Author: naturehydro

The second of these layers includes existing residential neighborhoods, which typically consist of small parcels of land owned by residents who are highly resistant to redevelopment, unless in the context of adding second units to existing houses. Even the process of creating secondary units tends to be slow and to produce relatively few units, despite some municipal programs to encourage it. Commercial and industrial land uses and apartment buildings are likely to be located near major and minor arterials and freeway ramps. Low‐density residential development is more likely to occur at a distance from these roads due to noise and traffic concerns. Mid‐ to high‐density residential development is likely to be attracted to downtown locations, neighborhood centers, and shopping centers; especially in the low‐GHG emissions scenarios in which public policy focuses on redeveloping and building up existing urban centers. Mid‐ to high‐density residential development is also likely to be attracted to railroad stations in these scenarios as new passenger service is added and public policy emphasizes “transit‐oriented development.” Some industrial development is likely to locate near railroad lines in these scenarios, since rail offers more energy‐efficient transportation of many goods. We assumed that locations currently slated for development that are distant from existing cities County, such as the Dunnigan area along Interstate 5 in the north of the county, will serve as urbanization attractors only in the scenario with higher GHG emissions. Census blocks with recent development are distributed fairly evenly between rural and urban areas of the county. We assumed that these blocks with recent growth would attract more urbanization in the future. This is in part because these areas are likely to possess infrastructure such as roads, water lines, sewer mains, and power lines which make development easier and cheaper.

It is also because these areas are likely to contain previously subdivided parcels of land that are not yet built upon,nft hydroponic and land owners that are more interested in subdividing, selling, or building on the land. In scenario with higher GHG emissions, in which planning controls are weaker, census blocks with growth will be a stronger attractor, particularly in exurban locations. In the lower GHG emissions scenarios they will play a weaker role in attracting urbanization, since public policy is more likely to protect non‐urban land, and less left‐over land is likely within urban areas. Previously, our larger research team developed a set of story lines for scenarios reflecting different climate change and urbanization policies for Yolo County in 2050 . These were intended to emulate for the county story lines developed in 2000 by the IPCC , with the addition of a scenario with very low GHG emissions corresponding to an even more stringent policy direction than established by California’s AB 32 legislation. Each scenario corresponds to a broad‐brush story line, which is built upon a set of political, economic, institutional, and demographic assumptions. Each story line is a possible future for urban growth and emissions for the county.As in IPCC scenario A2 , our A2 scenario assumes that population growth would remain high, with an approximate doubling of the current county population to 394,000 . With an increase in population, continued economic growth and technological innovation, the county would see urbanized areas increase by 50 percent. Current preservation and land use policies would remain in place and although new suburban subdivisions would be built, there would be some focus on improving land use through greater land use mix, higher densities, and more infill, and limiting sprawl. Agricultural land would be lost to urbanization while less participation in farmland preservation programs, such as the Williamson Act, would result in less farm acreage and fewer farmers. Even with an increase in population, vehicle miles traveled would remain stable through land use and pricing changes, increased use of alternative modes, and greater fuel efficiencies. Still, the A2 story line would be fossil fuel intensive as a result of more drivers and the dominance of automobiles as the main transportation mode.

In terms of climate, under A2, average temperatures are predicted to increase between 1°C and 3°C for 2050. Changes in cropping systems and technological support for agriculture would continue in about the same way as present, without major societal investment in alternative options to deal with the impacts of global warming. The A2 story line is a near continuation of current demographic, economic, technological, and environmental developments with some improvements and responses to current issues being addressed and implemented. We should emphasize that in terms of suburban sprawl, the A2 story line is by no means a worst‐case scenario. Rather, it should be seen as a continuation of practices in the 1990 to 2010 period. If this story line had been based on prevailing development patterns from 1950 to 1990, suburban densities would be in the range of 4–6 units per acre instead of 8, less development would occur in medium‐ and high‐density forms, and a higher percentage of larger 1–10 acre ranchettes would be created. Suburban sprawl would cover a much larger percentage of the county in that case, taking far more agricultural land out of production. In IPCC scenario B1 , societies become more conscious of environmental problems and climate change, and sustainable development efforts are implemented. Under our Yolo County B1 story line, population would grow slowly, reaching a mid‐range population size of 335,000 by 2050 . Economic development would be moderate, with a shift from the production of goods to a more service‐based economy that is connected to the larger global economy. Technological innovation remains high in the Sacramento region, with an emphasis on small‐scale, green technologies. B1 is a relatively low GHG emissions scenario in which the urban area extends only 20 percent as a result of compact growth through higher densities, increased infill, and a focus on small, locally owned retail stores rather than big box developments that require more driving. As current transportation and emission policies become more stringent and the use of high‐efficiency vehicles and alternative modes increases, vehicle miles traveled would be significantly reduced and transportation emissions with them. 

Agricultural land conversion would be lower in this story line as a result of less urban expansion and the use of farming easements and other incentives to maintain land in farming. Though long‐term temperatures may be lower than in the A2 story line, average temperatures in 2050 do not differ . Consistent with AB 32, voluntary actions in agriculture would place more emphasis on increasing carbon sequestration and decreasing N2O emissions through multiple crops per year, more ecologically intensive practices, reduction of fertilizer use, and efforts to capture methane emissions from livestock. Moreover, there would be greater societal investment in preparing ahead for climate change adaptation options, such as crop breeding, pest management, and resilience to intermittent droughts. Under B1, Yolo County experiences the benefits of slower population growth and improved urban land use practices, resulting in preservation of agricultural land and reduced GHG emissions.To the two IPCC‐based story lines, we add a third scenario with more stringent GHG emissions regulation than AB 32. Under our AB32+ story line, Yolo County experiences slower population growth reaching only 235,000 in 2050, which would have to occur through policies or voluntary actions that affect family planning and migration . In this story line, moderate economic growth focuses on value‐added production economic viability of the local rural sector, and support for ecosystem services generated by closer alignment between the rural and urban sectors . A less resource‐intensive lifestyle would dominate, coupled with an increase in the quality of life through an increase in ecosystem services in both sectors. Priorities would be placed on both regulating services and cultural services . The urban boundary remains at the current extent through strict land use planning policies and development emphasizing efficient use of land, mixed use, intense infill, increased densities,hydroponic gutter and growth in the urban core. More compact development patterns and the promotion of local development and payment for ecosystem services, coupled with many alternative modes of transportation and increased use of zero emission vehicles, would result in a reduction of vehicle miles traveled and GHG emissions from transportation. Although long‐term temperatures may be lowest under this scenario, 2050 temperatures are essentially the same as in the other story lines. In order to both mitigate and adapt to the changing climate, agricultural producers would make major changes in management practices, focusing on ecological intensification rather than on non‐renewable inputs. This would require substantial societal investment in development of new renewable technologies and for diversification of cropping systems to fit site‐specific situations. Practices such as farm scaping and revegetation of riparian buffer zones to mitigate and reduce GHG emissions would also be promoted for their co‐benefits, such as improved water quality .

Markets for products may become more locally based, and efforts would be made to reduce GHG emissions from processing and transport of agricultural products. Overall emissions would be the lowest under AB32+ with a reduction from urban areas due to denser, more balanced land development, less resource‐intensive lifestyles, and improved transportation options. Changes in crop choice and management practices would likewise reduce GHG emissions from agriculture. In addition to modeling these three scenarios using UPlan, we modeled additional versions of A2 and AB32+ in which population was held constant at the B1 level. This step allows us a more analogous comparison of the three story lines.After using UPlan to produce urban growth footprints for the above scenarios, we calculated two main categories of GHG emissions for the new urbanization produced by each. These calculations are very approximate, but help to give a sense of the magnitude of variations that can result from different policy approaches. One category of GHG emissions was from transportation. Household travel surveys done by SACOG show that household vehicle miles travelled vary by a factor of six between households in low‐density per acre and high‐density locations . Some of this difference may be due to household size and composition, but much is likely due to proximity to jobs, shopping, schools, and alternative transportation modes. In addition, many other policy steps in the lower GHG emissions scenarios are likely to reduce driving in the 2050 time frame. These other factors include rising gas or carbon taxes; improved balance of jobs, housing, and shopping within communities; improved bicycle, pedestrian, and public transit options; and other economic incentives such as higher parking charges and tolls. Transportation emissions are also of course dependent on the fuel efficiency of motor vehicles. Average fuel efficiency of American vehicles remained more‐or‐less unchanged from the mid‐ 1980s through 2010, and so for purposes of illustration, this was assumed in the A2 scenario until 2050. In the B1 scenario, we assumed modest efficiency increases of 2 percent a year , and for the AB32+ scenario we assumed improvements of 4 percent a year . Rather than continually improve conventional gasoline engines, these scenarios would most likely see increasing percentages of the motor vehicle fleet converting to hybrid or all‐electric propulsion, with an increasing proportion of the electricity produced by renewable sources.Household energy use was a second category of calculated GHG emissions. In Yolo County domestic energy comes almost entirely from electricity or natural gas, as oil heating is rare in California and use of wood stoves is also low and increasingly discouraged due to local air pollution concerns. Here again we can expect substantial differences in GHG emissions between infill urbanization and new residential development on agricultural land, due to larger unit sizes and a much higher percentage of stand‐alone single family homes in the former case. To calculate household energy use for the three scenarios, we used data from the 2009 California Residential Appliance Saturation Study , a collaboration of the state’s five largest utility companies that surveyed detailed consumption habits of nearly 26,000 households. This study breaks households down by climate zone, and compares energy consumption for single‐family homes, town homes, small multifamily buildings, large multifamily buildings, and mobile homes by California Energy Commission climate zone. Both electricity and gas use for the middle three categories were approximately half that of single family homes, probably in large part because average unit sizes were smaller, and perhaps also because shared‐wall construction tends to be more energy efficient than stand‐alone single‐ family homes.

The Clear Lake release schedule specifies how much water is available annually and monthly to the District during the peak agricultural season from April to September. The decree’s “Quantity” criteria sets allowable seasonal withdrawal limits based on April 1 water levels recorded at Rumsey, known as the Rumsey gauge. If the Rumsey gauge is at or above 7.54 feet, then 150,000 acre feet of water is available for the growing season from April 1 to October 31. Monthly percentages of the ASW are available for release each month. If Rumsey levels are below 3.22 feet, no water can be released that year apart from flood flows. For in‐between levels, ASW are set in the release schedule that increases to a maximum of 150,000 acre feet in what is known as the quantity criteria. As per these stipulations, the District did not make any releases in the severe drought of 1976–1977, as well as in 1990 at the end of several dry years. The Solano Decree also stipulates “Stage criteria” that set limits to draw down, posing an additional constraint to the District’s withdrawal of water in any given month. Clear Lake releases in the winter are also controlled by the 1920 Gopcevic Decree for flood control operation. The highly controlled nature of this lake can be attested by the historical monthly average lake levels which have varied only 5.7 ft on average within a water year, with a maximum range of 10.9 ft and a minimum of only 2.3 ft.The Cache Creek model, run at a monthly time step, uses climate and land cover information to simulate the water balance. It uses the results to simulate the management of Clear Lake and Indian Valley Reservoirs and water supply for irrigation downstream. The model simulates irrigation demand for 20 crop types within Yolo County, grow bags garden which is met through surface and groundwater sources .

The model was calibrated to a historical run from 1971–2000, which formed the baseline scenario. The calibrated model was then run under various combinations of climate and agricultural land use projections as described below. Figure 3.1 shows the study area along with the spatial discretization of the model. The spatial domain of the model covers 5027 square kilometers and includes the Cache Creek watershed up to Capay , and all of Yolo County. The focus of the irrigation water demand and supply analysis is on the District service area , although the model can also simulate irrigation demand for the rest of the county. Table 3.1summarizes each catchment’s characteristics. A water balance simulated for each catchment. Spatial data on elevation, watersheds, and land use were acquired and used to define and characterize each catchment. Elevation data were extracted from the Digital Elevation Model provided by the U.S. Geological Survey. Land cover information was assembled from two sources. For the non‐agricultural landscape, the National Landcover Data Set was used . For the agricultural areas, county reports and DWR Land Use Surveys were used . Upstream catchments were aggregated from the DWR watersheds layer. This aggregation was based on climate considerations, the locations of major infrastructure , in‐stream flow requirements, and flow gauges. Parameters of the rainfall‐runoff module were calibrated against the longest available continuous data from gauges in unimpaired watersheds. These were at Kelsey Creek and Hough Springs on the north fork of Cache Creek , in the headwaters of Clear Lake and Indian Valley, respectively. Goodness of fit metrics were computed for each set of simulated and observed hydrographs. Two groundwater objects were defined and conceptually aligned to the groundwater sub‐ basins delineated by DWR: one below Capay Valley receiving recharge as infiltration from the Capay Valley catchment, the other below the Yolo Valley floor, receiving recharge from the catchments downstream of Capay.

