Early action supports smallholder farms

Rural communities rely on smallholder maize farms for subsistence agriculture, the main driver of local economic activity and food security. However, their planted area estimates are unknown in most developing countries. This study explores the use of Sentinel-1 and Sentinel-2 data to map smallholder maize farms. The random forest (RF), support vector (SVM) machine learning algorithms and model stacking (ST) were applied. Results show that the classification of combined Sentinel-1 and Sentinel-2 data improved the RF, SVM and ST algorithms by 24.2%, 8.7%, and 9.1%, respectively, compared to the classification of Sentinel-1 data individually. Similarities in the estimated areas (7001.35 ± 1.2 ha for RF, 7926.03 ± 0.7 ha for SVM and 7099.59 ± 0.8 ha for ST) show that machine learning can estimate smallholder maize areas with high accuracies. The study concludes that the single-date Sentinel-1 data were insufficient to map smallholder maize farms. However, single-date Sentinel-1 combined with Sentinel-2 data were sufficient in mapping smallholder farms. These results can be used to support the generation and validation of national crop statistics, thus contributing to food security.

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Differential Effects of Refeeding on Melanocortin-Responsive Neurons in the Hypothalamic Paraventricular Nucleus

Endocrinology ◽

10.1210/en.2008-0411 ◽

2008 ◽

Vol 149 (9) ◽

pp. 4329-4335 ◽

Cited By ~ 17

Author(s):

Edith Sánchez ◽

Praful S. Singru ◽

Runa Acharya ◽

Monica Bodria ◽

Csaba Fekete ◽

...

Keyword(s):

Gene Expression ◽

Paraventricular Nucleus ◽

Hypothalamic Paraventricular Nucleus ◽

Mrna Levels ◽

Sprague Dawley ◽

Early Action ◽

Sprague Dawley Rats ◽

Melanocortin Signaling ◽

Agouti Related Protein ◽

Serum Tsh

To explore the effect of refeeding on recovery of TRH gene expression in the hypothalamic paraventricular nucleus (PVN) and its correlation with the feeding-related neuropeptides in the arcuate nucleus (ARC), c-fos immunoreactivity (IR) in the PVN and ARC 2 h after refeeding and hypothalamic TRH, neuropeptide Y (NPY) and agouti-related protein (AGRP) mRNA levels 4, 12, and 24 h after refeeding were studied in Sprague-Dawley rats subjected to prolonged fasting. Despite rapid reactivation of proopiomelanocortin neurons by refeeding as demonstrated by c-fos IR in ARC α-MSH-IR neurons and ventral parvocellular subdivision PVN neurons, c-fos IR was present in only 9.7 ± 1.1% hypophysiotropic TRH neurons. Serum TSH levels remained suppressed 4 and 12 h after the start of refeeding, returning to fed levels after 24 h. Fasting reduced TRH mRNA compared with fed animals, and similar to TSH, remained suppressed at 4 and 12 h after refeeding, returning toward normal at 24 h. AGRP and NPY gene expression in the ARC were markedly elevated in fasting rats, AGRP mRNA returning to baseline levels 12 h after refeeding and NPY mRNA remaining persistently elevated even at 24 h. These data raise the possibility that refeeding-induced activation of melanocortin signaling exerts differential actions on its target neurons in the PVN, an early action directed at neurons that may be involved in satiety, and a later action on hypophysiotropic TRH neurons involved in energy expenditure, potentially mediated by sustained elevations in AGRP and NPY. This response may be an important homeostatic mechanism to allow replenishment of depleted energy stores associated with fasting.

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An analysis of US greenhouse gas cap-and-trade proposals using a forward-looking economic model

Environment and Development Economics ◽

10.1017/s1355770x10000495 ◽

2011 ◽

Vol 16 (2) ◽

pp. 155-176 ◽

Cited By ~ 6

Author(s):

ANGELO COSTA GURGEL ◽

SERGEY PALTSEV ◽

JOHN REILLY ◽

GILBERT METCALF

Keyword(s):

Greenhouse Gas ◽

Ghg Emissions ◽

Substantial Effect ◽

Welfare Cost ◽

Economic Implications ◽

Early Action ◽

Labor Tax ◽

Long Term Effect ◽

Capital Tax ◽

Forward Looking

ABSTRACTWe develop a forward-looking version of the recursive dynamic MIT Emissions Prediction and Policy Analysis (EPPA) model, and apply it to examine the economic implications of proposals in the US Congress to limit greenhouse gas (GHG) emissions. We find that shocks in the consumption path are smoothed out in the forward-looking model and that the lifetime welfare cost of GHG policy is lower than in the recursive model, since the forward-looking model can fully optimize over time. The forward-looking model allows us to explore issues for which it is uniquely well suited, including revenue-recycling and early action crediting. We find capital tax recycling to be more welfare-cost reducing than labor tax recycling because of its long-term effect on economic growth. Also, there are substantial incentives for early action credits; however, when spread over the full horizon of the policy they do not have a substantial effect on lifetime welfare costs.

