Final report for GW16-020
Cropping system intensification (reducing the frequency of unvegetated fallow years in crop rotations) has potential implications for the environment and economy of dryland agriculture as it impacts every aspect of the agroecosystem – from soil health, to nutrient management, to crop yields. The goals of this study were to quantify the effects of intensification on crop yields, fertilizer use, and soil properties related to carbon sequestration and nutrient cycling, and to determine how robust these effects are to variability in climatic, soil, and management factors. Additionally, despite the suggested benefits of dryland cropping system intensification, wheat-fallow remains a ubiquitous practice in the West-Central Great Plains, and we sought to understand the social dynamics underpinning decisions about whether and how much to intensify. We took soil and plant samples, and 6-year yield and fertilizer use histories, from dryland no-till fields from southeastern Colorado to northwestern Nebraska representing every level of cropping system intensity from wheat-fallow to continuous, 4-year rotations. Through in depth interviews with 25 farmers representing each level of cropping intensity, we uncovered the barriers to intensifying, and the strategies and motivations that have driven successful intensification.
We found that cropping system intensification was positively associated with soil organic carbon, aggregate stability, and fungal biomass, and these effects were robust amidst variability in environmental and management factors. Continuous rotations had 17% and 12% higher SOC concentrations than wheat-fallow in 0-10 cm and 0-20 cm depths, respectively. Aggregate stability in continuous rotations was about twice that in wheat-fallow rotations, and fungal biomass was three times greater in continuous rotations than wheat-fallow, but was not significantly different from mid-intensity rotations. Fungal biomass was positively correlated with aggregate stability. We also found that total and potentially mineralizable nitrogen (N) were 12% and 30% greater in continuous rotations relative to wheat-fallow, respectively, suggesting that internal N cycling was stimulated in continuous systems. Additionally, mid-intensity and continuous rotations had roughly 2 and 3 times more arbuscular mycorrhizal fungal (AMF) colonization than wheat-fallow, respectively, and AMF colonization was positively correlated with plant phosphorus (P) concentration. These results suggest that cropping intensity enhances internal cycling of N and phosphorus (P). Continuous dryland farmers also achieved 60% greater annualized crop production using a similar amount of fertilizer compared to wheat-fallow farmers. To explain the social dynamics underpinning decisions about intensification, we build on Carolan’s application of Bourdieusian social fields to agriculture, and find several overlapping fields within Carolan’s more general fields of sustainable and conventional agriculture, which are reflected in different degrees of intensification. In particular, we find that the emerging soil health movement, perceptions of risk and profitability, and crop insurance policy are drivers of the frequency of fallow on the landscape. We identify strategies for change, some of which would serve to reshape social fields, and others which leverage existing social positions and relationships, to enable farmers to overcome the barriers constraining cropping system intensification. We communicated these findings, and stories of farmers who have successfully intensified, to farmer and other audiences via blog posts, magazine articles, presentations at regional farming conferences and national scientific meetings, the drylandag.org website, and a short film.
Objective 1: Identify the social, ecological, political, and economic barriers to cropping intensification and the resources needed to eliminate or reduce the frequency of fallow in crop rotations.
Sub-objective 1: Examine the strategies farmers have employed to successfully intensify their cropping systems, and the obstacles faced by farmers with higher frequencies of fallow.
Sub-objective 2: Identify the factors that influence farmer decision-making at multiple scales.
Sub-objective 3: Identify the most effective educational, social, or political resources that could be mobilized to enable producers to intensify crop rotations.
Objective 2: Quantify cropping intensity effects on wheat and annualized grain yields, soil structure, and fungal biomass across management and climatic gradients.
Sub-objective 1: Compare winter wheat yields and annualized grain yields across farms with varying cropping intensity.
Sub-objective 2: Quantify cropping intensify effects on aggregate size and stability, intra-aggregate organic carbon, bulk density, and soil organic carbon.
Sub-objective 3: Assess the relationship between fungal biomass and soil aggregate stability.
Objective 3: Quantify the relationships between cropping intensity, nutrient cycling, fertilizer use, and crop yields.