Our model’s treatment of groundwater is similar to the Central Valley application . It is capable of relative comparison among scenarios of groundwater recharge and extraction volumes, but not of simulating absolute groundwater depths. The model simulates the operations of Clear Lake, Indian Valley, and the water delivery through canals. Detailed description of how WEAP simulates reservoir releases through conservation storage and flood rules is available in Yates et al. . Reservoir physical characteristics were obtained from California Department of Water Resouces California Data Exchange Center and the District. Indian Valley operating rules were obtained from the District. Clear Lake operating rules were obtained from the District, and from documentation of the Solano and Gopcevic Decrees described earlier . Details, including the stepwise procedure on implementing the Solano Decree, are available in public documents and through the District. Clear Lake releases during the wet season are controlled by the Gopcevic Decree, for which target storage levels come into play from January to March. These target storages were set as WEAP’s “Top of Conservation” in the model’s Clear Lake reservoir object. The second operating constraint, also from the Solano Decree, is its stage limitation criteria. These criteria were programmed and set as “Top of Buffer” in the reservoir object. The third constraintis the hydraulic capacity of Clear Lake’s outlet channel. Hydraulic capacity varies by the stage; data obtained from the District was used to develop a hydraulic capacity constraint as a function of stage. This expression was set as a hydraulic constraint on the releases from Clear Lake in the model. Outlet flows were then constrained to be a minimum of the hydraulic capacity constraint, and the allowable monthly withdrawal as determined by the Solano decree’s Quantitative criteria—the latter also entirely encoded within WEAP. Clear Lake does not provide carryover storage for irrigation demand. Although Indian Valley does provide carryover storage, typically it is operated with no carryover storage . In general, the District attempts to utilize all its Clear Lake allocation each year. This means that Clear Lake usage is prioritized over Indian Valley as much as possible. In the model’s setting of supply priorities, this translates to a lower filling priority for Clear Lake over Indian Valley. Simulation of reservoir operations was verified by comparing simulated versus observed reservoir levels.

The District’s main conveyance is in the form of 175 miles of mostly unlined canals and arterial ditches that run off the West Adams and Winters Canals from Capay Diversion Dam on Cache Creek. In the model, these conveyances are aggregated into a single transmission link object, with capacity set to the total distribution’s capacity of 750 cubic feet per second , and with an estimated leakage of 40 percent of conveyance flows obtained from calibration attempts and informed by District estimates of mass balances . Seventeen crop categories were modeled for the catchments dominated by agriculture. Table 3.3 lists the different crop categories considered along with county‐wide acreages from four selected years. The crop categories are informed by DWR’s irrigated crop acres and water use portfolio,grow bag for tomato taking into consideration both the crop categories and corresponding acreages available through the county reports as well as estimates of the District scale cropping pattern. An annual time series of total irrigated acreage and irrigated crop areas was assembled at the county level . Individual crop acreages were spatially distributed among the four agricultural catchments using GIS datasets available for 1989 and 1997 through the DWR Land Use Surveys . This allowed a cropping pattern to be represented in the model for the historical period for each agricultural catchment. Each crop’s irrigation water needs were simulated using crop‐specific crop coefficients, irrigation schedules, and irrigation thresholds. Crop‐specific parameters pertaining to irrigation were adapted from the Central Valley application by Joyce et al. , who calibrated the crop and irrigation parameters at the spatial scale of the DWR Planning Area level against four annual estimates of applied water published by DWR for 1998,1999, 2000, and 2001 . In our model, we also used DWR portfolio data available for the same years, but at a finer spatial level—the Detailed Analysis Unit . The irrigation threshold parameter in WEAP was calibrated for each crop to match DWR’s applied water estimates for 1998, 1999, 2000, and 2001 for the DWR’s Lower Cache Creek DAU which closely follows the county boundaries. Figure 3.2 presents the calibrated irrigation schedules and thresholds for each crop. The model’s estimation of water demand represents a departure from the operations of the District. The District solicits water demands from its customers every year in March, and then decides by April how much total quantity will be available. This decision is based on water levels in the two reservoirs and a projection of the season ahead. Since our goal was to look to the future, we used a simulation approach instead of hard‐coding the historical demand based on the District’s historical roster. The latter would not have provided us the means of projecting demand into the future.Yolo County based on the relationship between historical crop acreage, a set of economic variables , and climate variables . To forecast cropping area from the present to 2050, climatic variables were calculated from daily climate projections for the A2 and B1 scenarios generated by the GFDL climate model described above. The second land use projection was based on a hypothetical scenario envisioning an agricultural landscape which adapts to climate change in two ways: by allocating a smaller fraction of land to crops that require large amounts water; and by increasing crop diversity. For example, the acreage of rice, alfalfa, and other water intensive field crops were gradually reduced to the lows observed during a period of severe drought in the mid‐1970s . Likewise, an increase in crop diversity over time was simulated by progressively allocating a larger fraction of land to vineyards, winter grains, almonds, deciduous orchards, subtropical orchards, tomatoes, cucurbits, and truck crops . Since this crop diversification projection is a hypothetical construct, rather than a statistically derived forecast, a future time frame of 2009–2099 was used.

It should also be noted that this approach assumes gradual changes in crop acreage and did not attempt to capture the year to year variability reflected in the historic record. Statewide there has been a notable shift in irrigation methods from surface water applied using flood or furrow irrigation towards low‐volume sprinkler and drip irrigation, particularly for vegetable crops, orchards, and vineyards . These methods can potentially reduce soil evaporation and applied water . Furthermore, a recent survey of grower perspectives on water scarcity and climate change in Yolo County indicates a strong inclination to expand their use of drip and low‐volume irrigation among local farmers . Likewise, incentive programs to promote adoption of improved irrigation technology are seen as a politically feasible water demand management strategy. However, one criticism is that, in some watersheds, such policies have failed to curtail groundwater extraction as some farmers use the “water savings” to expand irrigated acreage or grow more water‐ intensive crops . As such, we included a conceptual scenario which assumes that irrigation technology and efficiency will continue to improve in coming decades but overall irrigated acreage in the district will not. We reflect these trends in the model, by decreasing the irrigation threshold parameter, in a manner similar to the work of Joyce et al. and Purkey et al. . Beginning in 2010, irrigation thresholds for each crop, except for wine grapes, winter grains, and safflower, were assumed to decrease linearly so that by 2099 they reached 70 percent of the historic reference threshold. For the latter crops, no change in water‐saving irrigation technologies was assumed because vineyards are already on drip irrigation, winter grains are mostly supplied by rain and stored soil water, and safflower is already a low water consuming crop.Another measure of water shortage is the frequency of years receiving no water allocation from Clear Lake. For example, if the Rumsey gauge is below 3.22 feet, the initial ASW assessment is for no allocation of water that year. During the historical period the model predicted 6 such years . Model projections for the climate only scenario suggest that the number of years receiving no allocation will increase gradually with time, particularly during the latter half of the century. In the far term under A2, reservoir inflows are very low in some years in response to the warmer and drier conditions.

The sand fraction was separated from the clay and silt fractions by wet sieving through a 0.05 mm sieve. Water retention at various tensions was determined using a pressure plate. Plant-available water holding capacity was estimated as the volume fraction of water retained between 33 and 1500 kPa. A sample of < 2-mm , air-dry soil was placed on a porous ceramic plate and wetted by capillary action; gravimetric water content was measured following attainment of equilibrium at 33 and 1500 kPa. Soil pH was measured 1:2 in H2O and 1.0 M KCl. Phosphate retention was determined using the method of Blakemore et al. and the Bray-1 extraction was used as an estimate of available P . Exchangeable cations were displaced by 1 M NH4OAc at pH 7.0, then the cations were measured in the supernatant using an atomic absorption spectrometer . The cation exchange capacity was determined in 1 M NH4OAc after extraction of NH4 + by 10% NaCl as a measure of CEC. Base saturation was calculated as the sum of base cations by 1 M NH4OAc divided by CEC. Sulfate-sulfur was extracted using monocalcium phosphate as outlined by Schulte and Eik and available micro-nutrients were determined by DTPA extraction . All weight percent data were reported on an oven-dry basis . Non-sequential selective dissolution in Na-pyrophosphate and ammonium-oxalate was used to characterize Fe, Al and Si in various pedogenic pools. Total C and N concentrations were determined on ground samples by dry combustion using a Costech C/N analyzer . Soil microbial biomass C and N were measured using chloroform fumigation and direct extraction with 0.5 M K2SO4 . Briefly, 10 g oven-dry equivalent samples were fumigated for 48 h in the dark,plastic pot and then C and N were extracted with 0.5 M K2SO4. Similar extraction was applied for non-fumigated samples. The non-fumigated control values were subtracted from fumigated values as an estimate of microbial C and N. A Kec/Ken factor of 0.35 was applied for both C and N . Carbon mineralization was measured in the topsoil and subsoil by incubating duplicate soil samples in the dark under laboratory conditions over a 119-day period.

Soil moisture was adjusted to ∼ 80% of field capacity and pre-incubated for one week prior to starting the long-term incubation. Soils were incubated in sealed Mason jars fitted with septa. Carbon dioxide in the headspace of each soil sample and blanks with no soil was measured each week using an Infrared Gas Analyzer. The CO2 emission was normalized to initial total C content of each soil and expressed as CO2-C mg kg−1 soil C. In addition, net N mineralization was measured on these same samples at the end of the 119-day incubation by determining concentrations of mineral N in 1 M KCl extracts at time zero and at 119 days. Quantification of NO3 – used the vanadium chloride method and NH4 + the Berthelot reaction with a salicylate analog of indophenol blue . A correlation analysis was performed to assess soil properties most strongly affected by land-use changes, using IBM SPSS Statistics 22. 2013.All soils were well drained with an A horizon overlying Bw horizons that extended to the depth of investigation . Soil particle-size distribution was similar among the four sites with the majority of the horizons having a loam texture . Some distinct changes in particle-size distribution within various pedons are attributable to more recent tephra deposition that resulted in burial of the former soil profile. Bulk density in subsoil horizons was very low , characteristic of soils formed in volcanic ash . Db was also low in the A horizon of the pine forest , but was higher under agricultural management due to traffic compaction resulting in a reduced pore volume. The agricultural soils displayed a distinct increase in Db and a reduction in total porosity in the topsoil horizons compared to the pine forest soil. Given the low bulk densities, total porosity was correspondingly high, ranging between 60 and 77%, with values decreasing in surface horizons with agricultural management. Plant-available soil water was generally in a narrow range with the exception of the surface horizons of the pine forest soil . The water retention capacity varied from 37 to 53% in topsoil horizons and from 45 to 51% in subsoil horizons with the lowest values in the pine forest.

Soil pH-H2O increased from very strongly acid in the pine forest and tea plantation to moderately acid in the horticultural crops with fallow and intensive cultivation . Regardless of land use, all soils in this study had low CEC characteristic of acidic Andisols dominated by allophanic materials . The lowest values occurred in the pine forest and the highest values in the horticultural soils. The pHKCl-pHH2O values ranging between −0.1 and −0.5 were indicative of a soil colloidal fraction dominated by variable charge materials . Especially notable is the very low base saturation and concentrations of exchangeable Ca and Mg for the PF and TP soils . Exchangeable base cations are a common limiting factor for horticultural production in the studied Andisols since these nutrient cations are extremely low under pine forest. While the horticultural management practice of applying horse manure and lime did not appreciably increase the measured CEC, it was remarkably effective in increasing exchangeable base cations . For example, exchangeable Ca, Mg and K increased from 1.5, 0.3 and 0.2 cmolc kg−1 in the pine forest to 26.3, 3.5 and 1.0 cmolc kg−1 in the intensive horticultural crops, respectively . The high base saturation of over 100% under horticultural land uses compared to < 23% for the pine forest and tea plantation .Organic C concentration in A horizons was highest in PF and 1.0 to 2.0% lower under agricultural management . In contrast, organic C was lower in the PF subsoil while the agricultural sites had elevated organic C concentrations in several subsoil horizons. Organic C stocks in the upper 100 cm of the soil profile were calculated by summing the organic carbon stocks in each individual horizon were present). Organic carbon stocks followed : TP ≈ IH > FH > PF . The agricultural soils contained more organic carbon than the pine forest soil. While horse manure was added to the IH soil for the past 7 years, the TP and FH soils received no organic matter amendments and still had similar pedon organic matter stocks.

