For instance, the pre-side dress soil nitrate test is used widely in conventional systems to indicate plant available N just before the exponential growth phase of the crop. However, low soil NO3 – pools can occur even when N availability is high if soil NO3 – turns over rapidly, such as when high input and high output fluxes occur simultaneously. The higher soil carbon availability often resulting from organic management can increase both microbial N demand and gross soil N transformation rates, thereby increasing plant-soil-microbe soil N cycling and turnover of inorganic N. Thus, new indicators of N availability are needed that take into account active C and N processes in organic systems. Good candidates are labile soil organic matter fractions, which will benefit from more on-farm validation and standardization. Expression levels of genes involved in root N uptake and assimilation may also indirectly indicate plant available N in soil and provide a complement to bio-geochemical indicators of N availability, especially when soil NO3 – turnover is high. Plant N uptake and assimilation systems respond to wide variation in external N availability and internal N metabolites that reflect plant N status through regulatory mechanisms that optimize capture of limiting nutrients. Recent work has expanded knowledge of plant root transcriptional responses to N availability from laboratory-based systems into natural soil conditions, thus providing a basis for selecting candidate genes as indicators of soil N processes. These genes include high-affinity transporters of NH4 + and NO3 – ; nitrite reductase, responsible for reduction of NO3 – to nitrite; and glutamine synthetase and glutamate synthase,vertical agriculture which are involved in NH4 + assimilation into amino acids. Analyzing expression of these genes in roots may provide a “plant’s eye view” of soil N availability, and show how root Nassimilation is high even when soil inorganic N pools are low, i.e. in situations of tightly coupled and rapid N cycling.
If working organic farms can achieve both tightly-coupled N cycling and high crop yields, then how do farmers do it? Are there indeed bio-geochemical or plant-based indicator measures that will help organic farmers learn about their systems and provide the basis for adaptive management? Tomato , a model species for plant N metabolism and plant genetics, is widely grown on organic farms in California, where organic farmers use a variety of management practices. This provides a unique opportunity for a landscape study on how variability in SOM and management relate to yield and N cycling on working organic farms and how root expression of N metabolism genes could indicate rapid plant-soil-microbe N cycling . The overall hypothesis of this study is that tightly-coupled N cycling will be associated with higher levels of total and labile soil C and N and more diverse nutrient inputs . In turn, expression of root N metabolism genes will be elevated and more closely related to soil bio-assays for N availability than inorganic N pools in such fields. A landscape approach was used to assess crop yields, plant-soil N cycling, root gene expression, and the potential for soil N retention across a representative set of organic fields growing Roma-type tomatoes in one county in California, USA.The study took place during the tomato growing season to focus on the synchrony between soil N availability and crop N demand. The participatory framework in concert with GIS-based evaluation of land in organic tomato production was designed to provide real world context for evaluating novel combinations of indicators for N cycling in organic systems. The specific objectives were to: 1) identify different N cycling patterns in organic fields representative of the local landscape based on a suite of plant, soil, and soil microbial variables; 2) examine how root expression of key N metabolism genes relates to biogeochemical indicators of plant-microbe-soil N cycling; and 3) evaluate trade offs among ecosystem functions in N cycling scenarios.The organically-managed fields in this study were on similar parent material in Yolo County, California, which is situated along the western side of the Sacramento Valley.
Annual precipitation in 2011 was 403 mm, and the mean maximum and minimum temperatures were 21.7 and 7.3°C, respectively, compared to 462 mm, 23.1°C, and 8.4°C for the previous 20 years . From 1989–2011, certified organic acreage in Yolo County, California increased 15-fold while production value increased nearly 30-fold to >$30M.Farms growing organic Roma-type tomatoes in 2011 in Yolo County were identified using the California Certified Organic Farmers directory and farmers were contacted during the winter of 2010–11. CCOF is the primary organic certifier in this region of California. Widespread interest among organic farmers in this region to improve N cycling and increasing concerns about N loss due to state-level policy initiatives related to greenhouse gas emissions and water quality provided an entry point to engage a variety of farmers in this study. Eight growers expressed interest in the project and identified the fields in which they expected to transplant tomatoes in early April 2011 . Through multiple one-on-one meetings with these farmers we learned management practices and following the study, we discussed biophysical and management data from their field relative to data from other fields in the study and potential reasons for differences.GIS analysis of the land in organic tomato production was performed in order to ascertain how well the 13 fields that were sampled compared to the range of variability in organic tomato fields in Yolo County. Soil, landscape, and management attributes of all fields in organic tomato production in Yolo County were characterized with a landscape regionalization approach. A set of 103 points were randomly assigned to all such fields based on a 2008 field-scale county survey, representing one point every 4 hectares. For each of these points, the values of 12 variables were compiled from several sources. Categorical variables included soil great group and soil drainage class from the SSURGO database, the number of crop rotation types in a one mile surrounding square, and an agricultural sub-region classification. Continuous variables from the SSURGO database included percent sand, silt, and clay, organic matter, elevation, and the Storie index .GIS data were subjected to a clustering algorithm, partitioning around medoids , based on a distance matrix derived from Gower’s dissimilarity algorithm. PAM analysis with five clusters returned the best defined clusters yielding an average silhouette width of 0.499. The proportion of the landscape in organic tomato production represented by each cluster was calculated by performing a Voronoi tessellation of the 103 points, assigning eachpolygon of the tessellation to a cluster type,vertical farming aeroponics and then intersecting the tessellation with the field boundaries to allow determination of cluster areas.