Sub-objective 1: Quantify % AMF colonization of winter wheat roots and its effect on plant phosphorus (P) uptake.
Sub-objective 2: Quantify annualized grain production and fertilizer use across cropping system intensities.
Sub-objective 3: Quantify total and potentially mineralizable N across cropping system intensities.
Previously, we sought to measure leaf water potential as an indicator of drought stress in wheat. After one sampling at the most drought-prone site, we concluded there was no measurable pre-dawn or midday drought stress. We have since changed this objective (Sub-objective 1 of Objective 3) to examine the effect of % AMF colonization of winter wheat roots on plant P concentration.
Wheat-fallow (WF) is one of the dominant dryland cropping systems in the semi-arid Great Plains. This system consists of growing winter wheat (Triticum aestivum) from September to July, then fallowing for 14 months until the next wheat planting. No-till farmers in this region often reduce summer fallow frequency from one out of two years (WF), to one out of three or four years (mid-intensity; MID), by rotating winter wheat with crops like corn (Zea mays), sorghum (Sorghum bicolor), proso millet (Panicum miliaceum), peas (Pisum sativum), or sunflowers (Helianthus annuus). They may also eliminate summer fallow altogether via continuous cropping (CON).
Soil and plant sampling was conducted in 2015 and 2016 on 96 dryland, no-till fields in eastern Colorado and western Nebraska, representing 54 fields from working farms and 42 fields from long-term experiment stations. Each of three levels of cropping intensity – WF (n=27), MID (n=37) and CON (n=26) – was represented along a potential evapotranspiration (PET) gradient that increased from 1368 mm yr-1 in northwestern Nebraska to 1975 mm yr-1 in southeastern Colorado. Additionally, two 30-year old Conservation Reserve Program perennial grass plots (30-yr CRP) at the three long-term experiment stations in Colorado were sampled as a reference for comparison with the cropping systems (n=6). To assign a value of PET to each site, a linear equation was used based on known PETs from 6 locations as measured by open-pan evaporation and the latitude of each site. We used PRISM climate data from 1980-2010 to assign a value of 30-yr average annual precipitation to each field. Average annual precipitation at the study sites ranged from 349-472 mm and means by cropping intensity were statistically equivalent. Five-year field histories were collected for each field. We collected nitrogen (N) and phosphorus (P) fertilizer use data from working farms for the years 2010-2014 to calculate annualized fertilizer use. No field received compost or manure. All fields were under tilled WF management for several decades prior to implementation of no-till and the current crop rotation. Every field was planted to winter wheat in the fall of 2015.
In the fall of 2015, soil samples from the 0-20 cm depth were taken using a corer (2 cm dia.) at 4 locations within each field that represented corners of a 10 x 10 m square on each field and geo-referenced for later samplings. At each location, we composited 10 soil cores. Field replicates were analyzed separately for soil organic carbon (SOC) and later averaged to obtain field-level means. Soil samples used to determine texture and pH were composited and analyzed by field. Soil texture was determined by hydrometer, and pH using a 1:1 slurry of soil and deionized water at Ward Laboratories in Kearney, NE. At the three long-term experiment stations in Colorado, samples were taken from both a summit and toeslope position in each field to examine if the differences in water availability at upland and lowland positions influenced SOC. Samples on all other fields were taken from a flat topographical position and labeled as a summit.
Additionally, soil and plant samples were taken in spring of 2016 at the same locations as the fall sampling. The spring sampling was to a shallower 0-10 cm depth because the surface soil layers are more likely to be influenced by management practices, and surface soil physical properties, such as aggregation, can confer important functionality on water infiltration and storage. A 5.5 cm slide-hammer corer was used to take one 0-10 cm depth soil sample per sampling location (4 cores per field) to assess water-stable aggregation, bulk density, SOC, total N, potentially mineralizable N (PMN), and phospholipid fatty acids (PLFA). Bulk density, SOC, and total N were analyzed by sampling location (4 field replicates), and later averaged to obtain field-level means. Soils to determine water-stable aggregation, PMN, and PLFA were composited and analyzed by field. Subsamples of field moist soils (20 g) were weighed, dried at 105°C, and reweighed to determine gravimetric water content. Toeslope positions at the long-term experiment stations were excluded in the spring sampling. All samples were kept on ice in coolers for 4 to 36 hours before being refrigerated at 4°C. Plant samples (taken to coincide with wheat heading) were analysed for arbuscular mycorrhizal fungal (AMF) colonization of wheat roots, and wheat P concentration.