As a direct comparison, the IH soil receiving horse manure contained only slightly more organic C than the FH soil located 4 m away that received no horse manure and was fallowed over the past 7 years. Dissolved organic carbon concentrations were appreciably higher in the PF topsoil and throughout subsoil horizons of the TP profile . The horticultural soils tended to have lower overall DOC concentrations than PF and TP land uses. Total N concentrations followed a similar distribution to organic C concentrations among sites with total N stocks in the upper one meter of soil following : IH > FH ≈ TP > PF . The C:N ratio was lowest in the upper 50 cm of the IH and FH soil profiles , while values for PF, TP and lower soil horizons at all sites were generally in the range 16 to 19. The highest concentrations of inorganic N were found in the IH pedon and were dominated by NO3 – . In contrast to the IH soil dominated by NO3 – , inorganic N concentrations were dominated by NH4 + in the TP, FH and PF soils with the highest value in the TP soil and lowest under FH land use. High P fixation , characteristic of Andisols, was exhibited for all land-use types. Under forest vegetation , the soil P retention was consistent at 97% throughout the entire pedon . Change of land use to TP and FH did not appreciably affect P fixation. However,grow bag the IH land use receiving application of horse manure for the past 7 years showed appreciably lower P fixation in the upper 40 cm. Reflecting the high P fixation, available P content was below the detection limit for all horizons of all land-use types, except for the upper horizons of the IH land use .There were several significant correlations among soil properties . Oxalate-extractable Sio showed a positive correlation with the clay fraction, while Feo had a strong negative correlation with pH and exchangeable Ca and Mg. In contrast, Alo showed no significant correlations with other soil properties. For organo-metal complexes , Alp had highly negative and positive correlations with the clay fraction and organic C, respectively. However, Fep showed no significant correlations with other soil properties. Soil pH showed a highly negative correlation with P retention and Feo, along with a positive correlation with exchangeable cations , total N and Db. Soil bulk density showed a positive correlation with exchangeable cations and negative correlation with P retention. P retention had a negative correlation with exchangeable cations .Andisols are characterized by low Db and high porosity due to the abundance of amorphous and poorly crystalline materials and organic matter that contribute to highly stable and very well structured soils under natural conditions. However, the low natural Db may change due to anthropogenic activities.

The evidence was revealed by soil tillage under intensive horticultural crops contributing to increased Db from compaction by potential destruction of soil aggregates due to physical mixing/abrasion by tillage operations. Tillage was reported to destroy macropore pathways of Andisols in Mexico resulting in a lower in- filtration and permeability of topsoil horizons .Chemically, the exchangeable cations have positive significant correlation with Db, indicating the increase in soil exchangeable cations gave rise to the increased soil bulk density . This is probably due to the role of Ca and Mg ions derived from lime and manure in binding soil particles, resulting in the change of soil friable structure under forest to more compact aggregate formation under intensive horticultural cultivation. The water retention capacity varied from 37 to 53% in topsoil horizons and from 45 to 51% in subsoil horizons with the lowest values in the pine forest . These data indicate that the number of soil pores storing plant-available water is lower in the forest Andisols than those converted for agriculture. In other words, the water retention capacity has increased about 50% following conversion from pine forest to agriculture. This implies that the compaction associated with tillage is responsible for increasing the water retention capacity through conversion of macropores to meso/micropores. The water retention capacity in this study was higher than for cultivated Mexican Andisols reported by Prado et al. . The high water retention in Andisols is caused primarily by their large volume of meso/micropores . Formation of these meso/micropores is greatly enhanced by poorly crystalline materials and soil organic matter . Buytaert et al. studied toposequece of Andisols in south Ecuador and reported the large water storage capacity as revealed by water content ranges from 2.64 g g−1 at saturation, down to 1.24 g g−1 at wilting point. The long-term cultivation of agricultural soils in this study has not caused appreciable degradation to the overall Db, porosity or water retention characteristics of these Andisols. While macroporosity was decreased by tillage, the macropore content of topsoil horizons remained > 15% providing adequate infiltration and soil aeration. The loss of macropores is compensated for by the increase in meso/micropores that contribute to increased plant-available water holding capacity. In spite of the increase of bulk density and loss of macropore capacity, field observations confirmed that the agricultural soils in this study retained their high infiltration capacity with no evidence of surface runoff. In Italy, well developed Andisols on flow-like landslides over 70 years experienced low run off and minimal soil erosion owing to a good infiltration in spite of the high slope steepness and the anthropic pressure associated with land management .The pine forest soil was very strongly acidic owing to the strong leaching regime associated with the isothermic/perudic climatic regime. Applications of lime and more recently horse manure to the IH soil were effective in raising the pH of the horticultural soils . In spite of the low soil pH values in the tea plantation, the potential for Al3+ toxicity was not evident as ascribed to the low exchangeable Al3+ concentrations . Threshold values for Al toxicity are generally considered about 2 cmolc kg−1 for common agricultural crops and 1 cmolc kg−1 for Al-sensitive crops .

In order to enhance agricultural advisory service delivery, there is need to harmonize the efforts of extension providers to more effectively address the intensification of maize and adapt to climate change. The Diffusion of Innovations Theory was developed by Everett M. Rogers to analyze the diffusion and adoption of agricultural innovations among farmers. Considered the theoretical basis for agricultural extension, this theory asserts that the adoption of an agricultural technology communicated through specific channels occurs over time among members of a social network . Under this theory, individuals within the network are categorized according to their ability to adopt the technology. This theory follows the top-down “transfer of technology” model where innovations are developed by researchers, disseminated by extension personnel, and then adopted by farmers. In the context of Malawi, agricultural innovations are developed both by researchers within the hierarchical structure of the public extension system and are co-developed at the grassroots level with input and field testing from farmers . Therefore, it is useful to understand the basic elements of the Diffusion of Innovations Theory to evaluate Malawi’s public extension system, but also analyze the extension system as a network of actors that all develop, share, and improve agricultural innovations and information. To effectively analyze Malawi’s complex,cut flower bucket pluralistic extension system it is useful to develop a conceptual framework that draws from the Diffusion of Innovations Theory as well as Social Network Analysis.

Feed the Future’s Developing Local Extension Capacity project led by Digital Green in partnership with Care International, the International Food Policy Research Institute and the Global Forum for Rural Advisory Services developed a “best fit” framework for analyzing Malawi’s extension and advisory services that target key activities for improving the system . This framework outlines extension parameters and characteristics allowing stakeholders to understand the state of Malawi’s extension system and where critical levers for change exist .Within this framework, the frame conditions outside the manageable interests are meant to show factors that influence extension services in Malawi, but are not the focal area for change within the efforts of this study. The critical factors for change within the manageable interests of this study are the characteristics of AIS including 1) the governance structures which I will analyze using Social Network Analysis to understand the development of content, transfer of information, and engagement between stakeholders; 2) the organizational and management capacities which I will analyze as the capacity for organizations to provide EAS and ways in which organizations are structured; 3) the advisory methods used by providers to deliver EAS; 4) the connection to local and international markets; 5) the livelihood strategies integrated into the provision of EAS; 6) the engagement of community members, villages, and farmers in the process of EAS information development and dissemination; and 7) the performance of EAS with respect to climate change adaptation based on the messages communicated to farmers and the effectiveness of information delivery. GoM has developed a pluralistic extension policy that calls for the delivery of specialized services to farmers by governmental institutions, nongovernmental organizations, and private industry. These extension services support farmers to overcome barriers to increasing crop yields and adapting to changing climatic conditions.

Yet, inconsistent recommendations provided by different extension providers regarding best practices to adapt to climate change have remained significant challenges in Malawi. GoM has emphasized the need to improve coordination among extension providers in order to reduce inefficiency, redundancy, and confusion due to conflicting messages to farmers. In fact, few nongovernmental organizations or private companies coordinate their extension activities with DAES under MoAIWD. In order to improve stakeholder engagement amongst extension providers, communicate clear messages to farmers, and improve the adoption climate adaptation practices, there is a critical need to identify what climate change information is currently being communicated to farmers across the extension system.To meet the objectives of the study, I contacted key informants using the snowball sampling method to identify affiliate organizations and individuals who provide extension services to maize farmers in Malawi. The location for this study was chosen based on the projected severity of climate change impacts to Malawi’s maize production across all regions and high concentration of extension providers that operate throughout the country. I developed a professional network in Malawi that connected me to key organizations and individuals who work on extension. This network included staff from IFPRI where I interned, GFRAAS, MaFAAS, USAID, and faculty specializing in extension from LUANAR in Malawi. My researcher team at the University of California, Davis included my advisor and the CoPrincipal Investigator, Dr. Amanda Crump who has worked on agricultural extension issues in Malawi and an experienced thesis committee who provided additional contacts for this research. In total, 130 individuals were contacted through email and phone calls and 19 agreed to participate in virtual interviews through online platforms for this research study. Interviews were conducted in English over the phone from October 1, 2020 to January 1, 2021 with individuals from organizations who provide extension services to farmers. It should be noted that English if the official language of Malawi and is widely spoken among extension providers.

The table below shows how many individuals from each type of organization participated in the research study through in-depth interviews.Through this research, I sought to assess the challenges and successes in communicating climate change adaption information to maize farmers in Malawi in order to improve the development of content and delivery of information by extension providers. Therefore, I focused on understanding the development and dissemination of information throughout the extension system, messaging around climate risk and variability, curriculum and learning tools extension providers use to educate farmers, and engagement with other organizations that support farmers across the extension system in Malawi. I employed qualitative methods to develop a deeper understanding of the participants’ experiences, perspectives, and relationships which are essential to better understand Malawi’s extension system and the stakeholders involved . The research began with a literature review to understand climate change impacts to agriculture in Malawi, climate change adaptation practices for farmers, and the agricultural extension system in Malawi. This was followed by key informant interviews with a sample of identified organizations using an interview questionnaire. A detailed Interview Questionnaire was developed to administer in-depth interviews with all participants. The Interview Questionnaire introduced participants to the intention, goal, and dissemination of the research. This document also informed all participants that their participation was voluntary and asked if they consented to participating in the research. Participants who opted not to participate were thanked and no further communication was made. All participants were asked if they consented to being recorded on the my phone and all who participated consented to being recorded. In addition, a script was developed to introduce participants to questions and a question route was developed to ensure consistency across all interviews. A semi-structured questionnaire was used because I recognized that a detailed explanation of certain topics might be required, and certain responses could not be anticipated prior to the interview. The questions asked during interviews focused on climate smart agricultural practices shared with farmers, extension approaches, and key constraints in delivering effective climate change information to maize farmers. Specifically,flower display buckets the interviews gathered information about the following themes: organizational activities and partnership, information development and dissemination, educational tools, extension methods, climate smart agricultural practices, and communication successes and challenges . In order to evaluate the validity of the content obtained during interviews, I tested the instruments developed for this study prior to use with several members of my research team at UC Davis and at MaFAAS by phone. This allowed me to understand if the questions were clear and could be answered in the time allotted for the interview. After testing, I made several format and phrasing changes to the Interview Questionnaire to improve the quality of the guide and ensure that participants could understand the questions being asked.

Each interview lasted between 60-90 minutes depending on the participant’s responses and elaborations. In preparation for this study, I sought approval from the Institutional Review Board and Committee on the Use of Human Research Subjects at UC Davis and the Malawi Government through the National Commission for Science and Technology’s Framework for Guidelines for Research in the Social Sciences and Humanities. This process required the submission of a research proposal to be reviewed and approved by the National Commission for Science and Technology in Malawi. In order to comply with Malawi’s requirements, this research was first affiliated with a local research institution; LUANAR. This affiliation was initiated with an extension faculty member of LUANAR, Mr. Paul Fatch, on July 28, 2020. All appropriate forms and documents were provided to UC Davis and the Malawi Government. The UC Davis IRB deemed this study to meet the criteria of exemption on August 24, 2020. I obtained a permit from the Malawi Government on September 29th, 2020 was approved to proceed with all interviews.Qualitative data analysis used for these interviews involved analyzing the relationships between themes in my data in order to understand the phenomena and derive a theory about information generated during the interviews. The data for this research were collected through in-depth interviews with 19 participants who provide extension services to farmers in Malawi. All phone interviews were recorded using my mobile device or computer and were transcribed using Word. After all interviews had been transcribed, each interview was coded to identify common patterns and themes using NVivo. NVivo is a Qualitative Data Analysis computer software package that helps a researcher analyze qualitative data produced during interviews. Using NVivo significantly improves the quality of qualitative research analysis by reducing the number of manual tasks and allowing the researchers to easily discover themes in the data . The data analysis process began by developing a project database in NVivo. The unit of analysis for the study was the “organization.” Therefore, I analyzed patterns, themes, and relationships between organizations instead of the individuals representing those groups. A unique “case” was created for each organization to ensure that the organization’s associated information such as type of organization was linked to them and stored in the NVivo Classification Sheet in order to compare information between organizations. After building the NVivo project structure, coding took place in order to sort the data into meaningful segments. I used both inductive and deductive methods to develop theme codes that were generated both from the theoretical framework used and those that reflected emerging themes present in the data. The words and phrases directly mentioned by the participants were then combined to formulate a connection and relationship between related words or phrases in order to develop broader themes. The model explorer tool in NVivo was then used to visually map the ways in which different themes related to one another in order to derive greater meaning from the data. This analysis was then connected to existing concepts and the theoretical framework used and existing concepts discovered through the literature review. In addition to theme codes, relationship codes were also developed to record the relationships between stakeholders and the transfer of information between organizations. Relationship coding occurred any time an interview participant mentioned information sharing or a partnership between two organizations. Organizational relationships were categorized in three district ways. First, if an interview participant mentioned one organization receiving information from another the relationship was coded as “Organization X receives information from Organization Y.” Second, if an interview participant mentioned information sharing between two organizations, but did not specify which organization developed the information, the relationship was coded as “Organization X shares information with Organization Y.” Finally, if one organization was associated with another, such as the Department of Agricultural Extension Services is part of the Ministry of Agriculture, the relationship was coded as “Organization X is associated with Organization Y.” Relationship coding allowed me to visualize the stakeholder network and the dissemination of information through a Network Sociogram that was exported from NVivo into a data visualization software, Gephi. The Network Sociogram produced through Gephi allowed for the visualization of the complex network of relationships and organizations that are central to information sharing within Malawi’s extension network, and organizations that are not as closely connected to others.Social network analysis was used as the analytical method for understanding and evaluating Malawi’s extension network.

