depletion masked by seawater intrusion, outflows to the bay and tile drains, subtotal 18,910 TAF=year on average], which approximately cancel each other out. While these components contribute to the hydrology of the basin, GBM omits these components for simplification, and it represents a limitation of the model. The GBM was evaluated from 2016 to 2040 under two scenarios to estimate the average net groundwater storage of the area . Both scenarios began with an overall depletion of −57 million m3=year in 2015. This significant depletion of groundwater is related to the most recent multiyear drought in California that began in 2012,ebb flow tray which exacerbated the exploitation of groundwater resources because of the lack of groundwater regulations and policies in California before the passage of SGMA.
Both projections display similar behavior; however, the optimized scenario shows overall less groundwater depletion for the Pajaro Valley groundwater basin. The optimized scenario projects 10 years where the aquifer storage is zero or positive from 2020 to 2033. Both projections show the greatest depletion in 2040, with aquifer storage levels of −83 million m3=year and −48 million m3=year for the baseline and optimized scenarios, respectively.The highest storage point is 2 million m3 in 2033 and 35 million m3 in 2023 on the baseline and optimization scenarios, respectively. Overall, the GBM projections from 2016 to 2040 showed an average net groundwater depletion of −48 million m3=year for the baseline scenario in contrast with −10 million m3=year for the optimized scenario. This illustrates the possibility of a 79% increase in net groundwater storage from 2016 to 2040. Focusing on a shorter period from 2016 to 2030, the difference between projections is even greater with −47 million m3=year for the baseline scenario and −5 million m3=year for the optimization scenario, which is an 89% increase in net groundwater storage from 2016 to 2030. A single factor ANOVA evaluation indicated a significant difference in the net groundwater storage between scenarios. These findings support part of the study goal, which was that constraining agricultural water use can result in less groundwater overdraft. These results illustrate that groundwater simulation models can estimate future trends in groundwater depletion, consistent with previous studies in other agricultural and groundwater-dependent areas of California, while also validating the innovate application of optimization models to explore ecological and sustainable solutions to groundwater and land management challenges .
There are opportunities to improve water management in Pajaro Valley to reduce aquifer depletion and prioritize a reliable supply of freshwater for population demands and agriculture activities if farmers are incentivized to making collective decisions to optimize profits while managing groundwater sustainably.Successful productivity growth in agriculture has been the source of early development and subsequent structural transformation and industrialization in most of today’s high income countries. This has been amply documented by the work of historians such as Bairoch who analyzed at the Western industrialization experience, cascading from England in the mid- 1700s, to France and Germany around 1820, the United States and Russia in the mid-1800s, and finally Japan with restauration of the Meiji emperors in 1880. Following WWII, agriculture has similarly been the engine of growth and transformation for the Asian industrialization miracles in Taiwan, South Korea, China , and Vietnam . In all these countries, an agricultural revolution preceded a subsequent industrial take-off, typically by something like a half century. Agriculture has also fulfilled an important role in facilitating industrialization in countries like India, Brazil, and Chile . Agriculture remains today the expected engine of growth for the “agriculture-based countries”, those countries with a high contribution of agriculture to GDP growth and a high share of their poor in the rural sector . These are also countries where the farm population is importantly composed of smallholder farmers , in some cases exclusively and in others coexisting with larger commercial farms . In both cases, agricultural growth importantly requires modernization of the operation of SHFs. With labor-intensive industrialization increasingly compromised by robotization and the reshoring of industries toward the industrialized countries , agriculture with agroindustry and the associated linkages to services and non-tradable consumption has been heralded as a potentially effective strategy for GDP growth in these countries . This includes most of the Sub-Saharan Africa countries. This approach is a major departure from the classical structural transformation approach based on labor-intensive industrialization advocated in the dual economy models . The World Development Report Agriculture for Development’s main message was that agriculture-based countries should invest more in agriculture in order to fully capture its potential for growth and poverty reduction.
