Todd S. Rosenstock

Beyond climate-smart agriculture: toward safe operating spaces for global food systems

Agriculture is considered to be “climate-smart” when it contributes to increasing food security, adaptation and mitigation in a sustainable way. This new concept now dominates current discussions in agricultural development because of its capacity to unite the agendas of the agriculture, development and climate change communities under one brand. In this opinion piece authored by scientists from a variety of international agricultural and climate research communities, we argue that the concept needs to be evaluated critically because the relationship between the three dimensions is poorly understood, such that practically any improved agricultural practice can be considered climate-smart. This lack of clarity may have contributed to the broad appeal of the concept. From the understanding that we must hold ourselves accountable to demonstrably better meet human needs in the short and long term within foreseeable local and planetary limits, we develop a conceptualization of climate-smart agriculture as agriculture that can be shown to bring us closer to safe operating spaces for agricultural and food systems across spatial and temporal scales. Improvements in the management of agricultural systems that bring us significantly closer to safe operating spaces will require transformations in governance and use of our natural resources, underpinned by enabling political, social and economic conditions beyond incremental changes. Establishing scientifically credible indicators and metrics of long-term safe operating spaces in the context of a changing climate and growing social-ecological challenges is critical to creating the societal demand and political will required to motivate deep transformations. Answering questions on how the needed transformational change can be achieved will require actively setting and testing hypotheses to refine and characterize our concepts of safer spaces for social-ecological systems across scales. This effort will demand prioritizing key areas of innovation, such as (1) improved adaptive management and governance of social-ecological systems; (2) development of meaningful and relevant integrated indicators of social-ecological systems; (3) gathering of quality integrated data, information, knowledge and analytical tools for improved models and scenarios in time frames and at scales relevant for decision-making; and (4) establishment of legitimate and empowered science policy dialogues on local to international scales to facilitate decision making informed by metrics and indicators of safe operating spaces.

Reducing greenhouse gas emissions and adapting agricultural management for climate change in developing countries: providing the basis for action

Agriculture in developing countries has attracted increasing attention in international negotiations within the United Nations Framework Convention on Climate Change for both adaptation to climate change and greenhouse gas mitigation. However, there is limited understanding about potential complementarity between management practices that promote adaptation and mitigation, and limited basis to account for greenhouse gas emission reductions in this sector. The good news is that the global research community could provide the support needed to address these issues through further research linking adaptation and mitigation. In addition, a small shift in strategy by the Intergovernmental Panel on Climate Change (IPCC) and ongoing assistance from agricultural organizations could produce a framework to move the research and development from concept to reality. In turn, significant progress is possible in the near term providing the basis for UNFCCC negotiations to move beyond discussion to action for the agricultural sector in developing countries.

The role of large environmental noise in masting: General model and example from pistachio trees

Masting is synchronous, highly variable reproduction in a plant population, or synchronized boom-bust cycles of reproduction. These pulses of resources have cascading effects through ecosystems, and thus it is important to understand where they come from. How does masting happen and synchronize? In this paper, we suggest a mechanism for this. The mechanism is inspired by data from a pistachio orchard, which suggest that large environmental noise may play a crucial role in inducing masting in plant populations such as pistachio. We test this idea through development and analysis of a mathematical model of plant reproduction. We start with a very simple model, and generalize it based on the current models of plant reproduction and masting. Our results suggest that large environmental noise may indeed be a crucial part of the mechanism of masting in certain types of plant populations, including pistachio. This is a specific example of an important functional consequence of the interactions between stochasticity and nonlinearity.

Toward a protocol for quantifying the greenhouse gas balance and identifying mitigation options in smallholder farming systems