As most of the continental United States got settled toward the end of the 1800s, and land became more scarce and costly, yield-increasing innovations and practices became the major source of increased agricultural output. Cochrane suggests that the quest for higher yields led to the research and extension activities that resulted in the introduction and adoption of chemical fertilizers, pesticides, and improved varieties during the 20th century. The relative scarcity of labor has led to . the development of capital-intensive equipment and practices for the application of new inputs and the continuolls introduction of laborsaving tillage and harvesting technologies in the United States . . Technological change has been largely responsible for the continuous increase in agricultural supply, the increased capital intensity of agriculture, and the growing dependency on chemical inputs. As we approach the end of the 20th century, it seems that agricultural resources and environmental quality are getting more scarce. The increase in the value of these inputs suggests the development and adoption of innovations that will conserve water and reduce soil erosion and pesticide use. Scientific breakthroughs in genetics and biochemistry and a substantial reduction in the computing costs over the last 15 years suggest that many of the developments of the future will rely heavily on the use of biotechnology and computers. The direction of technological change in agriculture should also be affected by changes in macroeconomic conditions and tax laws. The increase in real interest in recent years and the tax reforms of the 1980s, in particular the move away from cash accounting and the treatment of capital gains as ordinary income, are likely to lead to the deemphasis of the development of capital-intensive technologies in agriculture. Nevertheless,cut flower transport bucket it seems likely that technological changes will continue to improve productivity and increase agricultural supply over time.

Agricultural products are basic commodities-essential products which command very high prices when scarce but very low prices as they become abundant. Table 1 presents farm-level demand and income elasticities for major food groups in the United States. It shows that the demand elasticities for major agricultural commodities are less than unitary and, in some cases , very close to zero. The cross-price elasticities of food items are positive, indicating that these commodities are substitutes. Income elasticities of nonmeat items are close to zero and may be even negative . The demand for meats is quite responsive to income, and the income elasticities of chicken and beef are slightly less than one. Wohlgenant’s estimates of income elasticities of the demand for beef and chicken seem to be higher than in other studies. The results of Haidacher et al. suggest that income elasticities of these products are closer to zero than one . Haidacher et al. also find that demand and income elasticity for food quality are quite high, and consumers are ready to pay substantially more for higher quality food. While overall demand elasticities for vegetables are quite low, these demand elastici ties vary throughout the year. Demand and income elasticities for fruits and vegetables are low during their season and become quite high during their off-season . Demand functions for agricultural products in many other developed nations have features similar to those in the United States . It seems that the growth potential of the markets for standard agricultural commodities in developed nations is quite limited, but product quality improvements may increase farmers’ revenues substantially. Mellor argues that developing countries have the potential to provide faster growing markets for agricultural commodities, since demands for these commodities grow in those nations faster than supplies. The rapidly growing nations of Asia provide especially good markets for feed grains and meat products because the food consumption patterns of these countries have not yet stabilized and the income elasticities of their meat demands are quite high. Agricultural systems are subject to much randomness and uncenainty. Much of the randomness results from natural phenomena.

The production of crops depends heavily on weather conditions, and yields vary as rainfall and climatic conditions change from year to year. Pest and disease problems are other contributors to the randomness in agricultural production_ Economic conditions are also contributors to the randomness faced by agriculture through their impacts on inputs’ prices, credit terms, and demand conditions. Prices of agricultural commodities are varying quite substantially over time in response to changes in demand and supply conditions around the globe. There has been much variability in real prices of agricultural inputs over the last 20 years. The prices of many agricultural inputs depend heavily on the price of oil, and the random variations in oil prices destabilized the prices of these inputs. Some inputs are imported, and their prices vary as exchange rates fluctuate. The real prices of credit for short- and long-term agricultural activities have varied in response to economic conditions and government policies. Actually. government activities have been major resources of randomness and uncertainty for the farm sector. Some of the government activities, besides monetary policy, which are likely contributors to randomness and uncertainty facing farmers include the agricultural commodity programs and marketing orders which terms have been varying frequently and sometimes drastically; immigration laws, the minimum wage, and workers’ health and safety regulations; pesticides and environmental quality regulations; and tax policies on both state and national levels. There is a growing body of evidence that farmers are risk averse and are ready to give up some of their average income in return for less variability of the economic conditions they face . The evidence suggests that smaller farmers are more likely to be more adverse to risk 1 than larger ones. Moreover, farmers are especially susceptible to downside risk, and their aim is to reduce it . Many agricultural inputs and activities and institutional regulations and activities aim at reducing randomness and u ncertai nty faced by farmers. Some government policy interventions are also designed to reduce randomness and instability facing producers and consumers. Redesign of such policies should recognize the impacts of public stabilization activities on private storage activities and provide coordination mechanisms for the control of different stocks .

Glenn Johnson coined the term “asset fixity,” and its interpretation has been the subject of much controversy. According to Tweeten , it was originally used to denote situations when gaps between purchase and resale prices of agricultural assets result in fixed asset-use levels under a wide range of prices and in inelastic supply responses. It was also used to denote what Williamson defined as asset specificity, namely, the tendency of many agricultural assets and forms of capital to be specialized and not easily convertible to uses outside the agricultural sector. This rigidity is not restricted to physical assets such as the tomato harvesters or milking barns, etc.; it also applies to different forms of human. capital. Hence, the transition of workers and assets in and out of the agricultural sector is not smooth. Changes in economic conditions-in particular, periods of down scaling and reduction of demand for agriculturally related skills-are likely to result in severe human adjustment problems. The specificity of many agricultural assets and skills cause their value to vary substantially with prices and conditions of agricultural commodities. In spite of the dramatic changes in technology and substantial increases in the sizes of farm operation, the agricultural sector has, on the whole, a competitive structure . Family farms are still probably the dominant form of operation, even though many of them have become businesses grossing several million dollars annually. Structure and behavior seem to be competitive in the production of major field crops, livestock, and dairy products. There is much vertical integration and centralization in the production of poultry and eggs, and there is a substantial amount of venical interaction in the production of some fruits and vegetables. In spite of these cases,procona flower transport containers the competitive model is very useful as a basis for analysis in the farm sector. Competitive behavior has been assllmed in empirical analyses of price determination along the food marketing chain . Rausser, Perloff, and Zusman question this assumption and suggest that contract theory and models of noncompetitive behavior are more appropriate for modeling the input markets to the assembly, processing, and distribution components of the food marketing chain. The nature of the products and the prevalence of long-term contracts in these markets led to rather fixed prices for processing and handling components of food items. This rigidity of response to change in economic conditions is in contrast to the flexibility of farm products which are produced by competitive markets. Agriculture, like many other sectors of the economy, frequently faces imperfect credit markets. In particular, bankers use other signals besides interest rates to allocate credit, so that not all the demand for funds at a given interest rate is met, and some of the better investment projects may not be financed. It has been argued that credit market imperfections are the results of lack of perfect information on behalf of the lenders. Banks may not flawlessly discriminate between loan requests, and they have developed several mechanisms to assist them in screening applications and insuring repayments although these devices have their faults.

Collateral financing has been used in many agricultural investments that might have caused discrimination against individuals with small landholdings with worthwhile worthy projects. Moreover, instability of prices and income has affected the ability of farmers to borrow and invest. Credit is likely to be more easily available in periods of agricultural prosperity than agricultural recession, thereby hampering the ability of the farm sector to withstand hostile environments. The growing reliance on debt-service financing in the agricultural sector in the middle 1980s may reduce some of the inequities and inefficiencies that are associated with collateral inactivity. But even with debt-service financing, credit markets are far from perfect. Credit availability constraints are likely to limit farmers’ ability to adapt to and survive stricter policy regulations. Agricultural production is the outcome of an interaction between human activities and natural resources and the physical environment. Such activities involve the deployment of resources that are exhaustible or have a slow renewal rate. Topsoil, groundwater, and water quality are obvious examples of such agricultural resources. Hueth and Regev argue that pest vulnerability to pesticides is / another exhaustible resource that has to be preserved. The argumentation as to the ,likelihood of the greenhouse effect suggests that some view even temperate weather as an exhaustible resource. In any case, heavy dependence on the use of chemical inputs, groundwater, and soil-eroding practices is causing depletion of exhaustible agricultural resources and is likely to reduce the productive capacity of the agricultural sector in the long run . Agricultural activities are also the causes of environmental externality problems. Agricultural runoff and seepage of agricultural chemicals contaminate bodies of water, reducing their value as sources of drinking water as well as fishing and recreation sites. Straw burning and intensive tillage practices pollute air resources and reduce air quality. These externality problems must be taken into account in the designing of policies that affect the agricultural sector. The externalities, and particularly the resource exhaustibility problems associated with agricultural production systems, are becoming increasingly severe over time. For example, the intensive use of center-pivot irrigation of the last 20 years has led to substantial depletion of the OjalIala aquifer, leading to curtailment of irrigation activities in some parts of Texas, the High Plains, and Oklahoma . In the literature on exhaustible resources , it is argued that, unless rates of technological change are extremely high, the efficiency prices of exhaustible resources tend to increase over time. Moreover, free market prices of exhaustible resources may diverge substantially from their efficiency prices, and government intervention may be needed to assure efficient utilization of these resources. Social management of natural systems involving exhaustible and slow-to-renew resources such as forests and fish populations requires resource dynamic considerations to be incorporated explicitly into policy making frameworks. It seems that such considerations will call increasing weight in the management of agriculture in the future. There are many differences in environmental conditions, economic situations, and productivities between regions in the United States. The qualities of natural resources such as water and soil are the subject of much heterogeneity. human capital, wealth, and preference vary substantially among farmers.

We additionally profiled the root transcriptomes of 1-month-old tomato plants under well-watered, waterlogged and water-deficit conditions. We hypothesized that genes directly involved in the biosynthesis and deposition of suberin will be highly correlated in both water-deficit and the introgression line population. By combining both introgression lines, waterlogging and water-deficit datasets in a weighted gene correlation network analysis3, we identified modules of co-expressed genes . A module containing 180 genes was significantly enriched in suberin-related genes . This was confirmed by intersection with a public dataset profiling gene expression in tomato DCRi lines . DCRi lines activate suberin-associated genes in the epidermal cells of fruit, which leads to suberization of the fruit surface. The ‘royalblue’ module contains several orthologues of well-known suberin biosynthetic gene families such as glycerol-3-phosphate acyltransferases , 3-ketoacyl-CoA synthases and feruloyl transferases . In addition, putative tomato orthologues of known transcriptional regulators of suberin biosynthesis: AtMYB41, AtMYB63 and AtMYB92 , among others, were found in this module.Although translatome profiles exist for the exodermis, these data do not provide resolution of the developmental gradient along which suberin is deposited. To refine the candidate suberin-associated gene set, we conducted single-cell transcriptome profiling of the tomato root. We used the 10X Genomics scRNA-seq platform to profile over 20,000 root cells. We collected tissue from 7-day-old primary roots of tomato seedlings up to 3 cm from the tip to include the region where suberin deposition is initially observed. Gene expression matrices were generated using cellranger and analysed in Seurat. Once the data were pre-processed and filtered for low-quality droplets,25 liter pot plastic the remaining high-quality transcriptomes of 22,207 cells were analysed.

