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

Site characterization and systems analysis in Central Mekong

The systems addressed in this chapter and in the CGIAR Research Program on Integrated Systems for the Humid Tropics (Humidtropics) broadly include natural systems comprising biophysical, resource and climate realities; social systems made up of people, societies and their institutions; and, what some term as artificial systems built on elements of the first two (Checkland 1981). Agricultural systems, for example, modify natural systems for productive use, add infrastructure to provide markets, and modify human institutions to organize labour and services to enable the agricultural system to function. Regardless of how systems are categorized, they can be simplistically deconstructed into components and the interactions between them. In this chapter we characterize some of the Central Mekong systems, and also address some of the system dynamics, at two basic levels of resolution. Section 2 addresses regional agricultural systems consisting of one or more districts within a country, and includes variations in natural and social systems in addition to agricultural systems. Five regional cases that reflect the diversity across the Central Mekong Action Area are examined and compared. The authors focus on systems at the community or local landscape level, particularly the individual farm household component, and the variation between households within the landscape. Variables include household agricultural practices, household resources, capacity, and links to markets and institutions. Section 3 looks at diversity in the variables among farm households and the implications for livelihoods and well-being. Section 4 examines food security levels arising from specific farm household strategies and performance, how the two are related, and the implications for potential farm interventions. We conclude by comparing the types of systems examined, the differences in types of tools needed, and the differences in questions asked and learning generated. Throughout this chapter, authors refer to data from reports and articles that interested readers can find in Annex I

Decision support system for peatland management in the humid tropics

Large areas of globally important tropical peatland in Southeast Asia are threatened by land clearance, degradation and fire, jeopardising their natural functions as reservoirs of biodiversity, carbon stores and hydrological buffers. Many development projects on tropical peatlands have failed because of lack of understanding of the landscape functions of these ecosystems. Utilisation of these peatland resources for agriculture or other land use requires drainage which, unavoidably, leads to irreversible loss of peat through subsidence, resulting in severe disturbance of the substrate, CO2-emissions and problems for cultivation. To assist planners and managers in wise use of these tropical peatlands a decision support system (DSS) has been developed. This DSS, which is based on a GIS application, combines the Groundwater Modelling Computer Programme PMWIN with expert knowledge on subsidence, land use and water management. The DSS can be used to predict the long-term effects of different types of land use, e.g. peat swamp forest, sago or oil palm plantations, on the lifetime and associated CO2 release of these tropical peatlands. The type of land use dictates the required depth of the groundwater table, which on its turn has a significant effect on the sustainability of the peatland. Therefore, special attention should be paid when deciding which type of land use to pursuit. The Decision Support System (DSS) will help to improve the decision-making process. The groundwater model PMWIN was selected because it maintains a good balance between the complexity of the model (esp. regarding to its input data requirements) and the availability of input data. The groundwater model was calibrated using data from the Balingian Area, Central Sarawak, Malaysia. The model was used to predict, based on a given land use scenario, the ratio between surface and groundwater runoff, the depth of the groundwater table and recharge and discharge zones of the peat dome. Various land use scenarios, each with its own specific water management requirements, were developed and used to predict the long-term changes in ground level and associated CO2 release. For each scenario the following outcome was generated: time span after which the water management systems have to be deepened, time span after which gravity drainage is no longer possible, time span for peat disappearance. Final results are presented in the form of maps generated by the GIS application. These maps serve as a communication tool with stakeholders to demonstrate what the hydrological effects are on for instance a certain land use type and drainage system lay-out

Cumulative Prognostic Score Predicting Mortality in Patients Older Than 80 Years Admitted to the ICU.

OBJECTIVES: To develop a scoring system model that predicts mortality within 30 days of admission of patients older than 80 years admitted to intensive care units (ICUs). DESIGN: Prospective cohort study. SETTING: A total of 306 ICUs from 24 European countries. PARTICIPANTS: Older adults admitted to European ICUs (N = 3730; median age = 84 years [interquartile range = 81-87 y]; 51.8% male). MEASUREMENTS: Overall, 24 variables available during ICU admission were included as potential predictive variables. Multivariable logistic regression was used to identify independent predictors of 30-day mortality. Model sensitivity, specificity, and accuracy were evaluated with receiver operating characteristic curves. RESULTS: The 30-day-mortality was 1562 (41.9%). In multivariable analysis, these variables were selected as independent predictors of mortality: age, sex, ICU admission diagnosis, Clinical Frailty Scale, Sequential Organ Failure Score, invasive mechanical ventilation, and renal replacement therapy. The discrimination, accuracy, and calibration of the model were good: the area under the curve for a score of 10 or higher was .80, and the Brier score was .18. At a cut point of 10 or higher (75% of all patients), the model predicts 30-day mortality in 91.1% of all patients who die. CONCLUSION: A predictive model of cumulative events predicts 30-day mortality in patients older than 80 years admitted to ICUs. Future studies should include other potential predictor variables including functional status, presence of advance care plans, and assessment of each patient's decision-making capacity

Monitoring of heart failure: comparison of left atrial pressure with intrathoracic impedance and natriuretic peptide measurements in an experimental model of ovine heart failure

Monitoring of HF (heart failure) with intracardiac pressure, intrathoracic impedance and/or natriuretic peptide levels has been advocated. We aimed to investigate possible differences in the response patterns of each of these monitoring modalities during HF decompensation that may have an impact on the potential for early therapeutic intervention. Six sheep were implanted with a LAP (left atrial pressure) sensor and a CRT-D (cardiac resynchronization therapy defibrillator) capable of monitoring impedance along six lead configuration vectors. An estimate of ALAP (LAP from admittance) was determined by linear regression. HF was induced by rapid ventricular pacing at 180 and 220 bpm (beats/min) for a week each, followed by a third week with daily pacing suspensions for increasing durations (1–5 h). Incremental pacing induced progressively severe HF reflected in increases in LAP (5.9 ± 0.4 to 24.5 ± 1.6 mmHg) and plasma atrial (20 ± 3 to 197 ± 36 pmol/l) and B-type natriuretic peptide (3.7 ± 0.7 to 32.7 ± 5.4 pmol/l) (all P<0.001) levels. All impedance vectors decreased in proportion to HF severity (all P<0.001), with the LVring (left ventricular)-case vector correlating best with LAP (r2=0.63, P<0.001). Natriuretic peptides closely paralleled rapid acute changes in LAP during alterations in pacing (P<0.001), whereas impedance changes were delayed relative to LAP. ALAP exhibited good agreement with LAP. In summary, impedance measured with an LV lead correlates significantly with changes in LAP, but exhibits a delayed response to acute alterations. Natriuretic peptides respond rapidly to acute LAP changes. Direct LAP, impedance and natriuretic peptide measurements all show promise as early indicators of worsening HF. ALAP provides an estimate of LAP that may be clinically useful