We determined SOC and total N on spring and fall air-dried soils. Soils were ground on a roller grinder and analyzed for total C and N on a LECO CHN-1000 auto analyzer (St. Joseph, MI). Soil inorganic C was assessed using the modified pressure calcimeter method, and subtracted from total C to determine concentrations of SOC.
A wet sieving method was performed on 80 g air-dried subsamples to separate soils into large macroaggregate (>2000 um), small macroaggregate (250-2000 um), free microaggregate (53-250 um), and free silt and clay (<52 um) size fractions. Air-dried soils were submerged in water for 5 minutes prior to sieving for slaking. Sieves were manually moved up and down 50 times over a period of 2 minutes in a shallow pan of deionized water. Each size fraction was collected in pre-weighed aluminum pans, dried in a forced-air oven at 60°C and weighed. Floating litter was decanted into a separate pan, dried, and weighed. Aggregate mean weight diameter (MWD) was calculated as an indicator of aggregate stability. Very few large macroaggregates were present, so they were combined with small macroaggregates for subsequent sand correction. A sand correction was performed on both the macroaggregate and microaggregate fractions to compare sand-free weights between soils with different sand contents. Briefly, 5 g subsamples were mixed with 5% sodium hexametaphosphate and shaken to disperse aggregates. This solution was then passed through 53 um and 250 um sieves for microaggregate and macroaggregate fractions, respectively. Sand that remained on top of the sieve was dried and weighed.
PLFA analyses were conducted on subsamples of the soils used to measure aggregation. Immediately upon passing through the 8 mm sieve, soils were further sieved to 2 mm and frozen at -20°C within 2 days of sampling before being freeze-dried. PLFAs were extracted and separated at Ward Labs (Kearney, NE). Briefly, two grams of lyophilized soil (dry weight equivalent) was extracted in 9.5 ml mixture of dichloromethane (DMC)/methanol/citrate buffer (1:2:0.8 v/v) for 1 h at 240 rpm. Then, 2.5 ml of DMC and 10ml of a saturated KCl solution were added to each tube, shaken for 5 min, and centrifuged to separate the organic fraction. Soil lipid extracts were separated in silica gel columns, transmethylated, and PLFAs were quantified by gas chromatography on an Agilent 7890A GC. The fatty acid methyl ester 18:2 w6c was used to represent fungal biomass, and 10:0 2OH, 14:0 iso, 15:0 iso, 15:0 anteiso, 16:0 iso, 16:1 w7c, 17:0 iso, 17:0 anteiso, 17:0 cyclo, and 18:1 w7c were used to represent bacterial biomass. Only bacterial and fungal PLFAs (65 total biomarkers) were added to represent microbial biomass (polyunsaturated and PLFA biomarkers longer than 20 C chains were excluded from the analysis).
PMN was determined using a 7-day anaerobic incubation. Briefly, soils were sieved to 2 mm and divided into two groups for initial extraction and incubation. Initial extraction soils (5g) were shaken with 50mL 2M KCl for 1 hr, filtered, and extracts were frozen for later analysis. Incubated soils were added to 10 mL of deionized water, flushed with N2 gas for 1 minute, capped, and stored at 30°C for 7 days before being extracted similar to the initial extraction soils. Samples were analyzed for NH4+-N and NO3--N colorimetrically using a microplate reader (Biotek, VT), and PMN was calculated as the concentration of NH4+-N in the incubated sample minus the NH4+-N concentration in the initial extraction sample.