After normalization, we used unsupervised clustering to identify distinct cell populations . These cell clusters were then assigned a cell type identity using the following approaches: We first quantified the overlap with existing cell type-enriched transcript sets from the tomato root and marker genes extracted from each of the clusters. An individual cluster was annotated as a specific cell type given the greatest overlap between the two sets and a significant adjusted P value . Then, to map gene expression dynamics across maturation, we examined cell-state progression by calculating pseudotime trajectories using a minimal spanning tree algorithm. The tree was rooted in the root meristematic zone , and clusters were grouped into 10 cell types to reflect existing biological knowledge on differentiation of the tomato root . Lastly, genes with previously validated expression patterns in tomato, transcriptional reporters and predicted cell type markers given their function in Arabidopsis, were overlaid on the clusters to refine annotation . Given the successful annotation of these cell types, we focused on the mapped developmental trajectories deriving from a presumedcortex–endodermal–exodermal initial population . Given the suggested link between suberin and drought tolerance, as well as the decreased suberin levels in both control and ABA conditions in our tomato mutants, we hypothesized that the slmyb92 and slasft lines would be more sensitive to water limitation compared with wild-type plants. We subjected 4-week-old well-watered plants to 10 days of water-deficit conditions . Suberin deposition and monomer levels were studied in the root system of slmyb92-1, slasft-1 and wild-type plants in both the water-sufficient and water-limited conditions. Under water-sufficient conditions, suberin deposition was only faintly observed in wild type, and exclusively in the exodermis, while being completely absent in both mutant lines . Consistent with this observation, very low levels of suberin monomers were detected, with no significant differences observed in the very long chain fatty acids, primary alcohols, ω-hydroxyacids, α-ω-dicarboxylic acids and aromatic components of suberin .

Under water limitation, however, deposition of exodermal suberin was increased, with both mutant lines having lower levels than wild type . The transcriptional regulator mutant slmyb92-1 showed a general reduction of most monomer groups compared with wild type. The slasft-1 mutant, in comparison, was primarily depleted in ferulic acid and its esterification substrates, as well as in individual primary alcohols and ω-hydroxyacids . Furthermore, stem water potential, stomatal conductance and transpiration rate were significantly decreased in response to water-limited conditions in both slmyb92-1 and slasft-1 relative to wild type, and leaf relative water content was also decreased in slmyb92-1 . When considering all physiological traits collectively using principal component analysis, slasft-1 showed a milder water-deficit response compared with wild-type plants, while slmyb92-1 was more extreme . These data demonstrate that decreased suberin levels in the tomato root exodermis directly perturb whole-plant performance under water-limited, but not under water-sufficient conditions. Furthermore, changes in specific suberin monomers and the lamellar structure that were observed between the two mutants in response to water-limited conditions may differently influence the extent of the physiological response.In the well-characterized Arabidopsis root endodermis, suberin is deposited as a hydrophobic layer between the plasma membrane and the primary cell wall5 . Developmentally, suberin biosynthesis and deposition occurs as a second step of endodermal differentiation, the first being the synthesis and deposition of the lignified Casparian strip. Suberin serves as an apoplastic barrier and a transcellular barrier, thus contributing to the regulation of the movement of water and solutes to the vascular cylinder.

Our collective observations demonstrate that, relative to Arabidopsis, the components of the pathway are conserved; their spatial localization is distinct; ASFT and MYB92 are critical regulators of suberin biosynthesis given their phenotypes as single loss-of-function mutant alleles, as opposed to their redundancy in Arabidopsis and exodermal suberin has equivalent function to endodermal suberin and can function in its absence . Spatially, in the tomato root exodermis, suberin lamellae are deposited between the exodermal primary cell wall and the plasma membrane all around the cell, similar to the Arabidopsis root endodermis and other suberized apoplastic barriers such as the potato periderm . In a temporally similar fashion to the Arabidopsis endodermis, there is a non-suberized zone at the root tip, a patchy suberized zone in the middle of the root and a continuous suberized zone nearer to the root– hypocotyl junction . We obtained clues to the underlying genes controlling exodermal suberin biosynthesis over developmental time by co-expression, single-cell transcriptome and genetic analyses. Conservation of the genes within the suberin biosynthetic pathway between Arabidopsis and tomato was evident from the functional genetic analysis of SlCYP86B, SlGPAT, SlLACS and SlASFT mutants. Despite the same genes controlling suberin biosynthesis, novelty in tomato is observed with respect to their tomato spatial expression and the critical contribution of SlASFT in primary cell wall attachment and inter-lamella adhesion of the suberin barrier. This phenotype has never been observed in Arabidopsis or potato asft mutant roots. In addition, members of the GPAT4 subclade have been regarded as exclusively involved in cutin biosynthesis, and here, SlGPAT4 was shown to participate in the formation of exodermal suberin . We focused on SlMYB92 as a candidate due to its expression at the end of the exodermal trajectory. Although the precise timing of these trajectories is largely predictive in nature,25 litre plant pot we note that the expression of the biosynthetic enzymes does not completely overlap that of SlMYB92 and suggests that SlMYB92 is not the sole transcriptional regulator of suberin gene expression. Our ability to obtain increasingly differentiated exodermal cells is probably limited by our ability to completely protoplast cells with secondary cell wall deposition. Therefore, the lack of SlMYB41/53/93 expression in the exodermal trajectory does not mean that these genes are not expressed in the exodermis. In Arabidopsis, single loss-of-function mutants of MYB41, MYB53 and MYB93 show no changes in suberin levels, while that of MYB92 shows a delay in suberization. By contrast, this extreme phenotype and compositional profiling in hairy roots and stable lines was observed in tomato when only MYB92 was mutated. The residual suberin levels found in the slmyb92 mutants could be regulated by other MYB transcription factors. Indeed, mutants in tomato orthologues of Arabidopsis MYB41 and MYB63 showed exodermal suberin phenotypes , suggesting that these genes may be expressed in later exodermal developmental stages. ABA-mediated regulation of tomato exodermal suberization is morphologically consistent with what is observed in the Arabidopsis root endodermis, with an increase in both the magnitude of suberin deposition and the proportion of the completely suberized zone, despite the distinct spatial localization.

At least in the case of the slmyb92 and slasft mutant alleles and the ABA assays, this transcription factor and biosynthetic enzyme influence both developmental and ABA-mediated suberin deposition patterns . Further analyses of mutant alleles of the tomato SlMYB41 and SlMYB62 transcription factors will determine whether a coordinated developmental and stress-inducible regulation of suberin biosynthesis is the norm for exodermal suberin. The degree to which this regulation is dependent on ABA signalling, as it is in Arabidopsis , also remains to be investigated. What remains to be identified, however, are the factors or regulatory elements that determine exodermal-specific regulation of these enzymes and transcriptional regulators, as well as how they are activated by ABA and why their activity is ABA-independent in S. pennellii. External application of ABA can be considered a proxy for both drought and salt-stress response. We tested the necessity of suberized exodermis for whole-plant performance under water-limited conditions in mature tomato plants . The strongly reduced response of slmyb92 and slasft plants to ABA was similarly observed upon drought stress. In both experiments, slmyb92-1 and slasft-1 failed to reach fluorol yellow signals and suberin levels equal to those of the control. Under control conditions , we detected overall low suberin levels, which were near the detection limit of 0.003 µg mg−1 and reduced our ability to identify significant differences between the lines. This was consistent with the lack of distinct fluorol yellow signal in mature root sections under water-sufficient conditions . The effect observed in chemical suberin quantification may have also been attenuated by the sample comprising whole root systems with highly branched lateral roots and including root areas with immature suberin. AtMYB92 is also known to regulate lateral root development in Arabidopsis together with its close orthologue AtMYB93, and differences in suberin within different root types are a possibility. Regardless, suberin monomeric levels were clearly decreased in the slmyb92-1 and slasft-1 mutants in a distinct and overlapping fashion in response to water-limited conditions. Consistent with its function, slasft-1 was primarily defective in accumulation of ferulate, primary alcohols and ω-hydroxyacids , while slmyb92-1 had defects in fatty acids and the predominant unsaturated C18:1 ω-hydroxyacids and dicarboxylic acids . The more extreme perturbation of physiological responses in response to water limitation in slmyb92-1 suggests that suberin composed of these fatty acid derivatives plays a role in controlling transcellular-mediated uptake of water . How the transcellular pathway operates in a root system where this apoplastic barrier is located four cell layers from the vascular cylinder remains an important and open question. The role of exodermal suberin as an apoplastic barrier to water flow has been studied in maize and rice, where it was determined as a barrier to water flow, although maize and rice also present a suberized endodermis. Thus, the role of exodermal suberin alone has never been studied with respect to its influence on plant responses to water limitation. The precise role of endodermal suberin, independent of the Casparian strip, has been studied in Arabidopsis, which lacks an exodermis. In 21-day-old, hydroponically grown Arabidopsis plants, the horst-1, horst-2, horst-1 ralph-1 and pCASP1:CDEF1 mutants with a functional Casparian strip but with reduced suberin were monitoredfor the importance of suberin in water relations. These mutants, except for horst-2, have higher Lpr and root aquaporin activity relative to wild type. One can extrapolate that the decreases in stem water potential, transpiration and stomatal conductance relative to wild type in water-limited conditions are a consequence of decreased suberin or perturbations in suberin composition . Assuming that our suberin-defective mutants have higher root hydraulic conductivity, our hypothesis to reconcile our observations with the higher Lpr would be that our mutants have compromised water-use efficiency under water limitation. This could lead to a delayed onset of the drought response such that the water loss is too great to recover by the time stomata are closed. The mechanisms by which this occurs need to be determined and could benefit from further exploration. The levels of lignin in the exodermis and endodermis were not altered in the mutants of the identified transcriptional regulators , and perturbations in endodermal lignin alone have no influence on root hydraulic conductivity in Arabidopsis, thus, lignin plays no role in our observations.

To evaluate whether the availability of land use types influenced bee abundance and diversity, we ran Pearson’s chi-square tests of independence. We tested whether the frequency of occurrence of each land use type across study sites was associated with the number of 1) captured bees, 2) abundance of the three most common bee tribes, 3) bee genera, and 4) bee tribes. All analyses were conducted in R. We present evidence for the effects of different local and landscape factors on bee abundance and diversity in the Colombian Andes, a region for which this type of studies are scarce. Bee abundance and diversity were influenced by several habitat factors including flower availability, elevation, and unshaded crop cover within 1 km. Contrary to our hypotheses, bee abundance decreased, although diversity increased in farms with higher habitat complexity, and both local and landscape factors greatly influenced bee community composition. Our first research question examined which local and landscape factors influenced bee abundance and diversity. In general, bee abundance was predicted by flower abundance and elevation. Our results coincide with other studies documenting a positive response of bee density to floral resources , and the unresponsiveness of some groups such as Meliponini to flower availability . We found overall bee abundance was greatly influenced by the abundance of Apis and Trigona bees in areas with high flower abundance. Other studies have documented the positive responses of Apis to the spatial aggregation of floral resources and mass-flowering crops , and have suggested Apis can exclude other bees from accessing flowers through interference or exploitative competition . Like Apis, Meliponini are social and generalist bees. While there is niche overlap among Meliponini species,grow raspberries in a pot and between Meliponini and Apis bees, there is resource partitioning among Meliponini but not between Meliponini and Apis . Thus, a possible explanation for the differential influence of flower abundance on different bee groups may be mediated by their competitive interactions with Apis.

Other factors influencing bee abundance may be related to traits specific to bee groups. For example, Augochlorini abundance decreased with pasture cover within 1 km and increased with % vegetation between 1-3 m. In our study, percent of vegetation cover between 1-3 m was negatively correlated with the percent of vegetation between 0-1 m, which is the strata at which we found more flowering herbs in unshaded land uses. Further, pasture cover within 1 km was negatively correlated with forest or agroforest cover within 1 km. In general, Augochlorini are associated to forest areas , are soil or wood nesters, and use more flowering herbs and vines as feeding resources in comparison with eusocial bees . Thus, use of nesting and food resources by this tribe may explain their abundance patterns. Elevation had a strong influence on the abundance of Apis and Trigona bees. Elevation may act as a filter structuring biological communities because of its inverse relationship to temperature, which may influence species distributions based on their tolerance to cold environments and to climatic fluctuations, among other biophysical variables . Apis and native Trigona cf. amalthea and T. cf. fulviventris have broad altitudinal and geographic ranges as well as high reproductive capacities with colonies of ~10000 and >2500 individuals respectively . The combination of these two factors may explain the high abundance of these genera in our study region, yet it still does not totally explain their positive response to high elevations. It is interesting to consider that, when we excluded these hyperabundant genera, bee abundance was not responsive to elevation. This suggests other mechanisms that favor some groups and undermine others may be at play in the highlands of this region. Bee richness and evenness were also predicted by elevation. We found a strong gradient of bee richness within a relatively narrow altitudinal range , which is considered a hump in general altitudinal-diversity gradients . Potts et al. found richness of bee taxa decreased with elevation in the Alps due to thermal tolerance of bees found along the elevation gradient.

