The cost of doing business is likely an important factor, although the specific reasons behind high costs of trade for a particular context have been challenging to distinctly identify . Measurement challenges make it difficult to distinguish the degree to which long distances, poor infrastructure, policy-driven market distortions, intermediary market power, and relatively limited information and communication technology might contribute to high trade costs in a particular context . While estimations of transport costs in Africa are clearly high in comparison to other regions , there is very little causally identified evidence on the competitiveness of trade in Africa . Where trade costs are high, there may be limited spatial arbitrage, making producers reliant on those who have the capacity to access these arbitrage opportunities and act as trade intermediaries . Rural markets remain poorly integrated when there are limited options for trade, and this inefficient movement of supply results in wide-ranging local market prices across regions and seasons . This volatility can be a direct disincentive to widespread adoption of productive technologies because an influx of output is met immediately by a collapse in prices .Poor infrastructure has been rigorously investigated in a few cases; road quality, for example could clearly increase transport costs, driving up input prices, driving down output prices, and thereby reducing the scope for profitable adoption of productivity-enhancing technologies. Infrastructure development to reduce transportation costs has been associated with increased access to output markets and smoother prices . For example, in Sierra Leone, investments in infrastructure were correlated with lower transaction costs for both farmers and traders . However,square plastic pot farmers’ ability and willingness to pay for access to deeper output markets should not be assumed even where road conditions are adequate .Contract enforcement problems in supply chains and output markets can also impede producers’ profitability.
Buyers may trade only with trusted brokers or other traders with whom they have repeated interactions, resulting in a fractured chain of many short-distance, relationship-based exchanges . The surpluses offered by each small seller may be too diffuse to attract large national buyers, instead requiring aggregation, which often necessitates both coordination and access to capital . Unobservable dimensions of output quality can mean higher quality products go unrewarded on the market, resulting in the prevalence of lower quality products . Enforceable contracts could create economies of scale, and facilitate access to technologies, financial services, and output markets for small-scale producers . Hansman et al. find evidence from Peru fish meal manufacturing that vertical integration increases with demand for high-quality outputs; firms produce considerably higher output and are paid higher export prices when they own more of their suppliers. These benefits of contracting arrangements could presumably help smallholders access higher-value markets where economic growth in agriculture is increasingly concentrated . But evidence is still limited, and there are risks that contract farming schemes can falter or collapse . In what follows, we describe the recently emerging experimental evidence based on interventions to improve the efficiency of input and output markets in relation to smallholder farmers. We focus on two primary domains. First, price discovery, given the relatively numerous studies that have tried to improve the delivery of price information to producers and intermediaries. We then examine a set of interventions intended to improve contracting in supply chains, including studies that work to improve quality recognition, to improve contracting performance through intermediary institutions, and to create entirely new market structures leveraging ICT platforms.Drained peat lands occupy only 1% of agricultural land but are estimated to emit 32% of global cropland carbon dioxide -equivalent emissions . As peat land soils are drained and exposed to the atmosphere, high rates of aerobic decomposition lead to substantial CO2 respiration rates relative to other ecosystems .
High rates of peat decomposition along with emissions of other important greenhouse gases like methane and nitrous oxide can result in large net GHG emissions from these agricultural ecosystems . Nitrogen fertilization and flood irrigation are common in peat land agriculture , potentially creating optimal conditions for high denitrification rates and N2O production. Drained peat lands have been shown to be significant N2O sources; the IPCC mean estimate for drained agricultural peat lands is 8 kg N2O-N ha-1 y-1 . However, few studies have made continuous multi-year measurements of N2O emissions, and N2O fluxes are often absent from long-term agricultural peat land GHG budgets . This is partially driven by the technological challenges of conducting continuous, long-term N2O flux measurements under field conditions . Most N2O flux measurements are conducted intermittently with sampling frequency often ranging from once per day to once per month using traditional manual static chambers . This is particularly true in agricultural peat lands . However, CH4 and N2O are often characterized by hot spots and hot moments of GHG emissions , which are difficult to characterize using infrequent manual sampling approaches . The dynamics of soil oxygen , temperature, moisture, and nitrate concentrations are likely to contribute to hot moments of soil N2O flux , although the spatial and temporal dynamics of these events are also difficult to predict without high frequency measurement. Potential hot moments of soil CH4 fluxes are similarly difficult to capture utilizing manual chamber methods, although CH4 fluxes from drained agricultural peat lands are assumed to be minimal . However, management practices such as irrigation can create periods of anaerobic conditions ideal for CH4 production . Continuous eddy covariance measurements of CH4 fluxes at the ecosystem-scale have highlighted the influence of soil temperature, water table fluctuations, and plant activity on the exchange of CH4 across the land-atmosphere interface in restored wetlands . In contrast, the spatiotemporal controls on the magnitude and frequency of CH4 fluxes in irrigated agricultural soils are less well constrained. The recent development of cavity ring down spectroscopy and automated chamber measurements has greatly increased the ability to conduct continuous GHG flux measurements.
