Extension services can promote pasture restoration: Evidence from Brazil's low carbon agriculture plan.

Innovation and improved practices in the livestock sector represent key opportunities tomeet global climate goals. This paper provides evidence that extension services can pro-mote pasture restoration in cattle ranching in Brazil. We use a randomized controlledtrial implemented in the context of the ABC Cerrado (a large-scale program launched in2014 aimed at fostering technology adoption through a combination of training andtechnical assistance) to examine the effects of different types of extension on agriculturalpractices, input use, and productivity. Providing technical assistance to previously trainedproducers promoted pasture restoration, induced farmers to use inputs more intensively,helped them to improve their management and soil conservation practices, and substan-tially increased revenues. A cost­benefit calculation indicates that US$1 invested in theABC Cerrado program increased profits by US$1.08 to $1.45. Incorporating carbonsavings amplifies this return considerably.

A MODIS-based scalable remote sensing method to estimate sowing and harvest dates of soybean crops in Mato Grosso, Brazil.

Large-scale agriculture in the state of Mato Grosso, Brazil is a major contributor to global food supplies, but its continued productivity is vulnerable to contracting wet seasons and increased exposure to extreme temperatures. Sowing dates serve as an effective adaptation strategy to these climate perturbations. By controlling the weather experienced by crops and influencing the number of successive crops that can be grown in a year, sowing dates can impact both individual crop yields and cropping intensities. Unfortunately, the spatiotemporally resolved crop phenology data necessary to understand sowing dates and their relationship to crop yield are only available over limited years and regions. To fill this data gap, we produce a 500 m rainfed soy (Glycine max) sowing and harvest date dataset for Mato Grosso from 2004 to 2014 using a novel time series analysis method for Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery, adapted for implementation in Google Earth Engine (GEE). Our estimates reveal that soy sowing and harvest dates varied widely (about 2 months) from field to field, confirming the need for spatially resolved crop timing information. An interannual trend toward earlier sowing dates occurred independently of variations in wet season onset, and may be attributed to an improvement in logistic or economic constraints that previously hampered early sowing. As anticipated, double cropped fields in which two crops are grown in succession are planted earlier than single cropped fields. This difference shrank, however, as sowing of single cropped fields occurred closer to the wet season onset in more recent years. The analysis offers insights about sowing behavior in response to historical climate variations which could be extended to understand sowing response under climate change in Mato Grosso.

Are Brazil's Deforesters Avoiding Detection?

Rates of deforestation reported by Brazil's official deforestation monitoring system have declined dramatically in the Brazilian Amazon. Much of Brazil's success in its fight against deforestation has been credited to a series of policy changes put into place between 2004 and 2008. In this research, we posit that one of these policies, the decision to use the country's official system for monitoring forest loss in the Amazon as a policing tool, has incentivized landowners to deforest in ways and places that evade Brazil's official monitoring and enforcement system. As a consequence, we a) show or b) provide several pieces of suggestive evidence that recent successes in protecting monitored forests in the Brazilian Amazon may be doing less to protect the region's forests than previously assumed.

Addressing rainfall data selection uncertainty using connections between rainfall and streamflow.

Studies of the hydroclimate at regional scales rely on spatial rainfall data products, derived from remotely-sensed (RS) and in-situ (IS, rain gauge) observations. Because regional rainfall cannot be directly measured, spatial data products are biased. These biases pose a source of uncertainty in environmental analyses, attributable to the choices made by data-users in selecting a representation of rainfall. We use the rainforest-savanna transition region in Brazil to show differences in the statistics describing rainfall across nine RS and interpolated-IS daily rainfall datasets covering the period of 1998-2013. These differences propagate into estimates of temporal trends in monthly rainfall and descriptive hydroclimate indices. Rainfall trends from different datasets are inconsistent at river basin scales, and the magnitude of index differences is comparable to the estimated bias in global climate model projections. To address this uncertainty, we evaluate the correspondence of different rainfall datasets with streamflow from 89 river basins. We demonstrate that direct empirical comparisons between rainfall and streamflow provide a method for evaluating rainfall dataset performance across multiple areal (basin) units. These results highlight the need for users of rainfall datasets to quantify this "data selection uncertainty" problem, and either justify data use choices, or report the uncertainty in derived results.

Cattle ranching intensification in Brazil can reduce global greenhouse gas emissions by sparing land from deforestation.

This study examines whether policies to encourage cattle ranching intensification in Brazil can abate global greenhouse gas (GHG) emissions by sparing land from deforestation. We use an economic model of global land use to investigate, from 2010 to 2030, the global agricultural outcomes, land use changes, and GHG abatement resulting from two potential Brazilian policies: a tax on cattle from conventional pasture and a subsidy for cattle from semi-intensive pasture. We find that under either policy, Brazil could achieve considerable sparing of forests and abatement of GHGs, in line with its national policy targets. The land spared, particularly under the tax, is far less than proportional to the productivity increased. However, the tax, despite prompting less adoption of semi-intensive ranching, delivers slightly more forest sparing and GHG abatement than the subsidy. This difference is explained by increased deforestation associated with increased beef consumption under the subsidy and reduced deforestation associated with reduced beef consumption under the tax. Complementary policies to directly limit deforestation could help limit these effects. GHG abatement from either the tax or subsidy appears inexpensive but, over time, the tax would become cheaper than the subsidy. A revenue-neutral combination of the policies could be an element of a sustainable development strategy for Brazil and other emerging economies seeking to balance agricultural development and forest protection.

The climate impacts of bioenergy systems depend on market and regulatory policy contexts.

Biomass can help reduce greenhouse gas (GHG) emissions by displacing petroleum in the transportation sector, by displacing fossil-based electricity, and by sequestering atmospheric carbon. Which use mitigates the most emissions depends on market and regulatory contexts outside the scope of attributional life cycle assessments. We show that bioelectricity's advantage over liquid biofuels depends on the GHG intensity of the electricity displaced. Bioelectricity that displaces coal-fired electricity could reduce GHG emissions, but bioelectricity that displaces wind electricity could increase GHG emissions. The electricity displaced depends upon existing infrastructure and policies affecting the electric grid. These findings demonstrate how model assumptions about whether the vehicle fleet and bioenergy use are fixed or free parameters constrain the policy questions an analysis can inform. Our bioenergy life cycle assessment can inform questions about a bioenergy mandate's optimal allocation between liquid fuels and electricity generation, but questions about the optimal level of bioenergy use require analyses with different assumptions about fixed and free parameters.