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Flood forecast skill for Early Action: Results and Learnings from the development of the Early-Action Protocol for Floods in Uganda

10.5194/egusphere-egu21-16169 ◽

2021 ◽

Author(s):

Andrea Ficchì ◽

Hannah Cloke ◽

Linda Speight ◽

Douglas Mulangwa ◽

Irene Amuron ◽

...

Keyword(s):

River Flow ◽

National Level ◽

Scale Up ◽

Forecast Skill ◽

Red Cross ◽

Flood Forecast ◽

Global Team ◽

Early Action ◽

Skill Scores ◽

Local Risk

<p>Global flood forecasting systems are helpful in complementing local resources and in-country data to support humanitarians and trigger early action before an impactful flood occurs. Freely available global flood forecast information from the European Commission&#8217;s Global Flood Awareness System (GloFAS, a Copernicus EMS service) is being used by the Uganda Red Cross Society (URCS) alongside in-country knowledge to develop appropriate triggers for early actions for flood preparedness, within the Forecast-based Financing (FbF) initiative. To scale up the first FbF pilot to a national level, in 2020 URCS collaborated with several partners including the Red Cross Red Crescent Climate Centre (RCCC), the Uganda&#8217;s Ministry of Water and Environment, through the Directorate of Water Resources Management (DWRM), the Uganda National Meteorological Authority (UNMA), the 510 Global team and the University of Reading, through the UK-supported project Forecasts for Anticipatory Humanitarian Action (FATHUM). The new Early Action Protocol (EAP) for floods, submitted to the IFRC&#8217;s validation committee in September 2020, is now under review.</p><p>One of the aims of an EAP is to set the triggers for early action, based on forecast skill information, alongside providing a local risk analysis, and describing the early actions, operational procedures, and responsibilities. Working alongside our partners and practitioners in Uganda, we developed a methodology to tailor flood forecast skill analysis to EAP development, that could be potentially useful for humanitarians in other Countries and forecasters engaging with them. The key aim of the analysis is to identify skilful lead times and appropriate triggers for early action based on available operational forecasts, considering action parameters, such as an Action Lifetime of 30 days, and focusing on relevant flood thresholds and skill scores. We analysed the skill of probabilistic flood forecasts from the operational GloFAS (v2.1) system across Uganda against river flow observations and reanalysis data. One of the challenges was to combine operational needs with statistical robustness requirements, using relevant flood thresholds for action. Here we present the results from the analysis carried out for Uganda and the verification workflow, that we plan to make openly available to all practitioners and scientists working on the implementation of forecast-based actions.</p>

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Early Action Prediction Using 3DCNN With LSTM and Bidirectional LSTM

SSRN Electronic Journal ◽

10.2139/ssrn.3815963 ◽

2021 ◽

Author(s):

Manju D ◽

Dr. Seetha M. ◽

Dr. Sammulal P.

Keyword(s):

Action Prediction ◽

Early Action ◽

Bidirectional Lstm

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Prevalence of Brucellosis in Dairy Cattle in Organized and Smallholder Farms in Some Selected Areas of Bangladesh

Bangladesh Journal of Veterinary Medicine ◽

10.3329/bjvm.v12i2.21280 ◽

2014 ◽

Vol 12 (2) ◽

pp. 167-171

Author(s):

MAS Sarker ◽

MS Rahman ◽

MT Islam ◽

AKMA Rahman ◽

MB Rahman ◽

...

