They show that Kenyan exporters selectively allocated limited deliveries across contracts to maintain credibility in seller’s reliability. In a study of relational contracts in the Rwandan coffee industry, the same authors instrument the placement of mills to show that more potential competition from other mills reduces the use relational contracts with farmers making farmers worse off and reducing the quantity of coffee supplied to mills. Second-best competition is thus not necessarily welfare reducing.Biological simplification has accompanied agricultural intensification across the world, resulting in vast agricultural landscapes dominated by just one or two crop species. The Midwestern US is a prime example, where corn currently dominates at unprecedented spatial and temporal scales. An area the size of Norway is planted in corn in the Midwest in any given year with little variation in crop sequence; over half of Midwestern cropland is dedicated to corn-soy rotations and corn monoculture. Directly and indirectly, this agricultural homogeneity causes environmental degradation that harms ecosystem health while also contributing to climate change and increasing vulnerability to climate shocks. Agricultural diversification in space and time reverses this trend towards homogeneity with practices like crop rotations that vary which harvested crops are grown in a field from year to year. Crop rotations are a traditional agricultural practice with ample evidence that complex rotations— ones that include more species that turn over frequently—benefit farmers, crops, and ecosystems.
As one of the principles underlying agricultural soil management, diverse croprotations promote soil properties that provide multiple ecosystem services including boosting soil microbial diversity, grow trays enhancing soil fertility, improving soil structure and reducing pest pressur. These soil benefits combine to increase crop yields and stabilize them in times of environmental stress. Crop rotations’ environmental and economic benefits typically increase with the complexity of the rotation , while conversely, biophysical aspects like soil structure and microbial populations are degraded as rotations are simplified. Despite its benefits, crop rotational complexity continues its century-long decline in the Midwestern US. Corn-soy rotations increasingly dominate over historical crop sequences that included small grains and perennials, with corn monocultures also on the rise1. This increasing simplification is in part the result of a set of interlocking, long-standing federal policies aimed at maximizing production of a handful of commodity crops that distort farmers’ economic incentives. Regional rotation simplification is clear from analyses of crop frequency, county-level data, and farmer interviews. However, fine-grained patterns that more completely reflect farmers’ rotational choices across the region, and how those choices relate to influences from policy and biophysical factors that play out across agricultural landscapes, remain largely unstudied. This knowledge is essential for understanding how national agricultural policy manifests locally and interacts with biophysical phenomena to erode—or bolster—soil and environmental health, agricultural resilience, and farmers’ livelihoods. Bio-fuel mandates and concerted efforts to craft industrial livestock systems as end-users of these corn production systems make corn lucrative above other commodities, while federal crop insurance programs push farmers to limit the number of crops grown on their farms.
These policies, along with the current corporate food regime, drive pervasive economic incentives to grow corn, and farmers must increasingly choose between growing corn as often as possible to provide a source of government guaranteed income, and maximizing soil benefits and annual yields through diversified rotations. These policies both alter agricultural economics at a national level by boosting corn prices and manifest locally in grain elevators and bio-fuel plants that create pockets of high corn prices with rising demand closer to each facility. Biophysical factors like precipitation and land capability that are highly localized and spatially heterogeneous can catalyze or impede this simplification trend. For example, increasing rotational complexity is one strategy that farmers may employ to manage marginal soils or greater probability of drought, while ideal soil and climate conditions allow for rotation simplification to be profitable, at least in the short run5. As these top-down and bottom-up forces combine, we ask: how do farmers optimize crop rotational diversity in complex social-ecological landscapes, with top-down policy pressures to simplify intertwined with bottom-up biophysical incentives to diversify? . Because biophysical factors and even policy influences vary greatly at the field scale at which management decisions occur, an approach is needed to assess patterns of crop rotation that can capture simplification and diversification at this scale. Though remotely sensed data on crop types can now show fine-scale crop sequences, previous approaches to quantifying rotational complexity have relied on classifying rotations based on how often a certain crop appears in a region over a given time period, aggregating over large areas, or examining short sequences. To date, methods to capture rotational complexity have therefore been unable to address management decisions at the field scale , and/or lose valuable information about the number of crops present in a sequence and the complexity of their order .
