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, mostly in developing countries (Vermeulen et al 2012). Agricultural intensification and expansion in these regions is expected to catalyze the most significant relative increases 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 and Asia are predominately managed by smallholders, with 80% of land holdings smaller than ten hectares (FAO 2012). One can therefore posit that smallholder farming significantly impacts the GHG balance of these regions today and will continue to do so in the near future. However, our understanding of the effect smallholder farming has on the Earth's climate system is remarkably limited. Data quantifying existing and reduced GHG emissions and removals of smallholder production systems are available for only a handful of crops, livestock, and agroecosystems (Herrero et al 2008, Verchot et al 2008, Palm et al 2010). For example, fewer than fifteen studies of nitrous oxide emissions from soils have taken place in sub-Saharan Africa, leaving the rate of emissions virtually undocumented. Due to a scarcity of data on GHG sources and sinks, most developing countries currently quantify agricultural emissions and reductions using IPCC Tier 1 emissions factors. However, current Tier 1 emissions factors are either calibrated to data primarily derived from developed countries, where agricultural production conditions are dissimilar to that in which the majority of smallholders operate, or from data that are sparse or of mixed quality in developing countries (IPCC 2006). For the most part, there are insufficient emissions data characterizing smallholder agriculture to evaluate the level of accuracy or inaccuracy of current emissions estimates. Consequentially, there is no reliable information on the agricultural GHG budgets for developing economies. This dearth of information constrains the capacity to transition to low-carbon agricultural development, opportunities for smallholders to capitalize on carbon markets, and the negotiating position of developing countries in global climate policy discourse. Concerns over the poor state of information, in terms of data availability and representation, have fueled appeals for new approaches to quantifying GHG emissions and removals from smallholder agriculture, for both existing conditions and mitigation interventions (Berry and Ryan 2013, Olander et al 2013). Considering the dependence of quantification approaches on data and the current data deficit for smallholder systems, it is clear that in situ measurements must be a core part of initial and future strategies to improve GHG inventories and develop mitigation measures for smallholder agriculture. Once more data are available, especially for farming systems of high priority (e.g., those identified through global and regional rankings of emission hotspots or mitigation leverage points), better cumulative estimates and targeted actions will become possible. Greenhouse gas measurements in agriculture are expensive, time consuming, and error prone. These challenges are exacerbated by the heterogeneity of smallholder systems and landscapes and the diversity of methods used. Concerns over methodological rigor, measurement costs, and the diversity of approaches, coupled with the demand for robust information suggest it is germane for the scientific community to establish standards of measurements—'a protocol'—for quantifying GHG emissions from smallholder agriculture. A standard protocol for use by scientists and development organizations will help generate consistent, comparable, and reliable data on emissions baselines and allow rigorous comparisons of mitigation options. Besides enhancing data utility, a protocol serves as a benchmark for non-experts to easily assess data quality. Obviously many such protocols already exist (e.g., GraceNet, Parkin and Venterea 2010). None, however, account for the diversity and complexity of smallholder agriculture, quantify emissions and removals from crops, livestock, and biomass together to calculate the net balance, or are adapted for the research environment of developing countries; conditions that warrant developing specific methods. Here we summarize an approach being developed by the Consultative Group on International Agricultural Research's (CGIAR) Climate Change, Agriculture, and Food Security Program (CCAFS) and partners. The CGIAR-CCAFS smallholder GHG quantification protocol aims to improve quantification of baseline emission levels and support mitigation decisions. The protocol introduces five novel quantification elements relevant for smallholder agriculture (figure 1). First, it stresses the systematic collection of 'activity data' to describe the type, distribution, and extent of land management activities in landscapes cultivated by smallholder. Second, it advocates an informed sampling approach that concentrates measurement activities on emission hotspots and leverage points to capture heterogeneity and account for the diversity and complexity of farming activities. Third, it quantifies emissions at multiple spatial scales, whole-farm and landscape, to provide information targeted to household and communities decisions. Fourth, it encourages GHG research to document farm productivity and economics in addition to emissions, in recognition of the importance of agriculture to livelihoods. Fifth, it develops cost-differentiated measurement solutions that optimize the relationships among scale, cost, and accuracy. Each of the five innovations is further described in the main article

The Rural Household Multi-Indicator Survey (RHoMIS): A rapid, cost-effective and flexible tool for farm household characterisation, targeting interventions and monitoring progress towards climate-smart agriculture

RHoMIS is a rapid, cheap, digital farm household-level survey and analytical engine for characterizing, targeting and monitoring agricultural performance. RHoMIS captures information describing farm productivity and practices, nutrition, food security, gender equity, climate and poverty. RHoMIS is action-ready, tested and adapted for diverse systems in more than 7,000 households across the global tropics. Want more info? See: http://rhomis.net

Evidence- and risk-based planning for food security under climate change

Planning robust climate-smart development programs can be done today with existing information. We propose a risk-household-option modeling approach to address household food security under climate change in Africa. Through a case study in Niger, we demonstrate that prioritizing CSA is possible by taking into account livelihood status, risks, and potential effects of CSA practices