AMF colonization and wheat P concentration were determined from a subset of fields (n=51) that were growing Byrd variety winter wheat. AMF colonization is typically highest at wheat heading due to optimal soil temperatures and ample photosynthate supply to roots. About 5-10 whole winter wheat plants were harvested from each sampling location. Intact wheat plants were kept cool after harvest, and roots were separated from aboveground biomass. Aboveground biomass was dried at 30°C and shipped to Ward Laboratories in Kearney, NE for analysis of P concentration. Wheat roots were rinsed, cleared with 10% KOH, acidified with 2% HCl, and stained with Trypan blue within 24 hrs. Stained roots were then stored in vials of deionized water at 4°C for subsequent AMF colonization analysis. AMF colonization was assessed following the gridline intersect method. Four replicates for each field were analyzed and later averaged to obtain field-level means of % colonization.
For each working farm field whose operator was willing to report data (n=42), we collected yearly data from 2010-2014 on N and P fertilizer use, and yields for each crop from 2010 to 2015. No field received compost or manure, and amounts of nutrients applied other than N and P were negligible. The net operating income for each cropping system was calculated using partial enterprise budgets. Fertilizer prices for each year from the National Agricultural Statistics Service were converted to USD/ha of N and P and multiplied by the amount of N and P applied each year in kg/ha to obtain fertilizer expenditures in USD/ha. Amounts of herbicide were converted to acid equivalents (AE)/ha, and multiplied by the 2017 cost of each herbicide in USD/AE calculated from University of Nebraska to obtain herbicide expenditures in USD/ha. Additional expenditures included planting and seed costs, herbicide and fertilizer application costs, and harvest costs, estimated for each crop in each year using custom rates as reported by Colorado State University. Based on results of interviews with the farmers in this study, we assumed that each summer fallow period required 4 separate applications of herbicide. Annualized grain production was calculated by dividing the total amount of grain production from 2010 to 2015 by 6. To calculate revenues generated from crop production, yields were multiplied by crop prices for each crop in each year. Net farm operating income was calculated as revenues minus expenses for each year, excluding fixed costs, and then divided by 5 to calculate annualized net operating income in USD/ha/yr. Additionally, to assess the wheat yield penalty of continuous cropping relative to summer fallowing, we collected wheat yield data from a broader set of fields (5 to 10 fields per farmer) from 2012 to 2015, and recorded whether the preceding year was summer fallow or cropped.
The relationships between cropping system intensity and SOC at 10 cm and 20 cm, aggregate MWD, total N, PMN, AMF colonization, input use, crop yields, and microbial PLFA were tested using multiple linear regression. Models were selected using backwards selection with cropping system intensity as a categorical variable, and all management factors (# years in no-till, # years in rotation, and fertilizer use) and environmental factors (PET, % clay, pH, and slope) until all remaining terms were significant (a=0.05). To account for environmental and management factors as covariates, least-squared means for each level of cropping system intensity were generated and tested for significant pairwise comparisons. P-values less than 0.05 were considered significant. The relationships between % AMF colonization and plant P, and between % AMF colonization and PET, were tested using linear regressions. We used R for all data analyses, and multiple linear regressions were generated and tested for significance using the packages lme4, lsmeans, and lmerTest. We used R for all data analyses, and multiple linear regressions were generated and tested for significance using the packages lme4, lsmeans, and lmerTest. Due to the small sample size, CRP fields were excluded from statistical analyses.
Interviews with 23 farmers were conducted in 2015 and 2016 following the precepts of grounded theory, with slight alteration. Although this method does not presuppose an understanding of the phenomenon at hand, but rather allows relevant themes to emerge throughout the interview process, the first interviews were inspired by the semi-structured, open-ended interview questionnaire used by Blesh and Wolf (2014). Relevant themes were coded into applicable categories, which were constantly compared throughout the course of data collection, enabling higher order themes to emerge. Each interview lasted between one and two hours. Sampling was concluded when new themes ceased to emerge. Almost every farmer either grew up on a farm that practiced wheat-fallow or practiced it himself before transitioning to the present crop rotation. Interviewees were identified by snowball sampling, as each participant was asked to suggest at least one other farmer who fit the criteria. All farmers practice dryland agriculture with <500 mm annual precipitation and use no-till practices. By seeking out farmers across a wide geographical range along the full spectrum of cropping system intensity, we were able to access a variety of social networks, thereby overcoming a common shortfall in snowball sampling. All primary operators identified as male. We captured a wide range of farm sizes and ages, although age data was not formally recorded.