Globally, agriculture is directly responsible for 14% of annual greenhouse gas(GHG) emissions and induces an additional 17% through land use change, mostlyin developing countries (Vermeulen et al 2012). Agricultural intensification andexpansion in these regions is expected to catalyze the most significant relativeincreases in agricultural GHG emissions over the next decade (Smith et al 2008,Tilman et al 2011). Farms in the developing countries of sub-Saharan Africa andAsia are predominately managed by smallholders, with 80% of land holdingssmaller than ten hectares (FAO 2012). One can therefore posit that smallholderfarming significantly impacts the GHG balance of these regions today and willcontinue to do so in the near future.However, our understanding of the effect smallholder farming has on theEarth’s climate system is remarkably limited. Data quantifying existing andreduced GHG emissions and removals of smallholder production systems areavailable for only a handful of crops, livestock, and agroecosystems (Herrero et al2008, Verchot et al 2008, Palm et al 2010). For example, fewer than fifteenstudies of nitrous oxide emissions from soils have taken place in sub-SaharanAfrica, leaving the rate of emissions virtually undocumented. Due to a scarcity ofdata on GHG sources and sinks, most developing countries currently quantifyagricultural emissions and reductions using IPCC Tier 1 emissions factors.However, current Tier 1 emissions factors are either calibrated to data primarilyderived from developed countries, where agricultural production conditions aredissimilar to that in which the majority of smallholders operate, or from data thatare sparse or of mixed quality in developing countries (IPCC 2006). For the mostpart, there are insufficient emissions data characterizing smallholder agricultureto evaluate the level of accuracy or inaccuracy of current emissions estimates.Consequentially, there is no reliable information on the agricultural GHG budgetsfor developing economies. This dearth of information constrains the capacity totransition to low-carbon agricultural development, opportunities for smallholdersto capitalize on carbon markets, and the negotiating position of developingcountries in global climate policy discourse.Concerns over the poor state of information, in terms of data availability andrepresentation, have fueled appeals for new approaches to quantifying GHGemissions and removals from smallholder agriculture, for both existing conditionsand mitigation interventions (Berry and Ryan 2013, Olander et al 2013).Considering the dependence of quantification approaches on data and the currentdata deficit for smallholder systems, it is clear that in situ measurements must bea core part of initial and future strategies to improve GHG inventories and

Methods for the quantification of GHG emissions at the landscape level for developing countries in smallholder contexts

Landscape scale quantification enables farmers to pool resources and expertise. However, the problem remains of how to quantify these gains. This article considers current greenhouse gas (GHG) quantification methods that can be used in a landscape scale analysis in terms of relevance to areas dominated by smallholders in developing countries. In landscape scale carbon accounting frameworks, measurements are an essential element. Sampling strategies need careful design to account for all pools/fluxes and to ensure judicious use of resources. Models can be used to scale-up measurements and fill data gaps. In recent years a number of accessible models and calculators have been developed which can be used at the landscape scale in developing country areas. Some are based on the Intergovernmental Panel on Climate Change (IPCC) method and others on dynamic ecosystem models. They have been developed for a range of different purposes and therefore vary in terms of accuracy and usability. Landscape scale assessments of GHGs require a combination of ground sampling, use of data from census, remote sensing (RS) or other sources and modelling. Fitting of all of these aspects together needs to be performed carefully to minimize uncertainties and maximize the use of scarce resources. This is especially true in heterogeneous landscapes dominated by smallholders in developing countries.

Agriculture's Contribution to Nitrate Contamination of Californian Groundwater (1945-2005).

Nitrogen (N) use in intensive agriculture can degrade groundwater resources. However, considerable time lags between groundwater recharge and extraction complicate source attribution and remedial responses. We construct a historic N mass balance of two agricultural regions of California to understand trends and drivers of past and present N loading to groundwater (1945-2005). Changes in groundwater N loading result from historic changes in three factors: the extent of agriculture (cropland area and livestock herd increased 120 and 800%, respectively), the intensity of agriculture (synthetic and manure waste effluent N input rates increased by 525 and 1500%, respectively), and the efficiency of agriculture (crop and milk production per unit of N input increased by 25 and 19%, respectively). The net consequence has been a greater-than-order-of-magnitude increase in nitrate (NO) loading over the time period, with 163 Gg N yr now being leached to groundwater from approximately 1.3 million ha of farmland (not including alfalfa [ L.]). Meeting safe drinking water standards would require NO leaching reductions of over 70% from current levels through reductions in excess manure applications, which accounts for nearly half of all groundwater N loading, and through synthetic N management improvements. This represents a broad challenge given current economic and technical conditions of California farming if farm productivity is to be maintained. The findings illustrate the growing tension-characteristic of agricultural regions globally-between intensifying food, feed, fiber, and biofuel production and preserving clean water.

How climate-smart is conservation agriculture (CA)? – its potential to deliver on adaptation, mitigation and productivity on smallholder farms in southern Africa

Climate resilient cropping systems are required to adapt to the increasing threats of climate change projected for Southern Africa and to better manage current climate variability. Conservation agriculture (CA) has been proposed among technologies that are climate-smart. For a cropping system to be labelled “climate-smart” it has to deliver three benefits: a) adapt to the effects of climate and be of increased resilience; b) mitigate climate effects by sequestering carbon (C) and reducing greenhouse gas emissions (GHG); and c) sustainably increase productivity and income. Research on smallholder farms from Southern Africa was analysed to assess if CA can deliver on the three principles of climate-smart agriculture. Results from Southern Africa showed that CA systems have a positive effect on adaptation and productivity, but its mitigation potential lags far behind expectations. CA systems maintain higher infiltration rates and conserve soil moisture, which helps to overcome seasonal dry-spells. Increased productivity and profitability were recorded although a lag period of 2–5 cropping seasons is common until yield benefits become significant. Immediate economic benefits such as reduced labour requirements in some systems will make CA more attractive in the short term to farmers who cannot afford to wait for several seasons until yield benefits accrue. The available data summarizing the effects of CA on soil organic C (SOC) and reductions in greenhouse gases, are often contradictory and depend a great deal on the agro-ecological environment and the available biomass for surface residue retention. There is an urgent need for more research to better quantify the mitigation effects, as the current data are scanty. Possible co-interventions such as improved intercropping/relay cropping systems, agroforestry and other tree-based systems may improve delivery of mitigation benefits and need further exploration.