Following the world food crisis of 2008, there was a short-term positive response by governments, international organizations, and the donor community with a sharp increase in investment in agriculture. The number of countries meeting the CAADEP goal of allocating at least 10% of government expenditures to agriculture increased from 3 in 2007 to 10 in 2009. Overseas development assistance to agriculture increased by 60% between 2007 and 2009. But this response has not been sustained. In 2014 only 2 SSA countries out of 43 met the CAADEP goal. The modal SSA country spends only 5% of its public expenditures on agriculture. No SSA country spends a percentage of its public budget on agriculture that reaches the percentage contribution of agriculture to GDP, and 75% of the countries spend less than half that percentage . CAADEP also set a goal for public spending on agricultural Research-and-Development to reach 1% of agricultural GDP. Returns to investing in agricultural research are typically significantly in excess of cost relative to other public programs, indicating under-investment . This takes extreme forms in SSA where investment is by far the lowest among regions and has been declining over the last decade. In 2011, only six countries met the CAADEP research goal . With failure to invest in agriculture, the yield gap on cereals has continued to increase between SSA and other regions of the world. This gap is correlated with a growing chemical fertilizer gap and a large deficit in irrigation. Today, the World Development Report’s main message continues to be advocated by international development organizations such as the World Bank, the Food and Agriculture Organization, and the International Fund for Agricultural Development . This is motivated by the observation that 51% of the world extreme poor live in SSA, a share that continues to rise, and 78% of the world extreme poor work in agriculture in spite of rapid urbanization. Success in using Agriculture for Development is thus essential to meet the Sustainable Development Goals on poverty and hunger. In the current global economic context for the SSA countries,flood and drain tray investing in agriculture where the poor work has proven more effective for poverty reduction than taking the poor out of agriculture and to an urban-industrial environment through a Lewis -type structural transformation. Research shows that the poor are not found in agriculture due to adverse selection. Poverty reduction, where it has happened, has been more effective through productivity growth where the poor work than through structural transformation . A Solow-type decomposition of sources of growth shows that agricultural output growth in SSA in the 1985- 2012 period originated for 63% from area expansion compared to 8% from factor deepening and 29% from productivity growth . This is not sustainable due to an effective land constraint and declining farm size in most countries as a consequence of rapid population growth. Take Malawi as an example where agricultural land for households engaged in agricultural production fell from 2.3 acres in 2004, to 1.8 in 2010, and 1.4 in 2016 . Productivity growth and factor deepening consequently have to be the main sources of growth in SSA agriculture as in the rest of the developing world where they account for 83% of agricultural output growth.
This opinion on the role of agriculture for development is far from universally shared in the development community. Gollin et al. and Collier and Dercon have argued that rural poverty reduction has to come from employment creation in the urban-industrial environment and a structural transformation of the economy. As seen above, governments have correspondingly not invested public resources in agriculture to the recommended levels. Hence the puzzle in using agriculture for development is: why has the World Development Report/CAADEP recommendation not been followed? We argue here that it is because the mainly supply-side approach used for implementation has proved insufficiently effective, and needs to be complemented for this by a more explicit demand-side approach. There are African countries to look at for successful progress toward productivity growth in staple foods and a rural transformation . Chemical fertilizer use is overall low , but uneven across countries. The LSMS-ISA data show that the share of cultivating households using chemical fertilizer reaches 77% in Malawi, 56% in Ethiopia, and 41% in Nigeria, while remaining at 17% in Niger and Tanzania, and 3% in Uganda . Rural transformation is accompanied by land concentration in medium farms in countries like Kenya , Ghana , Tanzania , and Zambia . These farms are typically mechanized and owned by well-educated urban-based professionals who can be effective agents for technology adoption. These various success stories show that using agriculture for development can be done, but has not yet been sufficient to overcome aggregate rising gaps in yields between SSA and the rest of the world.While there has been limited success with raising public expenditures on agriculture, there has been considerable progress with data collection and with rigorous experimentation on how to promote the modernization of agriculture. We consequently know a lot more today about how to use agriculture for development than we did ten years ago, even though this knowledge has most often not been put into practice in the desirable form and to the desirable degree. It is consequently important to start by reviewing what we have learned. The main argument that has been used in support of the need for a structural transformation as the mechanism to grow and reduce poverty is that there is a large labor productivity gap between agriculture and non-agriculture . An important observation, however, based on the LSMS-ISA data for SSA is that while the gap in labor productivity per person per year between non-agriculture and agriculture is indeed large, the gap in labor productivity per hour worked is relatively small . In other words, when agricultural workers do work, their labor productivity is not very different from that of non-agricultural workers. What this suggests is that there is a deficit in work opportunities for agricultural vs. non-agricultural workers that creates an income gap between the two categories of workers. Because households engage in a multiplicity of sectoral activities, the relevant contrast in labor productivity is not between agriculture and non-agriculture, but between rural and urban households, with rural households typically principally engaged in agriculture. Looking at labor calendars for rural and urban households in Malawi in Figure 1, we see that weekly household hours worked are not different for rural and urban households at peak labor time, which corresponds to the planting season in December and January .