Overall, intensified cropping systems had higher SOC, aggregation, and fungal and total microbial biomass, and these trends were robust amidst variability in environmental and management conditions. Additionally, continuous cropping enhanced the N and P supply capacity of soil by increasing total N and PMN, and fostered AMF colonization, which correlated with enhanced wheat P uptake. Farmers practicing continuous cropping achieved greater annualized grain production despite applying similar total amounts of fertilizer.
We observed greater SOC concentrations in CON relative to MID and WF rotations at both the 0-10 and 0-20 cm depths. Cropping system intensity (p=0.02), PET (p<0.001), % clay (p<0.001), and slope position (p<0.001) explained 54% of the variability in SOC at the 0-10 cm depth. Similar to the shallow depth, cropping system intensity (p<0.01), PET (p<0.001), % clay (p<0.001), and slope position (p<0.001) explained 54% of the variability in SOC at the 0-20 cm depth. After accounting for PET, % clay, and slope as covariates, SOC concentrations in WF, MID, and CON averaged 1.09%, 1.15%, and 1.28% at 0-10 cm, and 0.92%, 0.89%, and 1.03% at 0-20 cm, respectively. SOC levels were 17% higher in CON rotations than WF at the 0-10 cm depth, but CON was not significantly different from MID. However, SOC concentrations in CON rotations were 16% greater than MID, and 12% greater than WF to a depth of 20 cm. SOC concentrations in CON and the less intensified rotations were about 80% and 70% of those in the 30-yr old CRP at both depths, respectively. SOC concentrations in MID rotations were similar to that of WF at both 0-10 cm and 0-20 cm depths. There were no significant cropping intensity effects on bulk density.
Aggregate MWD increased with cropping system intensity. Cropping system intensity (p>0.01), PET (p=0.03), and % clay (p<0.01) explained 30% of the variability in MWD of water-stable aggregates in the 0-10 cm depth. After accounting for PET and % clay as covariates, aggregate MWD in CON rotations was about twice as large as those in WF, and aggregate MWD in MID rotations was intermediate of the two. Aggregate MWD in the 30-yr CRP was 4 times greater than CON rotations, and 8 times greater than WF.
Total PLFA concentration (a proxy for microbial biomass), the fungi:bacteria ratio, and total fungal PLFA concentration increased with cropping system intensity. There was no relationship between cropping system intensity and bacterial PLFA concentration. Cropping system intensity (p=0.04), % clay (p<0.01), and pH (p<0.01) explained 31% of the variability in microbial biomass. Total PLFA in CON rotations was 35% greater than that of WF, and MID rotations were intermediate between the two. Total PLFA in 30-yr CRP was 1.5, 1.8, and 2.1 times greater than CON, MID, and WF rotations, respectively. Cropping system intensity was the only significant predictor of the fungi:bacteria ratio (p<0.01, R2=0.18) and total fungal PLFAs (p<0.01, R2=0.15). CON rotations had three times higher fungi:bacteria ratios than WF and MID rotations were intermediate of the two. Total fungal PLFA was 3 times greater in CON rotations compared to WF, but was not significantly different from MID rotations.