Keyword(s):

Age Groups ◽

Dairy Farm ◽

Economic Loss ◽

Screening Tests ◽

Ring Test ◽

Serum Samples ◽

Smallholder Farms ◽

Three Samples ◽

Holstein Friesian ◽

Confirmatory Tests

Brucellosis causes a great economic loss to the livestock industries through abortion, infertility,birth of weak and dead offspring, increased calving interval and reduction of milk yield and it is endemic in Bangladesh. In this study we collected milk and blood samples simultaneously from533 cows of Central Cattle Breeding and Dairy Farm, Savar, Dhaka and different Upazilas of Gaibandha and Mymensingh District. Five hundred thirty three samples were examined for antibodies to Brucella using the Milk Ring Test (MRT) and Rose Bengal Test (RBT). Overall 2.62 % of milk samples were positive according to MRT, while2.06 % of the serum samples were positive to the RBT. Only 6 (1.13 %) of the samples were positive for both test (MRT and RBT). Out of 312 samples only 10 (3.20 %) were positive to MRT while 8(2.06%) were positive to RBT in Holstein Friesian cross (p>0.05) on the other hand out of 221 samples only 4 (1.80%) were positive to MRT while 3(1.35%) were positive to RBT in Sahiwal cross. The prevalence of brucellosis was significantly higher in the age group of > 5 years than other age groups (p?0.01) on both test (MRT 2.75%and RBT 2.25%).Based on parity, significantly higher prevalence (MRT 2.93% and RBT 2.44%) of MRT and RBT were obtainedin parity 3-5in comparison to other parity group (p?0.01). It is, however, obvious that although the MRT is 1st-line screening tests for brucellosis in cows in some countries, their lack of specificity is of concern. Therefore, the requirement for other confirmatory tests that are more specific should be used for the diagnosis of the disease, especially in Bangladesh.DOI: http://dx.doi.org/10.3329/bjvm.v12i2.21280 Bangl. J. Vet. Med. (2014). 12 (2): 167-171

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Soil fertility and biodiversity on organic and conventional smallholder farms in Kenya

Applied Soil Ecology ◽

10.1016/j.apsoil.2018.10.020 ◽

2019 ◽

Vol 134 ◽

pp. 85-97 ◽

Cited By ~ 4

Author(s):

Juliet Wanjiku Kamau ◽

Lisa Biber-Freudenberger ◽

John P.A. Lamers ◽

Till Stellmacher ◽

Christian Borgemeister

Keyword(s):

Soil Fertility ◽

Smallholder Farms

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Forecasting Vegetation Condition with a Bayesian Auto-regressive Distributed Lags (BARDL) Model

10.5194/nhess-2021-223 ◽

2021 ◽

Author(s):

Edward E. Salakpi ◽

Peter D. Hurley ◽

James M. Muthoka ◽

Adam B. Barrett ◽

Andrew Bowell ◽

...

Keyword(s):

Soil Moisture ◽

Lead Time ◽

Expert Knowledge ◽

Research Work ◽

Early Warning Systems ◽

Ar Model ◽

Lead Times ◽

Vegetation Condition ◽

Early Action ◽

Auto Regressive

Abstract. Droughts form a large part of climate/weather-related disasters reported globally. In Africa, pastoralists living in the Arid and Semi-Arid Lands (ASALs) are the worse affected. Prolonged dry spells that cause vegetation stress in these regions have resulted in the loss of income and livelihoods. To curb this, global initiatives like the Paris Agreement and the United Nations recognised the need to establish Early Warning Systems (EWS) to save lives and livelihoods. Existing EWS use a combination of Satellite Earth Observation (EO) based biophysical indicators like the Vegetation Condition Index (VCI) and socio-economic factors to measure and monitor droughts. Most of these EWS rely on expert knowledge in estimating upcoming drought conditions without using forecast models. Recent research has shown that the use of robust algorithms like Auto-Regression, Gaussian Processes and Artificial Neural Networks can provide very skilled models for forecasting vegetation condition at short to medium range lead times. However, to enable preparedness for early action, forecasts with a longer lead time are needed. The objective of this research work is to develop models that forecast vegetation conditions at longer lead times on the premise that vegetation condition is controlled by factors like precipitation and soil moisture. To achieve this, we used a Bayesian Auto-Regressive Distributed Lag (BARDL) modelling approach which enabled us to factor in lagged information from Precipitation and Soil moisture levels into our VCI forecast model. The results showed a ∼2-week gain in the forecast range compared to the univariate AR model used as a baseline. The R2 scores for the Bayesian ARDL model were 0.94, 0.85 and 0.74, compared to the AR model's R2 of 0.88, 0.77 and 0.65 for 6, 8 and 10 weeks lead time respectively.

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