This is consistent with the richness gradient we found, which may also be influenced by the response of different groups to other conditions changing with elevation such as agricultural disturbance. In our study region elevation was positively correlated with the percent of eroded soils at the landscape scale , and the number of land use units on pasture and conventionally managed crops increased with elevation. This suggests rates of disturbance increase with elevation, possibly acting as a strong environmental filter excluding species associated with forests and complex habitats and favoring species with high tolerance to disturbance. This may explain the reduction of rare species and the high presence of Apis and Trigona bees in high elevations, which is reflected in an inverse relationship between evenness and elevation. Bee richness and evenness decreased and dominance increased with unshaded crop cover at the landscape scale. Thus, our results suggest that changes in dominance within communities are associated with the availability of complex habitat at the landscape scale, also found in other studies . Changes in dominance may be associated with the negative responses of rare solitary species to landscape simplification , and with the ability of some groups to equally use complex or simplified habitats. Apis and T. spinipes, closely related to T. cf. amalthea, have been considered hyper-generalist species that do not show negative responses to environmental disturbance and occupy simplified lands , thereby increasing their abundance within the community in areas unfavorable for other bees . Thus, although we cannot differentiate effects of elevation, agricultural disturbance and landscape simplification, these factors and their interaction may sort bee groups in and out of local bee assemblages in this region.

Vertical structure of the vegetation and flower abundance influenced bee richness as well. The stratum at which we found bees was 0-3 m, thus bees at higher strata were not sampled and these results may reflect our sampling bias. However, a study conducted along an ecological succession gradient using different sampling methods reported bee richness and abundance increased in low vegetational strata, and decreased in areas with dense canopy cover . In general, dense canopy reduces sunlight reaching the understory, which in turn influences flowering of herbs . Bee activity may follow the distribution of flowers in the vertical strata , which may explain our results. Our second research question addressed changes in bee community composition. Changes in the composition of bee communities were influenced by elevation and flower abundance. This is consistent with the elevation-richness gradient we found, and may be explained by the degree of specialization and distribution of different bees in our study region. Specialization of plant-pollinator interactions declines with increasing elevation , and biological groups in mountainous regions have narrower ecological niches and altitudinal distributions as elevation decreases . The degree of specialization along elevation gradients may also interact with negative effects of disturbance, thus the changes in community composition in our results may indicate either genera turnover or differential loss of species along the altitudinal range. This has additional implications in light of climate change. Studies have shown increasing temperatures and their influence on thermal tolerance have driven range shifts of different organisms towards higher latitudes and elevation . However, shifts do not only depend on physiological responses but are conditioned on species interactions such as competition and mutualism, and on variation in dispersal abilities . Further studies could target range shifts of bee communities in Anolaima, where environmental change and dominance of highly competitive species are concentrated in areas with higher elevation, possibly influencing range shifts. Our third research question evaluated whether the availability of different land uses was associated with differences in the diversity and abundance of bee genera and tribes. The influence of local and landscape habitat factors on bee abundance and diversity can be linked to current land uses in the region. In general,best grow pots abundance and richness was higher in low-impact land uses, and in areas associated with human constructions. Unshaded traditional crops and fallow lands can have high floral abundance and diversity . Thus, plant richness may beget bee richness in these land uses. Also, we found nests of different groups in human constructions, and foraging on flower and medicinal gardens and on forbs surrounding houses.

Constructions offer areas with favorable features for nest thermoregulation and unmanaged flowering plants may represent continuous floral resources. Thus, human resources may have inadvertent yet important positive impacts on bee populations. We did not find high bee abundance or richness in habitats with high structural complexity i.e. forest or agroforests. Most areas in this land use correspond to shaded coffee crops. In this region, farmers manually exclude forbs that grow despite the low incidence of light into the understory, and rain-fed coffee shrubs typically bloom synchronously only during two or three days a year. This means the understory of this land use does not offer a continuous availability of floral resources for bees. When flowering, trees may offer feeding resources at the canopy level. Tree availability positively influences bee diversity and the long-term availability of flowering trees positively influences abundance of solitary bees in shaded coffee agroforests in Mexico . Some solitary bees are associated with high forest strata, even at the end of the flowering season, perhaps due to supplemental floral resources provided by honeydew and sap from the canopy . Besides food sources, trees also offer nesting resources. Nates et al. found living trees were the most frequent nesting substrate for stingless bees in eastern Colombia. We frequently found Meliponini nests in Inga trees or in abandoned bird nests in citrus trees found in shaded coffee areas. Other studies have found Augochlorini nest in wood and even in bromeliads growing on trees . Therefore, despite the under story of agroforests is not greatly used by bees, the canopy may offer important resources for bees in Anolaima. However, a dense canopy or the presence of high flowering resources distributed across the landscape may influence negatively the local provision of pollination services to coffee shrubs . We also found evidence for the negative effect of conventional crops for bee richness, despite their smaller scales when compared to other agricultural landscapes. Conventional crops in this region are both monocrops and polycrops managed with high agrochemical use . For example, chlorpyrifos and neonicotinoids are systematically used twice a week on tomato for protection from white flies. In extreme cases, application mixtures also include antibiotics to treat cattle from Dermatobia flies. Apis and Trigona were using floral resources in these crops. This suggests bees have different degrees of tolerance to chemical disturbance. For example, while Apis bees and the stingless bee Partamona helleri have different resistance to certain pesticides, both are negatively impacted by mixes of biocides . Thus, species less sensitive to pesticides can thrive in areas with high-impact management, subsidizing the pollination of plants where other bees cannot tolerate agrochemical disturbance. However, areas with high agrochemical disturbance represent a sink for rare bee species yet also a potential threat for bees with relative high resistance to agrochemicals in Anolaima. Different local and landscape factors influence bee abundance and diversity in Anolaima. Some factors were associated with the increase in abundance of two hypergeneralist groups, Apis and Trigona, and with reductions in the representation of rare species, reflected in changes in evenness and dominance within local bee assemblages. This suggests a process of biotic homogenization with the loss of some species and the spread of others, especially in high-elevation areas. In addition, we found that factors that may directly affect bee physiology, such as elevation, interact with resource availability and space to influence the composition of bee communities. We found that land use types such as unshaded conventional crops have negative impacts on bee abundance and diversity, despite the high heterogeneity of agroecosystems in the study region, and that other land uses such as pastures may not benefit some bee groups but are not adverse for others. This suggests bee communities are highly responsive to agricultural management in small-scale farming systems. Our study calls for attention to assess the effect of environmental change on bee communities in mountainous regions where climate change may influence elevational range shifts, such as in the Colombian Andes.

We estimated a total of 19.9 and 21.8 tera-liter of water consumption via evapotranspiration over the agricultural land in California’s Central Valley in 2014 and 2016 water years , respectively . In the water year 2014, the top eight crop types accounted for 75% of total crop consumptive water use in the valley, including almond , rice , grapes , alfalfa , corn , walnuts , pistachios , and tomatoes . Overall, rice was the second largest water consumer after almonds, although it only used 7.6% of cropland, due to its highest annual evapotranspiration rate of 1,109 mm yr−1, on a per unit area basis . Pasture, walnut, almonds, citrus, and alfalfa also had relatively high evapotranspiration rate  . In contrast, wheat consumed the least amount of water per area among major crops, whereas pistachio, tomato, corn, cotton, and grapes had a moderate evapotranspiration rate between 600 and 800 mm yr−1. As the second and third largest cropland use in the valley, grapes, and corn used less total water than rice but similar water with alfalfa, walnuts, and fruits.Across the valley, the mean annual evapotranspiration rate varied by 35% over all agricultural pixels in 2014, mostly due to the diversity of the crop types. We also found high ariability of evapotranspiration rate within each crop type, especially over orchards such as almond, pistachios, and walnut, with a CV higher than 20% , most likely due to differences in planting density, age, canopy structures, and stressors among orchards . For example, the almond evapotranspiration rate varied by 34% , and the rate for pistachio varied by 59% in 2014. Wheat also had a very high variability , different from other annual crops, which typically had a much lower variation of evapotranspiration rate than perennial crops. For all major crop categories, the difference in CV between 2014 and 2016 was <7.2%. Compared to 2014, total crop consumptive water use increased by 9.6% in 2016 , with an evapotranspiration rate of 856 mm yr−1,containers size for raspberries although the reference evapotranspiration from Spatial-CIMIS decreased by 4%. This increase in evapotranspiration was mostly caused by land-use changes with higher irrigated areas and crops with higher averaged water consumptive use .

Total irrigated agriculture land use increased by 7.0% in 2016, partly due to a 2,370 km2 land-use conversion from fallow/ idle lands in 2014 to cropland in 2016. A large portion of fallow land conversion grew rice , wheat , and perennial crops in 2016, leading to an increase of total water use by 1.3 tera-liters. Another major land-use change was the conversion from annual crops to high water demand orchards, including almonds, walnuts, citrus, or grape in 2016, accounting for 1.5% of 2016 cropland and decreasing water use by 0.07 tera-liters due to the low evapotranspiration rate of young orchards.Variability of evapotranspiration rate among GSAs was primarily driven by non-meteorological drivers. Across GSAs, we found that the evapotranspiration rate highly correlated with net radiation and actual Priestley-Taylor coefficient . Many of these factors were regulated by land-use types, vegetation cover, and plant water stress status. In contrast, EToF was the dominant driver of evapotranspiration rate variability among GSAs , mostly driven by crop types, e.g., rice with EToF of 0.61 , tomato 0.33 , almond 0.52 , and pistachio 0.37 summarized at the GSA scale. Even for the same crop type, EToF varied significantly among GSAs for some tree crops and wheat 0.36 . The average almond EToF , e.g., ranged from 0.25 in the City of Tracy GSA in Tracy county to 0.75 in Rock Creek Reclamation District GSA in Chico county. Pistachio’s EToF was much lower in the majority of the western San Joaquin Valley areas , probably due to the plant stress caused by salinity . Citrus EToF had an IQR of 0.16 at the GSA scale. In contrast, the mean EToF showed much smaller variation among GSAs for the majority of annual crops such as alfalfa 0.54 , rice 0.61 , pasture 0.57 , and cotton 0.35 . Within each GSAs, the annual EToF also showed large spatial variation, with a mean CV of 31% across all agricultural fields; Some GSAs with a lower evapotranspiration rate had the highest variability , mostly located at and around the Westlands Water District region in the western-Fresno and Kings county.

In addition to crop diversity within each GSA, significant variation of EToF was also found for each tree crop type, such as almonds and pistachios , with IQRs of CVs among GSAs greater than 15%. For examples, the CVs of pistachio EToF within each GSAs had a mean of 34% and an IQR of 33% across GSAs, with the largest within-GSA variation found in the Central Delta-Mendota GSA; In the Southeast Kings GSA, CV of Pistachio EToF is 29%, much lower than its neighbor, Tri-County Water Authority GSA . Other types with highly variable EToF included almond, citrus, walnut, and wheat. In contrast, EToF was more homogeneous within GSA for alfalfa with a mean CV of 17% and IQR of 6% , and rice . About 39 GSAs had >60% of agricultural land areas planted with perennial crops including almond, pistachio, citrus, walnut, and grape in 2014, which accounted for 76% of total agricultural water use by these GSAs and 27% of Central Valley’s total agricultural water use in 2014 . These GSAs will likely face greater vulnerability to prolonged drought due to the high cost of fallowing productive orchards. When dividing the total consumptive use of perennial crops by the GSA area , we found that some small and medium-size GSAs, such as Delano-Earlimart Irrigation District GSA, Madera Water District GSA, and New Stone Water District GSA, will need to reserve a much greater depth of groundwater storage to maintain the orchards during drought.Our study showed that the semiempirical Priestley-Taylor algorithm, when calibrated with ground measurement data over diverse crop types and driven by Landsat Analysis Ready Data, improved the accuracy of the older 1 km MODIS-driven PT-0 model . The crop-specific Priestley-Taylor optimization performed consistently between the testing and independent data sets, and slightly better than the PT-JPL method . The generalized Priestley-Taylor optimization had a similar overall performance with PT-JPL when driven by the same input data. However, relatively larger uncertainties were found during nongrowing seasons, from November to March, when the evapotranspiration rate was relatively low. This was partly due to the limited field measurements data during winter and early spring for optimizing the sensitivity of actual Priestley-Taylor coefficients to the moisture content.