We observed positive effects of cropping system intensity on total soil N and PMN at a depth of 0-10 cm. Cropping system intensity (p=0.04), PET (p<0.001), % clay (p<0.001), and slope position (p<0.001) explained 59% of the variability in total N. After accounting for PET, % clay, and slope as covariates, total N stocks were 12% higher in CON rotations than WF (p=0.04), but CON was not significantly different from MID. Cropping system intensity (p<0.01), % clay (p<0.001), and an intensity-by-clay interaction (p=0.02) explained 47% of the variability in PMN. After accounting for % clay and the intensity-by-clay interaction as covariates, PMN was 30% higher in CON rotations than WF (p=0.06), but CON was not significantly different from MID. The trends and magnitude of the differences in PMN between intensities are similar when the intensity-by-PET interaction is removed from the model (data not shown), indicating that the interpretation of these differences is not confounded by the interaction. Analyzing the interaction between intensity and % clay revealed that % clay had a strong positive effect on PMN in WF rotations (R2=0.62, p<0.01), and a slight positive effect in MID rotations (R2=0.21, p=0.06), but no effect in CON rotations.
AMF colonization increased with cropping system intensity, and was negatively impacted by PET. Cropping system intensity (p=0.001), PET (p=0.47), and an intensity-by-PET interaction (p<0.01) explained 59% of the variability in % AMF colonization. PET was retained in the model due to the significant interaction between PET and intensity. After accounting for PET and the intensity-by-PET interaction as covariates, MID and CON rotations had roughly 2 and 3 times more colonization than WF (p=0.02, p<0.001), respectively. Additionally, CON rotations had 54% more AMF colonization than MID rotations (p=0.01). The trends and significant differences in AMF colonization between intensities are similar when the intensity-by-PET interaction is removed from the model (data not shown), indicating that the interpretation of these differences is not confounded by the interaction. However, analyzing the interaction between intensity and PET revealed that PET had a stronger negative effect on % AMF colonization in CON rotations (p<0.001, R2=0.63) than in MID rotations (R2=0.22, p=0.03), and PET had no effect on AMF colonization in WF rotations. Wheat aboveground biomass P concentrations positively increased with AMF colonization (R2=0.16, p=0.03).
Annualized fertilizer use from 2010 to 2014 was similar between cropping system intensities, as a result of smaller amounts of fertilizer applied per crop in CON rotations. Cropping system intensity (p=0.002) and PET (p<0.001) explained 55% of the variability in annualized N fertilizer use. MID rotations applied about 59% more N fertilizer per year (18 kg N/ha/yr) than both CON and WF rotations (p=0.007). In CON rotations, N applied per crop was about 22 and 34 kg N/ha less than WF (p=0.05) and MID rotations (p<0.001), respectively. Annualized P fertilizer use and P applied per crop did not differ by cropping system intensity.
Annualized grain yields from 2010 to 2015 increased with cropping system intensity, despite a wheat yield reduction associated with the elimination of summer fallow. Cropping system intensity (p<0.001), N fertilizer use (p=0.01), and PET (p<0.01) explained 71% of the variability in annualized grain yield. After accounting for N fertilizer and PET as covariates, MID and CON rotations produced 46% and 60% (p<0.01) more grain per year than WF, respectively. We separated all wheat yields into two cropping treatments based on whether wheat was preceded by summer fallow or continuous cropped. Cropping treatment (p<0.001) and PET (p<0.001) explained 36% of the variability in wheat yields. After accounting for PET as a covariate, on average across 2012 to 2015, wheat that followed a crop yielded 29% less than summer fallowed wheat (29 vs. 41 bushels on average, respectively; p<0.001).
Biophysical Discussion and Conclusions
While WF remains the one of the most common cropping systems in the semi-arid High Plains, this and other semi-arid regions around the world are undergoing a profound transition to intensified dryland cropping systems, and thus it is critical to understand the implications of this transformation. We found different levels of SOC, aggregation, and fungal biomass between different levels of cropping system intensity. Overall, our results suggest that cropping system intensity, independent of tillage, increases SOC both directly, through greater C inputs to soil, and indirectly, through effects on microbial communities and aggregation. We observed these relationships to be robust across a wide climatic gradient, and amidst variability in soil texture and management history. These results corroborate others who have found greater aggregation and SOC in more intensely cropped systems, but also shed new light on the central role that fungi may play in C storage in dryland agroecosystems.