At the other end of the spectrum, farmer surveys have impressively detailed the economic and biophysical considerations that go into farmers’ rotation decisions35, yet are limited by the number of farmers they can reach and who chooses to respond. Here, we explore how aspects of farm landscapes influence field-scale patterns of crop rotational complexity across the Midwestern US. We developed the first field-scale dataset of rotational complexity in corn-based rotations, covering 1.5 million fields in eight states across the Midwest and ranking crop sequences based on their capacity to benefit soils. We examined rotations from 2012-2017 to coincide with the introduction of the Renewable Fuel Standard, or “bio-fuel mandate,” which took full effect in 2012. We then correlated fields’ rotational complexity with biophysical and policy outcomes factors, using bootstrapped linear mixed models to account for spatial auto correlation in the data. By identifying spatially explicit predictors of rotational complexity, we illuminate how top-down policy pressures combine with biophysical conditions to create fine-scale simplification patterns that threaten the quality and long-term productivity of the United States’ most fertile soils.We focused our analysis on the eight Midwestern states with the highest corn acreage 2. We considered the six-year period from 2012 to 2017, which coincides with the introduction of the Renewable Fuel Standard in 2012. After deriving a novel field-scale rotational complexity index , we used spatially blocked bootstrapped regression to assess how key landscape factors associated with this indicator. These statistical methods account for overly confident parameter estimates that arise in naive models due to spatial autocorrelation in the data. All analyses were conducted in R47.To test for a relationship between RCI and predictive factors, all variables were centered and RCI was regressed against a set of covariate data in a linear mixed model including US state as a random effect to account for regional differences . We included interactions for which we had a priori hypotheses . The model was estimated using the R package `lme4`64. Two model assumptions are violated in the above model, requiring updated estimates of the parameters’ standard errors. First, because RCI is a derived statistic with an unusual domain, the index is not distributed according to a known distribution family and violates the assumption of normality in the residuals. Second, residuals showed high spatial autocorrelation at multiple scales and with an unknown structure, necessitating a nonparametric approach. Both violations are likely to shrink standard errors of the estimated parameters, leading to overconfident estimates; to illustrate, in the case of spatial autocorrelation, if the explanatory variables are randomly located in relation to crop rotation, spatial autocorrelation in crop rotation would falsely inflate significance. We used nonparametric spatial block bootstrapping to correct for this overconfidence. An algorithm for sparsely distributed spatial data, derived by Lahiri 2018, was implemented in R . Spatial block bootstrapping involves iteratively resampling data in spatial blocks to mimic the generation of autocorrelated data. Choice of block size is nontrivial, cannabis drying rack and choosing the optimal block is an open question, but blocks should be larger than the scale at which autocorrelation operates. Using the R package `gstat` to compute a variogram of the residuals generated by the naive LMM, we determined that range was 400815m. We used this as the dimension of each spatial block . We repeated this bootstrap with a range of possible spatial block sizes and found that this inference on parameters was robust to the choice of block size .Complexity of Corn-based Rotations in the Midwestern US: RCI values calculated for corn-based rotations create the first map, to our knowledge, that quantifies field-scale rotational complexity across the Midwestern US . RCI values from 2012-2017 range from 0-5.2 , and are positively skewed . Corn monoculture accounts for 4.5% of the study area and 3.3% of fields, suggesting that larger fields are more likely to be managed as monocultures .
The mode RCI score corresponds to a corn-soy rotation and dominates the region, covering over half of the study area. Two thirds of the area with this score was a CSCSCS or SCSCSC sequence, while the remaining third corresponds to other rotations that yield the same RCI .RCI scores have statistically clear correlations with land capability, mean rainfall, distance to the nearest bio-fuel plant, and field size, as well as with several interactions between these variables . Standard errors from the spatially blocked bootstrap were much larger than uncorrected naive confidence intervals, reflecting that accounting for spatial non-independence is necessary to estimate uncertainty of parameter estimates. Rotational complexity decreased with NCCPI, a proxy for land capability. We find that land of higher inherent capability is more likely to be used for lower complexity rotations. Fields with ample precipitation during the growing season are more likely to have simplified rotations. Though the relationship between the proximity of the nearest grain elevator and a field’s rotational complexity is not statistically clear , RCI showed a clear increase with distance to the nearest bio-fuel plant. Fields that are closer to bio-fuel plants are therefore more likely to have simplified rotations. Rotational complexity decreased with field size, with larger fields being more likely to have simplified rotations. Two of the interactions included in the model show statistically clear relationships. There is a positive interaction between land capability and field size, with higher quality land associated with decreasing RCI on small fields and slightly increasing RCI on large fields . The interaction between land capability and rainfall variance show a negative effect on RCI, with highly variable rainfall accentuating land capability’s impact on RCI . Interpretations of the relationship that each variable has with rotational complexity are shown in Table 4. Though each change is associated with a small shift in average RCI across the region, these can represent massive shifts in regional land management.As crop rotations continue to simplify in the Midwestern US despite robust evidence demonstrating yield and soil benefits from diversified rotations, our ability to explain and understand these trends will come in part from observing the biophysical and policy influences on farmers’ crop choices at one key scale of management: the field. By developing a novel metric, RCI, that can classify rotational complexity over large areas at the field scale, we open the door to regional analyses that can address the unique landscape conditions that impact farmers’ field-level management choices and their subsequent influence on rotational simplification. We find that as farmers are pushed towards simplification by broad federal policies , physical manifestations of these policies like bio-fuel plants are correlated with intensified simplification pressures. Similarly,we see that the pressure to build soils and boost crop yields through diversified rotations intensifies in fields with lower land capability, while conversely the negative effects of cropping system simplifications are accentuated on the region’s best soils.RCI uses the sequence of cash crops on a given field as a proxy for crop rotation, and sorts these sequences into scores based on the sequence’s complexity and potential for agro-ecosystem health. Because this metric has not been used in previous analyses, we verified RCI’s validity through comparisons to previous estimates of rotational prevalence in the region. For example, two separate surveys of farmers in the Midwestern US showed that between 24% and 46% report growing “diversified rotations” which we consider to be an RCI of greater than 2.24 . In the present study, 34% of fields had an RCI greater than 2.24. This and further comparisons of RCI to previous work show that RCI is capable of capturing previously-noted trends in the region.