Evidence- and risk-based planning for food security under climate change: Results of a modeling approach for climate-smart agriculture programming

Planning robust climate-smart development programs can be done today with existing information. We propose a risk-household-option modeling approach to address household food security under climate change in Africa. hrough a case study in Niger, we demonstrate that prioritizing CSA is possible by taking into account livelihood status, risks, and potential effects of CSA practices

Surveillance of Climate-smart Agriculture for Nutrition (SCAN): Innovations for monitoring climate, agriculture and nutrition at scale

Climate change will affect the ability to deliver not only the quantity but also the type and quality of food necessary for nutritious diets. Global and regional 'climate-smart agriculture' initiatives offer an opportunity to mitigate climate impacts and improve nutrition outcomes at scale. The Surveillance of Climate-smart Agriculture for Nutrition (SCAN) project develops new way to acquire, integrate and analyze data to determine what is climate-smart and nutrition-sensitive

Kinetics of stochastically-gated diffusion-limited reactions and geometry of random walk trajectories

In this paper we study the kinetics of diffusion-limited, pseudo-first-order A + B -> B reactions in situations in which the particles' intrinsic reactivities vary randomly in time. That is, we suppose that the particles are bearing "gates" which interchange randomly and independently of each other between two states - an active state, when the reaction may take place, and a blocked state, when the reaction is completly inhibited. We consider four different models, such that the A particle can be either mobile or immobile, gated or ungated, as well as ungated or gated B particles can be fixed at random positions or move randomly. All models are formulated on a $d$-dimensional regular lattice and we suppose that the mobile species perform independent, homogeneous, discrete-time lattice random walks. The model involving a single, immobile, ungated target A and a concentration of mobile, gated B particles is solved exactly. For the remaining three models we determine exactly, in form of rigorous lower and upper bounds, the large-N asymptotical behavior of the A particle survival probability. We also realize that for all four models studied here such a probalibity can be interpreted as the moment generating function of some functionals of random walk trajectories, such as, e.g., the number of self-intersections, the number of sites visited exactly a given number of times, "residence time" on a random array of lattice sites and etc. Our results thus apply to the asymptotical behavior of the corresponding generating functions which has not been known as yet.Comment: Latex, 45 pages, 5 ps-figures, submitted to PR

Insulin degludec improves long-term glycaemic control similarly to insulin glargine but with fewer hypoglycaemic episodes in patients with advanced type 2 diabetes on basal-bolus insulin therapy.

The aim of the present study was to compare the long-term safety and efficacy of insulin degludec with those of insulin glargine in patients with advanced type 2 diabetes (T2D) over 78 weeks (the 52-week main trial and a 26-week extension). Patients were randomized to once-daily insulin degludec or insulin glargine, with mealtime insulin aspart ± metformin ± pioglitazone, and titrated to pre-breakfast plasma glucose values of 3.9-4.9 mmol/l (70-88 mg/dl). After 78 weeks, the overall rate of hypoglycaemia was 24% lower (p = 0.011) and the rate of nocturnal hypoglycaemia was 31% lower (p = 0.016) with insulin degludec in the extension trial set, while both groups of patients achieved similar glycaemic control. Rates of adverse events and total insulin doses were similar for both groups in the safety analysis set. During 18 months of treatment, insulin degludec + mealtime insulin aspart ± oral antidiabetic drugs in patients with T2D improves glycaemic control similarly, but confers lower risks of overall and nocturnal hypoglycaemia than with insulin glargine treatment

Order statistics of the trapping problem

When a large number N of independent diffusing particles are placed upon a site of a d-dimensional Euclidean lattice randomly occupied by a concentration c of traps, what is the m-th moment of the time t_{j,N} elapsed until the first j are trapped? An exact answer is given in terms of the probability Phi_M(t) that no particle of an initial set of M=N, N-1,..., N-j particles is trapped by time t. The Rosenstock approximation is used to evaluate Phi_M(t), and it is found that for a large range of trap concentracions the m-th moment of t_{j,N} goes as x^{-m} and its variance as x^{-2}, x being ln^{2/d} (1-c) ln N. A rigorous asymptotic expression (dominant and two corrective terms) is given for for the one-dimensional lattice.Comment: 11 pages, 7 figures, to be published in Phys. Rev.

Diffusion with random distribution of static traps

The random walk problem is studied in two and three dimensions in the presence of a random distribution of static traps. An efficient Monte Carlo method, based on a mapping onto a polymer model, is used to measure the survival probability P(c,t) as a function of the trap concentration c and the time t. Theoretical arguments are presented, based on earlier work of Donsker and Varadhan and of Rosenstock, why in two dimensions one expects a data collapse if -ln[P(c,t)]/ln(t) is plotted as a function of (lambda t)^{1/2}/ln(t) (with lambda=-ln(1-c)), whereas in three dimensions one expects a data collapse if -t^{-1/3}ln[P(c,t)] is plotted as a function of t^{2/3}lambda. These arguments are supported by the Monte Carlo results. Both data collapses show a clear crossover from the early-time Rosenstock behavior to Donsker-Varadhan behavior at long times.Comment: 4 pages, 6 figure