Additionally, addressing the global economic and environmental challenges associated with rising fertilizer use requires the establishment of internal capacity in agroecosystems to supply nutrients and control weeds. This capacity has been all but lost in simplified agroecosystems, including those reliant on summer fallow that once dominated many semi-arid climates. We found that continuous cropping in the High Plains can increase N retention and cycling and P uptake by plants, mediated by increased associations with AMF. This enhanced capacity to supply nutrients in continuously cropped soils enabled continuous dryland farmers to achieve more grain production using the same amount of fertilizers compared to those practicing wheat-fallow. Overall, we conclude that cropping system intensification represents an opportunity to achieve more grain production while managing nutrients with fewer external inputs. Together, these results suggest that the elimination of summer fallow in semi-arid cropping systems has the potential to offset the C emissions associated with no-till grain production, and achieve higher crop yields and soil health improvements that will contribute to the long-term success of dryland agriculture.
Sociological Results and Conclusions
Moving beyond simple economic rationales to explain a growing wave of cropping system intensification, we provide evidence for a social dynamic shaping the degree to which farmers are willing to intensify. For almost a century, the dominant social body of dryland agriculture ascribed to an imaginary (i.e. a worldview or deeply held understanding) that embedded the use of summer fallow in dryland agricultural practice. The soil health movement served to socially construct a new imaginary that rejects this traditional notion, and gave rise to a new social field of soil health practitioners with different values, perceptions, and knowledges than other dryland farmers. In contrast to the motivations of soil health practitioners to reduce inputs and build natural capital, many other farmers have transitioned to mid-intensity cropping systems for productivist motivations, citing evidence from mainstream research that replacing summer fallow with crops increases total food production. Still, the perceived risks associated with cropping system intensification, reinforced by crop insurance policies, prevent many dryland farmers from moving beyond a wheat-fallow rotation.
To facilitate innovation in sustainable agriculture generally, and cropping system intensification specifically, we suggest strategies like changing the mindset of mainstream agronomic researchers to be more inclusive of long-term viewpoints and profitability strategies beyond yield maximization, which would help to reshape the conventional social fields through existing networks of trust. Additionally, we suggest strategies such as maximizing short-term profitability of intensified cropping systems through market development, which would leverage our understanding of farmer perceptions in their existing social positions to facilitate intensification. A combination of these two types of strategies may help fit sustainable agricultural practices into existing imaginaries, and form new imaginaries that drive sustainable innovation.
Education and Outreach
We communicated these results in several different ways to reach a broad audience, while directing actionable information to farmers. We presented preliminary results at the 2016 ASA, CSSA, and SSSA conference in Phoenix for which we received an award (S. Rosenzweig, M. Schipanski. How robust is the rotation effect in semi-arid systems? ASA, CSSA, SSSA Annual Meeting 11/8/16). We also presented the results in two guest lectures for classes, and at a seminar for the Soil and Crop Sciences department at Colorado State University. We completed a short film based on this study (Resilient: Dryland Farming in the Semi-arid High Plains), purchased a domain, and populated a website (drylandag.org) that has an average of 217 unique visitors each week. Results were presented at a regional farming conference, and were shared in a newsletter for wheat growers. Results were also presented at the 2017 ASA, CSSA, and SSSA conference in Tampa (S. Rosenzweig, S. Fonte, M. Stromberger, M. Schipanski. A holistic assessment of soil health and agronomic effects of dryland crop rotations. 10/22/17), and at the 2017 Soil Ecology Society conference, for which we received the award of Best Oral Presentation (S. Rosenzweig, S. Fonte, M. Stromberger, M. Schipanski. Putting the soil health principles to the test in the semi-arid High Plains. Soil Ecology Society Meeting 6/5/17). We also told the story of a successfully intensified dryland farmer in a blog post (S. Rosenzweig, “Soil Health and an Era of Ecological Experimentation Agriculture.” HUMANnature 1/25/17), and told the story of the broader soil health movement in an online magazine article (S. Rosenzweig, “How a new way of thinking about soil sparked a national movement in agriculture.” Ensia 4/1/17). We plan to publish the results from this project in peer-reviewed journals such as Agriculture, Ecosystems & Environment, and Rural Sociology.