Based on a lack of grower interest, cluster 2 was not represented.Soil and plant sampling was designed to capture indicators of ecosystem functions related to plant-soil N cycling at times corresponding to key agronomic and phenological events, including immediately prior to tomato transplanting , peak tomato growth period , and tomato harvest . In each field, plots were established at six random locations within a 0.25 ha area. Pre-transplant measurements took place several days prior to tomato transplanting but after other field operations, such as tillage, incorporation of organic amendments and/or vetch cover crops, and bed formation. Tomatoes were transplanted in all fields between 6 April and 20 April, 2011. In each of the six plots, three soil cores for each depth were removed from tomato beds and composited in the field, separately for each plot.For mid-season measurements, fields were all sampled within two weeks of one another, an average of 68 days after transplanting.A soil core was removed in each plot, situated between two tomato plants 15 cm from the planting row. Three 50–150 mg sub-samples of roots were promptly removed from the soil core in the field under minimized/indirect light, rinsed, patted dry, and flash frozen in liquid nitrogen for subsequent RNA extraction .The two plants adjacent to this core were cut at the base and petiole samples from recently matured leaves were removed. Plants were rinsed and dried at 60°C for two weeks before grinding and analyzing for C and N . Tomato yields were sampled just before the farmer’s harvest. In each plot , two 1m × 2m sub-plots were established. At each of these subplots, individual tomato plants were cut at the base and ripe fruit was separated by hand from green and decayed fruit . This process uses criteria similar to that of machine harvested tomatoes as well as those harvested by hand for fresh market sales. Biomass of fruits and shoots were weighed in the field then sub-sampled and dried at 60°C for 2 weeks, before grinding and analyzing for C and N . Soil cores were also taken from each subplot and composited in the field for measurements described below.Soil samples were kept on ice and processed within several hours of field extraction by thoroughly homogenizing by hand. Soils from the 0–15 cm depth were analyzed for a variety of soil C and N fractions, bio-assays for N availability, and soil properties, while deeper depths were analyzed for inorganic N and gravimetric water content only. Inorganic N was extracted from moist soils with 2M KCl and analyzed colorimetrically for NH4 + and NO3 -. Potentially-mineralizable N was measured as NH4 + liberated during a seven-day anaerobic incubation at 37°C. Chloroform fumigation-extraction followed by UV-persulfate oxidation and alkaline persulfate oxidation was used to measure microbial biomass C and N , respectively. K2SO4 extractable organic C and N were quantified in non-fumigated samples. Permanganate oxidizable C , which reflects a processed soil fraction that is sensitive to management was measured according to standard procedures. Gravimetric water content was determined by drying at 105°C for 48 h. Air dried soil samples were sieved to 2 mm, ground, and analyzed for total C and N at the UC Davis Stable Isotope Facility. Shoots and fruit were analyzed for total C and N, δ13C, and δ15N at the UC Davis Stable Isotope Facility. Petiole NO3 – , an indicator of recent N status in conventionally-produced vegetables, was measured in the most recently-matured leaves. Petiole NO3 – changes rapidly with growth stage, so the data are graphed by post-transplanting growing degree day to account for phenological differences among fields as a result of slightly different sampling times relative to transplanting.Root RNA was extracted using Trizol reagent according to the manufacturer’s guidelines followed by DNase digestion using RQ1 RNase-free DNase . Total RNA was purified using the RNeasy Plant Mini Kit . RNA concentrations and quality were assessed using the Agilent Nano drop and the RNA 6000 Nano Assay . Only RNA samples with RNA integrity numbers of at least 7.0 were used for subsequent analyses. These RNA were used for cDNA synthesis for qRT-PCR analysis. cDNA was synthesized from 0.5 μg DNase-treated total RNA using the Superscript III kit .Expression of cytosolic glutamine synthetase GS1 in roots was more strongly related to indicators of plant-soil N cycling than were the other six key genes involved in root N metabolism . Of the soil variables, GS1 was more strongly related to soil bio-assays for N availability than to inorganic N pools . Microbial biomass N and PMN were most strongly associated with expression of GS1 in roots, followed by soil NO3 – . Permanganate oxidizable C and MBC, both indicators of labile soil C pools, also had significant associations with GS1 expression in roots, but soil NH4 + did not. Expression of GS1 also was positively associated with shoot N and petiole NO3 – , as was glutamate synthase NADH-GOGAT. Inclusion of GWC as a covariate in multiple linear regression models improved the proportion of explained variation in GS1 expression .PCA of 28 indicators of yield and plant nutrient status, root N metabolism, and soil C and N cycling showed strong relationships among suites of variables, which clearly differentiated fields along the first two principal components . The first principal component explained 28.3% of the variation; on the left side of the biplot are higher values of most variables, including yield, soil bio-assays, expression of root GS1 and NADH-GOGAT, and labile and total soil C and N pools . Soil NH4 + and NO3 – concentrations from all three sampling times as well as AMT1.2 were associated with one another and with positive values along principal component 2, which explained 19.4% of the variation.