Moreover, our Priestley-Taylor approach does not separate soil evaporation and plant transpiration. This introduces uncertainty in evapotranspiration estimates during non-growing seasons when evapotranspiration is mainly driven by evaporation from the soil due to minimal canopy coverage or leaf area. For example, we did find that PT-JPL better captured the peak of the actual Priestley-Taylor coefficient for the corn site during the dormant season , when PT-JPL’s estimates showed that soil evaporation was the most significant component. The uncertainty of our refined Priestley-Taylor approach here is similar to the DisALEXI model, as shown by the report from the Sacramento-San Joaquin Delta intercomparison project . For additional reference, Anderson et al. reported that DisALEXI had an RMSE of 1.09 mm day−1 at site number 1 and 1.24 mm day−1 at site number 24 when compared to daily measurements. Being a process-based model, DisALEXI does not depend on land-use maps and field measurements for calibration once validated. The semiempirical Priestley-Taylor approach, however, has the advantage of easy implementation,big plastic pots compared to other more sophisticated and computationally more expensive approaches.At a regional scale, the annual mean values of per-area water use of major crop types in the Central Valley estimated here are generally within the ranges reported in the literature . For example, DWR’s water portfolio and balances data set, as part of DWR’s 2018 Water Plan, reports that water requirement by corn ranges from 390 to 835 mm yr−1 in 2014 across sub-regions of all planning areas in the Central Valley . Burt et al. estimated that corn in the Central Valley conventionally used 813 mm yr−1 in a typical precipitation year. Our regional average of corn evapotranspiration was 16% more than DWR’s average corn water requirement over planning areas . Larger differences were found for alfalfa, pasture, wheat, almonds, pistachio, and vineyard, for which our regional averages were 30%–65% lower or higher than DWR’s values. Over all 30-m pixels of agricultural lands in Central Valley , the average annual evapotranspiration rate, estimated here, is higher than the estimates over the whole Central Valley by the BESS biophysical process-based model forced with 1 km satellite observations . The discrepancy is likely due to the scale effect and differences in land cover maps. Larger pixels likely contain other land-use areas such as fallow, urban, water, and natural vegetation. Nonetheless, our estimates in 2014 align with the values reported in Schauer and Senay based on the SSEBop remote sensing evapotranspiration model driven by Landsat thermal data. Our estimation of 19.6 tera-liter water consumption in 2014 was equivalent to 74% of DWR’s estimate over all planning areas within the Central Valley, which was derived from CalSIMETAW . Among the planning areas, the discrepancies ranged from –53.4% to −18.5%, with the most significant disagreement occurring in the southern and center-east of the San Joaquin Valley .

Similarly, previous studies in the Sacramento-San Joaquin Delta, a subset of the Central Valley, showed that remote sensing estimates are lower than CALSIMETAW’s estimates by 6–24% . Over this Delta area, our crop-specific Priestley-Taylor method in this study estimated 1.20 tera-liter in 2016, very similar to the DISALEXI’s estimate of 1.16 tera-liter in the water year 2016; both were about 80% of CalSIMETAW’s estimates of 1.49 tera-liters, based on the published data summary table in Medellín-Azuara et al. . Two factors may have caused the discrepancy in regional estimates between PT-UCD and CalSIMETAW. First, CalSIMETAW’s crop-coefficient approach implemented at a regional scale may overestimate actual evapotranspiration, because it did not account for the impacts of planting variabilities such as orchard age distribution and planting density, field conditions such as salinity and disease, and crop management like deficit irrigation. Second, the land-use map used by CALSIMETAW was different from the DWR’s land-use map that we used here. For example, CALSIMETAW estimated 13.7, 23.9, and 23.9 km2 of corn, alfalfa, and pasture in PA 704 in 2014, in contrast to our DWR’s map-based estimates of 18.2, 21.4, and 9.3 km2 of corn, alfalfa, and pasture.Currently, California’s GSAs employ various approaches to estimate evapotranspiration in their water budget accounting and management plan development, causing systematic inconsistencies among GSAs. For example, the Olcese GSA near Bakersfield estimates monthly evapotranspiration from 1993 to 2015 using the METRIC method version by the Irrigation Training & Research Center at the California Polytechnic State University; North Kings GSA uses CA DWR’s crop coefficients to estimate annual evapotranspiration rate over detailed analysis units from 1998 to 2010, while the Delano and Yuba GSAs use crop coefficients published by ITRC in 2003 and derived from an SEBAL-based evapotranspiration map in 2009, respectively. Our study shows that the fractional of reference ET , or similarly crop coefficients, for most crops, varies spatially across and even within GSAs, and for some crops, EToF changes considerably between years. More consistent estimates with known uncertainty from a calibrated or thoroughly evaluated approach are needed to ensure consistent quantitative information for data-driven decisions for water planning. Our optimized Priestley-Taylor approach driven by remote sensing observations provides an efficient way to capture both spatial heterogeneity and temporal dynamics of water balance. In particular, we found that orchards and wheat generally had a greater spatial variability of evapotranspiration and crop coefficients than other major crop types, across the Central Valley, within, and among GSAs. Age distribution and other stressors such as salinity likely contributed to such evapotranspiration variability for tree crops . Among three major nut tree crops, pistachio had the lowest mean annual evapotranspiration rate , followed by walnut and almond . Coincidentally, 26% of pistachio acreages in 2014%, 18% of walnut in 2015%, and 15% of almond in 2014 across California were non-bearing orchards . The high variability of wheat water use is likely due to cultivar and end-use for the crop .

To date, few MAR site suitability studies have conducted a sensitivity analysis or validation of recommended sites . Previous MAR suitability assessment studies have used indirect methods to validate MAR locations , while few have used numerical models and in situ observations . With this study, we propose to guide selection of suitable MAR sites by ensuring quantifiable benefits to groundwater levels, storage, water quality, and land subsidence. Although water management agencies maintain multiple MAR basins in the southern CV, most of these facilities have not been implemented to benefit the domestic water supply to rural communities. The Tulare Irrigation District has a 42 ha MAR basin located south of the Okieville community that has been operational since the 1940 . The recharge basin overlays the capture zone of the community’s southern groundwater wells. Its location was accurately identified by this study as suitable Ag-MAR location . Data from Okieville domestic wells show groundwater quality improvements from MAR, including lower nitrate, uranium and arsenic concentrations, which are well below the groundwater concentrations of nearby communities . These indicate that our methodology has positively identified locations where recharge can improve the drinking water supply of rural communities in a region of our study area. Although many studies have used GIS-based MCDA for MAR suitability studies, there is no consensus on appropriate criteria, weights, and methods as these are generally dependent on the study objective, data availability,planting blueberries in containers and local experience . The assignment of weights to each thematic layer or feature is one of the most subjective factors of MCDA and thus, one of the main sources of uncertainty .

To address this issue, AHP is increasingly used to convert subjective assessments of relative importance into a set of weights , though sometimes the relative importance of themes may not be discernable . In this study, local experts in hydrology and human ecology similarly recommended the use of equal weights for thematic layers in both the site suitability and community vulnerability analyses. However, future iterations of these analyses will require the active involvement of local stakeholders , a process that may benefit greatly from the integration of AHP into the GIS-based MCDA . One main difficulty when estimating suitable recharge areas is the spatial and temporal variability of the physical system. We acknowledge that our analysis mainly uses land surface characteristics to determine suitable Ag-MAR sites, while subsurface characteristics were not directly included. Other factors not accounted for in our analysis include water availability, water quality, unsaturated zone transport, and willingness of landowners to flood agricultural land. Although robust quality control measures were taken, the accuracy of our results relies on the integrity of input data. Issues of accuracy and completeness of proprietary, hand-digitized, or self-reported data are inevitable, hence field-level studies of local surface and subsurface characteristics should be completed as part of project scoping and pilot testing. They are also essential to assess soil surface conditions, the presence of potential unprotected wellheads, capacity of connected surface water conveyance systems, feasible Ag-MAR water application amounts , and cropping and agro-chemical application history to determine potential legacy contaminant loading in the unsaturated zone that could be mobilized by recharge . Although nitrate loading to groundwater has been assessed at larger scales in California’s CV , parcel-level data on fertilizer application rates and nitrogen removal by crops is not publicly available, preventing the assessment of legacy nitrate loading in the unsaturated zone.

Future improvements of this methodology should include the addition of contaminant transport modeling or site-specific simulation of drinking water contaminants to address this gap. Climate projections and impacts on surface water availability for recharge require further investigation . As shown by Bachand et al. , despite its semiarid climate, the southern CV faces frequent flood risks. Along the Kings River, flows have exceeded the flood stage almost once every 7 years in the last 4 decades, creating total losses exceeding $1.2 billion . Kocis & Dahlke showed that excess surface water from high flows occur on average every 4.7 out of 10 years with total amounts reaching up to 1.6 km3 between November and April in years when high flows are available. Water scarcity is expected to increase as the southern CV experiences more frequent and longer droughts and more frequent extreme events during wet years . Integrated water management solutions like Ag-MAR are urgently needed to stabilize groundwater supplies in the region.Motorized UAS were introduced as a potential remote sensing tool for scientific research in the late 1970s. However, due to a variety of limitations these platforms had few practical applications . For years, UAS technology was led by military needs and applications. The relatively few applications in research and agriculture included deployments in Japan for crop dusting and in Australia for meteorological studies . In the past decade, several factors have greatly increased the utility and ease of use of UAS, while prices have fallen. Consumer demand drove the hobby craft industry to make major improvements in UAS vehicles. Integrating improved battery technology, miniature inertia measurement units , GPS and customizable apps for smartphones and tablets has delivered improved flight longevity, reliability, ease of use and the ability to better utilize cameras and other sensors needed for applications in agriculture and natural resources . Innovations in sensor technology now include dozens of models of lightweight visible-spectrum and multi-spectrum cameras capable of capturing reliable, scientifically valid data from UAS platforms .

Meanwhile, the Federal Aviation Administration has helped facilitate increased UAS use, with rule changes adopted in August 2016 that lowered what have previously been significant regulatory obstacles to the legal use of UAS for research and commercial purposes . UC faculty throughout California are using UAS in a wide range of agricultural and environmental research projects — from grazed range lands to field crops and orchards, forests, lakes and even the ice sheets of Greenland . UAS also have become a part of the curriculum across the UC system, and are increasingly used by campus staff in departments from facilities to athletics to marketing . UAS are already in wide use in agriculture, and the sector is projected to continue to account for a large share — 19% in the near term, per a recent FAA report — of the commercial UAS market in the United States. The use of UAS for research, particularly remote sensing and mapping, is soaring: A search in Scopus finds 3,079 articles focused on UAS or UAV applications in 2015, compared with 769 in 2005. Across all commercial uses, the FAA estimates 2016 sales of commercial UAS at 600,000 units and expects that figure to balloon to 2.5 million units annually as soon as 2017 . Despite the growing ubiquity of UAS, a variety of practical and scientific challenges remain to using the technology effectively. Collecting and processing data that is useful for management decisions requires a disparate range of skills and knowledge — understanding the relevant regulations, determining what sensing technology and UAS to use for the problem at hand, developing a data collection plan, safely piloting the UAS, managing the large data sets generated by the sensors, selecting and then using the appropriate image-processing and mapping software, and interpreting the data. In addition, as highlighted in the research cases presented below, much science remains to be done to develop reliable methods for interpreting and processing the data gathered by UAS sensors, so that a user can know with confidence that the changes or patterns detected by a UAS camera reflect reality. The UC Agriculture and Natural Resources Informatics and GIS program has recently incorporated drone services into the portfolio of support that it offers to UC ANR and its affiliated UC Agricultural Experiment Station faculty.Working closely with UC Office of the President, Center of Excellence on Unmanned Aircraft System Safety , IGIS has also developed a workshop curriculum around UAS technology,container growing raspberries regulations and data processing, which is open to members of the UC system as well as the public. Please check the IGIS website to learn about upcoming training events around the state in 2017, including a three day “DroneCamp” that will intensively cover drone technology, regulations and data processing.When a tree is stressed — whether due to pest infestation, nutrient deficiency or insufficient water — its leaves change. These changes may be detectable in the visible light spectrum — a shift in a leaf’s shade of green. They can also be “seen” in other bands of the electromagnetic spectrum — for example, a change in the texture of a leaf’s waxy coating may alter how infrared light is reflected.