Accuracy and precision of photoacoustic spectroscopy not guaranteed

TODD S. ROSENSTOCK*, EUGENIO DIAZ-PINES†,PABLOZUAZO†,GRETAJORDAN‡,MARTINA PREDOTOVA‡,PAULMUTUO*,SHEILAABWANDA*,MARGARETTHIONG’O*,ANDREAS BUERKERT‡,MARIANAC.RUFINO§,RALFKIESE†, HENRY NEUFELDT* andKLAUS BUTTERBACH-BAHL†§*World Agroforestry Centre, PO Box 30677 United Nations Avenue, Nairobi 00100, Kenya, †Karlsruhe Institute of Technology –Institute of Meteorology and Climate Research, Atmospheric Environmental Research (KIT/IMK-IFU) Kreuzeckbahnstr. 19, 82467Garmisch-Partenkirchen, Germany, ‡University of Kassel, Section Organic Crop Production and Agroecosystems in the Tropicsand Subtropics (OPATS), Steinstr. 19, 37213 Witzenhausen, Germany, §International Livestock Research Institute, PO Box30709, Nairobi, KenyaBecause of their accuracy and precision for measuringgas concentrations, gas chromatographs (GC) are stan-dard analytical instruments used in investigations ofnitrous oxide (N

Greenhouse gas fluxes from agricultural soils of Kenya and Tanzania

Knowledge of greenhouse gas (GHG) fluxes in soils is a prerequisite to constrain national, continental, and global GHG budgets. However, data characterizing fluxes from agricultural soils of Africa are markedly limited. We measured carbon dioxide (CO2), nitrous oxide (N2O), and methane (CH4) fluxes at 10 farmer-managed sites of six crop types for 1 year in Kenya and Tanzania using static chambers and gas chromatography. Cumulative emissions ranged between 3.5–15.9 Mg CO2-C ha−1 yr−1, 0.4–3.9 kg N2O-N ha−1 yr−1, and −1.2–10.1 kg CH4-C ha−1 yr−1, depending on crop type, environmental conditions, and management. Manure inputs increased CO2 (p = 0.03), but not N2O or CH4, emissions. Soil cultivation had no discernable effect on emissions of any of the three gases. Fluxes of CO2 and N2O were 54–208% greater (p < 0.05) during the wet versus the dry seasons for some, but not all, crop types. The heterogeneity and seasonality of fluxes suggest that the available data describing soil fluxes in Africa, based on measurements of limited duration of only a few crop types and agroecological zones, are inadequate to use as a basis for estimating the impact of agricultural soils on GHG budgets. A targeted effort to understand the magnitude and mechanisms underlying African agricultural soil fluxes is necessary to accurately estimate the influence of this source on the global climate system and for determining mitigation strategies.

Nitrous oxide and methane fluxes from urine and dung deposited on Kenyan pastures

Livestock keeping is ubiquitous in tropical Africa. Urine and dung from livestock release greenhouse gases (GHGs), such as nitrous oxide (NO) and methane (CH), to the atmosphere. However, the extent of GHGs impact is uncertain due to the lack of in situ measurements in the region. Here we measured NO and CH emissions from cow urine and dung depositions in two Kenyan pastures that received different amounts of rainfall using static chambers across wet and dry seasons. Cumulative NO emissions were greater under dung+urine and urine-only patches ( 0.0001), more than three times higher in the wet compared with the dry season ( 0.0001), and higher in the farm receiving higher rainfall overall ( 0.0001). Cumulative CH emissions differed across treatments ( = 0.012), driven primarily by soil CH uptake from the urine-only treatment. Cumulative NO emissions were positively related to N input rate in excreta. However, the relationship was linear during the dry season ( 0.99; 0.001) and exponential during the wet season ( 0.99; < 0.0001). Nitrous oxide emission factors were 0.05% (dry season) and 0.18% (wet season) of N in urine and dung+urine, which is less than 10% of the IPCC Default Tier 1 emission factor of 2%. We predict that emissions from cattle urine in Kenya are approximately 1.7 Gg NO-N yr (FAO estimates 11.9 Gg NO-N yr). Our findings suggest that current estimates may overestimate the contribution of excreta to national GHG emissions and that emission factors from urine and dung need to account for agroecosystems with distinct wet and dry seasons.