Continuous measurements can increase the chances of capturing hot moments of net GHG fluxes and determining their role in annual GHG budgets. In combination with continuous soil sensor data, spatiotemporally intensive measurements can also be utilized to explore potential drivers of hot moments of soil CH4 and N2O emissions . We used cavity ring down spectroscopy and automated chambers to make over 70,000 soil CO2, CH4, and N2O flux measurements over three years from a drained agricultural maize peat land in California, USA. Flux measurements were coupled with continuous soil O2, temperature, and moisture sensors and a year-long soil N sampling campaign to better constrain the drivers and controls on hot moments of soil CH4 and N2O emissions. We utilized multiple statistical approaches,25 liter pot including wavelet coherence analysis and a modified jackknifing technique to further explore the drivers and controls on hot moments of soil CH4 and N2O effluxes. We tested the hypothesis that fertilizer application would drive hot moments of N2O emission through increased substrate availability. We also hypothesized that elevated soil temperatures and soil moisture would stimulate O2 depletion during the growing season, leading to increased N2O and CH4 production within the soil profile and associated hot moments of GHG emissions. The study was conducted in the Sacramento-San Joaquin Delta region of California . The field site was farmed continuously for over 10 years for conventional field corn . The site was periodically irrigated via spud ditches during the growing season and periodically flooded up to 30 cm above the soil surface in the winter to limit weed growth and provide habitat for migrating waterfowl . Fertilizer application rates were 118 kg N ha-1 y-1 .The region’s historical mean annual temperature was 15.1 ± 6.3 °C and mean annual rainfall averaged 326 ± 4 mm . This was also an Ameriflux site with continuous eddy covariance measurements of CO2, CH4, and water vapor since mid-2017. Soils are typical of the region and are classified within the Rindge series as Histosols . This soil type is frequently drained for agriculture due to its high agricultural productivity . Rindge soils belong to the Euic, thermic Typic Haplosaprists taxonomic class and are characterized by deep, poorly drained marsh soils formed from decomposed plant organic matter . Total soil C values at this site were 15.2 ± 0.4% at 0-15 cm, 15.9 ± 0.7% at 15-30 cm, and 19.5 ± 0.6% at 30-60 cm depth . Total soil N values were 1.0 ± 0.02% at 0-15 cm, 1.1 ± 0.04% at 15-30 cm, and 1.2 ± 0.03% at 30-60 cm depth . Surface soil fluxes of N2O, CH4, and CO2 were measured continuously from June 30, 2017 through June 30, 2020 using an automatic chamber system. This system consisted of nine opaque automated gas flux chambers connected to a multiplexer . The multiplexer allowed for dynamically signaled chamber deployment and routed gases to a cavity ring-down spectrometer . Chambers were measured sequentially over a 10-min sampling period with a 1.5-min flushing period before and after each measurement. Chambers were deployed in 10 x 10 m grid, with each chamber 5 m apart. Due to periodic flooding events, two sets of extended soil collars were utilized to maintain measurement collection and ensure chambers were not inundated. Chambers were randomly assigned to distinct physical features, beds or furrows during growing seasons and corn stover or bare soil during fallow periods. Throughout most of the year, 15 cm collars were installed with each chamber, offsetting the original chamber height by approximately 10 cm.
Due to winter flooding events that raise the water table up to 30 cm above the soil surface, additional 35 cm collars were deployed approximately between November and February. Individual chamber volumes were measured and used to adjust flux calculations . Chambers remained installed in their original positions throughout the field campaigns except during field management activities , which typically lasted less than one week. Two additional periods of chamber removal occurred after delays in initiating corn harvest in site year 1 and site year 3 . To determine chamber volume, collar heights were measured approximately weekly and values were interpolated over time to account for differences in soil and water table height. Chamber volumes were used to calculate the minimum detectable flux with detection limits of 0.002 nmol N2O m−2 s−1, 0.06 nmol CO2 m−2 s−1, and 0.002 nmol CH4 m−2 s−1 for 15 cm collars utilized during non-flooded conditions, and 0.004 nmol N2O m−2 s−1, 0.12 nmol CO2 m−2 s−1 , and 0.004 nmol CH4 m−2 s−1 for 35 cm collars utilized during flooded conditions. The minimum detectable fluxes reported here are conservative estimates, as the actual chamber volume was always smaller than the maximum theoretical volume used in detection limit calculations. Flux calculations and fitting were first performed using Eosense eosAnalyze-AC v. 3.7.7 software, then data quality assessment and control were subsequently performed in R . Fluxes were removed from the final dataset if they were associated with negative gas concentrations or erroneous spectrometer cavity temperature and pressure readings outside the calibrated operating range, corresponding to instrument malfunction. Fluxes were also removed if the chamber deployment period was less than 9 min or greater than 11 min, indicative of chamber malfunction. This data filtering removed 2.4% of flux measurement periods, generating a final dataset of 71,262, 70,337, and 70,554 individual flux measurements of CO2, N2O, and CH4, respectively. To calculate the impact of soil GHG fluxes on site-level global warming potential we utilized net ecosystem exchange eddy covariance values at the same site . To convert flux measurements to CO2e, we used the IPCC AR5 100-year GWP values of 28 CO2e for CH4 and 298 CO2e for N2O . Yield-based emission estimates were from derived flux measurements and harvest yield data records that were converted to g dry yield ha-1, assuming corn was harvested at 65% moisture . Following data filtering, the importance of very high flux events was determined to identify hot moments and their impact on yearly flux values. We defined hot moments as measurements with values greater than four standard deviations from the mean, as statistically 99.9% of the population should fall within four standard deviations of the mean.