Different types of stress generate unique electromagnetic “signatures.” If these signatures can be reliably correlated with specific causes, a UAS could be deployed to quickly scan a large orchard for signs of trouble, enabling early detection and treatment of pest infestations and other problems. Christian Nansen, a professor of entomology and nematology at UC Davis, leads a team working to refine this monitoring technique. They use hyperspectral camera, which generates a very high-resolution signature across a wide range of wavelengths. One of the challenges is that the electromagnetic signatures often contain high degrees of data “noise” — due to shadows, dust on leaves, differences between leaves and other factors — making it difficult to discern a clear signal associated with the stress that the tree is experiencing. To address this problem, Nansen’s team is refining a combination of advanced calibration, correction and data filtering techniques. As entomologists, they are also working to understand in fine detail the interactions between different pest species and tree stress, and how those affect the electromagnetic signature of a tree’s leaves .Rapid detection of water stress can help farmers optimize irrigation water applications and improve crop yields. In an orchard, precise assessments of water stress typically require manual measurements at individual trees using a device known as a pressure bomb that measures water tension in individual leaves. Tiebiao Zhao, a graduate student at UC Merced’s Mechatronics, Embedded Systems and Automation Laboratory, is collaborating with UC ANR Merced County pomology farm advisor David Doll with the goal of developing UAS-based tools to assess water stress across a large almond orchard at a high level of accuracy. Water stress can be detected by relatively low-cost multi-spectral cameras due to changes in how the canopy reflects near-infrared light. This project is building a database of canopy spectral signatures and water-stress measurements with the objective of developing indices that can be used to reliably translate UAS imagery into useful water-stress information. In a related experiment, Zhao is working with Dong Wang of the USDA Agriculture Research Service San Joaquin Valley Agricultural Sciences Center to detect the effects of varying irrigation levels and biomass soil amendments on crop development and yield in onions. As in Zhao’s almond experiment, the researchers are comparing spectral signatures gathered by low-cost UAS-mounted multi-spectral cameras with ground-truth data to better understand the relationship between the two .The Greenland ice sheet covers 656,000 square miles and holds roughly 2.3 trillion acre-feet of water — the sea level equivalent of 24 feet. As the climate warms, ice sheet melt accelerates; therefore, understanding the processes involved is important. This knowledge can help to refine predictions about the ice sheet’s future and its contribution to global sea level rise. A team of researchers led by UCLA professor of geography Laurence Smith is using UAS-based imaging technologies to map and monitor meltwater generation, transport and export. The group’s UAS carry multiband visible and near-infrared digital cameras that capture sub-meter resolution data, from which the researchers create multiple orthomosaics of the ice surface and perimeter over time. They are using the data to analyze a number of different cryohydrologic processes and features, including mapping rivers on the ice surface from their origins to their termination at moulins — vertical conduits that connect the ice surface with en- and sub-glacial drainage networks — and melt water outflow to the ocean. The team is also generating digital elevation models of the ice surface to extract hydrologic features, micro topography and drainage divides. In addition, they are working towards mapping ice surface impurities and albedo at high resolution using multi-band visible and near-infrared images.

For these simulations, the concentrations of dissolved species in background precipitation and in groundwater at the bottom model boundary were fixed, with compositions described in Table 2 to yield similar vertically distributed NO3 – concentrations as were measured in the soil cores. Flooding scenarios were then started from the initially steady flow and biogeochemical conditions developed as described above and run for 60 days. For these simulations, a free surface boundary was implemented for scenario S1 where 68 cm of water was applied all at once. In contrast, a specified flux boundary condition was imposed for the scenarios S2-S3, where floodwater applications were broken up over a week. The flood water composition is discussed in Section 2.3.5. The groundwater composition was taken from analyses reported by Landon and Belitz for a groundwater well located near our study site. For simplicity, the background recharge from rainfall was assumed to have the same composition as groundwater except that it was re-equilibrated under atmospheric O2 and CO2 conditions prior to infiltration. In addition, the concentrations of N species in the background recharge were set to values determined from our own analyses of N at the top of soil cores. The composition of the flood water was set to that of the background precipitation diluted by a factor of 100 for most constituents except for Cl-1 . Ratios of NO3 – to Cl-1 were used to trace the difference between dilution and denitrification effects on NO3 – . Denitrification and N2O production were simulated as aqueous kinetic reactions coupled to the fate of pH, CO2, Fe, S, NO3 – , and NH4 + based on the Spearman correlation analyses discussed above . Apart from pH and nitrate species, Fe and S have been linked to denitrification through chemolithoautotrophic pathways in addition to heterotrophic denitrification , and are therefore included in our reaction network.

Heterotrophic denitrification of NO3 – to N2 was represented via a two-step reduction process of NO3 – to nitrite and NO2 – to dinitrogen . Additionally,blueberry pot chemolithoautotrophic reduction of NO3 – to N2 with Fe and bisulfide as electron donors were implemented. Further, dissolved organic carbon was observed throughout the nine-meter profile at our field site, and CO2 and N2O profiles showed strong correlation . Therefore, DOC degradation was simulated using Monod kinetics, although individual DOC components were not simulated consistent with other modeling studies . In particular, we considered a single solid phase of cellulose in equilibrium with acetate as the source of DOC. Parameters for cellulose dissolution were calibrated using the total organic carbon concentrations obtained for each cluster. Biodegradation of acetate was coupled to multiple terminal electron acceptors, including NO3 – , Fe and SO4 2- which follow the hierarchical sequence of reduction potential of each constituent implemented by using inhibition terms that impede lower energy-yielding reactions when the higher energy yielding electron acceptors are present. These microbially mediated reactions and their kinetic rate parameters are shown in Table 5. Rates for denitrification were calibrated using the results from the acetylene inhibition assays as described above. Enzymes involved in denitrification include nitrate reductase, nitrite reductase and nitrous oxide reductase. To remain conservative in our estimates, we chose values typical for oxygen inhibition of nitrous oxide reductase L -1 ), the most sensitive to oxygen of the enzymes . Spearman rank correlation indicated that pH, DOC, S, NO3 – , and Fe exhibit significant correlation with N2O and therefore, these geochemical species were included in the reaction network. Cluster analysis was used to further detect natural groupings in the soil data based on physio-chemical characteristics, textural classes and the total dataset. Cluster analysis revealed three clusters representing distinct depth associated textural classes with varying levels of substrates and biogeochemical activity. Table 5 shows the median and range for N2O, CO2, NO3 – -N, Fe, S and total organic C for each of the clusters.

The first cluster is dominated by sandy loams within the top meter with highest median values of total N2O, total CO2, NO3 – -N, Fe, and total organic C concentrations, indicative of greatest microbial activity and denitrification potential. The second cluster is dominated by silt loams below one meter and had average values of total N2O, total CO2, NO3 – -N, Fe, and total organic C concentrations when compared to the other groups. The third group is dominated by sands and sandy loams below 1 meter and had the lowest median values of total N2O, total CO2, NO3 – -N, Fe, and total organic C concentrations amongst all groups. The clusters were thus automatically grouped by decreasing levels of denitrification and microbial activity. While most concentrations followed a decreasing concentration trend from cluster 1 to 3, the highest median values of S were associated with cluster 2. Liquid saturation profiles and concentration of key aqueous species predicted at different times for the homogeneous sandy loam column are shown in Figure A1. The sandy loam vadose zone is computed to be 32% saturated with near atmospheric concentrations of O2. As a result of oxic conditions, model results demonstrate significant residual NO3 – concentration within the vadose zone . Evolving from these conditions, Figure A1d shows that with flooding scenario S1, water reaches depths of 490 cm-bgs and saturation levels reach 40% in the sandy loam column. Deeper in the column, lower saturation and only small decreases in O2 concentration are predicted . Calculated concentration profiles show that O2 introduced with the infiltrating water is persistent at shallow depths down to 100 cm-bgs, below which O2 declines slightly as floodwater moves below this zone. Model results further indicate higher NO3 – reduction in the shallow vadose zone including the root zone with 35% of NO3 – being denitrified . Overall, this scenario results in NO3 – concentration persisting at depth. While other redox reactions, such as iron reduction and HSreduction of NO3 – to N2, may be important, conditions needed to induce these reactions were not realized in the sandy loam vadose zone due to the high pore gas velocities of the homogenous sandy loam allowing for large amounts of O2 to penetrate the profile from the incoming oxygenated water. In comparison to the homogenous sandy loam column, the predicted water content is higher and O2 concentration is 53% lower in the vadose zone of the homogenous silt loam column at steady state . This result is expected because of the difference in porosity,nursery pots with silt loams having higher water holding capacity and lower pore gas velocities compared to sandy loams.

Consequently, lower NO3 – concentration and lower NO3 – :Clratio are predicted in the silty loam vadose zone as compared to the sandy loam column . It is interesting to note that while greater NO3 – loss and denitrification are predicted for the silty loam vadose zone, carbon concentration associated with the shallow vadose zone are comparatively lower than for the sandy loam column. Moreover, the calculated pH is lower and iron concentrations are higher in the silt loam profile below the top meter when compared to the same depths within the sandy loam column . This suggests that chemolithoautotrophic reactions could be more important for these finer textured sediments. While both heterotrophic and chemolithoautotrophic reactions would be expected to result in a pH decrease , the greater decline in pH and concomitant increase in Fe+3 concentration suggests the importance of Fe and S redox cycling associated with the chemolithoautotrophic reactions in silty loam sediments . Evolving from these steady state conditions, scenario S1 suggests that denitrification is enhanced as floodwater infiltrates into the silt loam column. Model results indicate that saturation increases to 80% from 1 to 4 m depths and O2 decreases from 2.1 x 10-4 mol L-1 to 1.7 x 10-4 mol L -1 , resulting in 43% of the NO3 – being denitrified for this scenario . In comparison to the homogeneous profiles, the sandy loam with silt loam channel stratigraphy has higher calculated water contents and slightly lower O2 concentration within and surrounding the silt loam channel than the homogenous sandy loam column under steady state conditions . Calculated NO3 – concentrations are also similar between the homogenous sandy loam column and SaSi case, except for within and below the silt loam channel where lower NO3 – concentration was predicted . For scenario S1, water content for the SaSi case increased in a manner similar to the homogenous sandy loam, except for within the silt loam channel, which increased from 60 to 81%. Figure 4 further demonstrates that the infiltrating floodwater resulted in an increase in NO3 – concentration between 1 and 3 m within the sandy loam textured soil, but a decrease elsewhere. Within the channel itself , lower nitrate and NO3 – :Clratio are predicted, suggesting higher rates of denitrification . Overall, the model results indicate that an average of 37% of the NO3 – concentration is denitrified in the SaSi case 60 days after flooding, with 35% denitrification occurring in the sandy loam matrix and 40% occurring within the silt loam channel. This suggests that the silt loam channel acts as a denitrification hotspot. Furthermore, the silt loam channel has lower carbon and higher Fe+3 concentrations similar to the homogenous silt loam column again suggesting the importance of both heterotrophic and chemolithoautotrophic denitrification in these finer textured sediments. In comparison to the SaSi case, calculated water saturation and O2 profiles were markedly different between the homogenous silt loam column and the silt loam with sandy loam channel under steady state conditions . In particular, the sandy loam channel has lower calculated water content than the homogenous silt loam column . Further, greater gas flux within the channel resulted in 11-19% higher O2 concentration that penetrated deeper into the vadose zone as compared to the homogeneously textured column. NO3 – concentration are also estimated to penetrate deeper into the vadose zone in the SiSa case due to the high permeability of the sandy loam channel . While carbon concentration also penetrated deeper in the vadose zone in the SiSa case, higher calculated O2 concentration did not allow for comparable rates of denitrification below 1 m in this case as observed in the homogenous silt loam profile. This is further confirmed by the lower NO3 – :Clratio, which indicates that transport processes dominate biogeochemical fluxes within this column . With scenario S1, the calculated water content increased to 48% saturation while the O2 concentration remained the same within the channel. The high permeability channel allowed for NO3 – to move faster and deeper into the vadose zone. Overall, calculated denitrification was lower in the SiSa case as compared to the homogeneous textured column. In the simplified ERT stratigraphy, similar patterns were observed such that high permeability channels transported water, O2, and NO3 – faster and deeper into the subsurface than low permeability regions . As a result, concentration profiles showed significant variability across the modeled domain even under steady state conditions. For example, the calculated O2 and NO3 – concentrations are an order of magnitude lower in the shallow vadose zone below the limiting layer than within the preferential flow channel. Higher NO3 – :Clratio within the channel further confirms that preferential flow paths transport higher quantities of dissolved aqueous species without their being impacted by other processes such as denitrification . Dissolved carbon in particular is predicted to have a lower concentration in the preferential flow channel and the matrix surrounding the channel than below the limiting layer. In contrast, the Fe+2 concentration is estimated to be higher in the matrix surrounding the preferential flow channel and below the limiting layer . For scenario S1, model results indicate that NO3 – moved through the preferential flow path faster and deeper into the profile, while the limiting layer acts as a denitrification barrier as evidenced by the decrease in NO3 – :Clratio. The highest denitrification was estimated to occur in the matrix adjacent to the preferential flow channel , followed by intermediate nitrate reduction below the limiting layer and far away from the channel , while the lowest denitrification was estimated to occur within the channel itself .