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Drivers of household food availability in sub-Saharan Africa based on big data from small farms
Romain Frelat, Santiago Lopez-Ridaura, Ken E. Giller, Mario Herrero, Sabine Douxchamps, Agnes Andersson Djurfeldt, Olaf Erenstein, Ben Henderson, Menale Kassie, Birthe K. Paul, Cyrille Rigolot, Randall S. Ritzema, Daniel Rodriguez, Piet J. A. van Asten, and Mark T. van Wijk
PNAS January 12, 2016 113 (2) 458-463; first published December 28, 2015 https://doi.org/10.1073/pnas.1518384112
Romain Frelat
aInternational Livestock Research Institute, Livestock Systems and the Environment, Nairobi 00100, Kenya;bInternational Maize and Wheat Improvement Center, Sustainable Intensification and Socioeconomics programs, 06600 Mexico City, Mexico;
Santiago Lopez-Ridaura
bInternational Maize and Wheat Improvement Center, Sustainable Intensification and Socioeconomics programs, 06600 Mexico City, Mexico;
Ken E. Giller
cPlant Production Systems, Wageningen University, 6700 AK Wageningen, The Netherlands;
ORCID record for Ken E. Giller
Mario Herrero
dCommonwealth Scientific and Industrial Research Organisation, Agriculture Flagship, St. Lucia, QLD 4067, Australia;
Sabine Douxchamps
aInternational Livestock Research Institute, Livestock Systems and the Environment, Nairobi 00100, Kenya;
Agnes Andersson Djurfeldt
eDepartment of Human Geography, Lund University, S-223 62, Lund, Sweden;
Olaf Erenstein
Ben Henderson
Menale Kassie
Birthe K. Paul
fInternational Center for Tropical Agriculture, Tropical Forages Program, International Centre of Insect Physiology and Ecology Duduville Campus, Nairobi, Kenya;
Cyrille Rigolot
dCommonwealth Scientific and Industrial Research Organisation, Agriculture Flagship, St. Lucia, QLD 4067, Australia;gFrench National Institute for Agricultural Research, Unité Mixte de Recherche 1273 Metafort, 63122 Saint Genès-Champanelle, France;
Randall S. Ritzema
Daniel Rodriguez
hUniversity of Queensland, Queensland Alliance for Agriculture and Food Innovation, Toowoomba, QLD 4350, Australia;
Piet J. A. van Asten
iInternational Institute of Tropical Agriculture, IITA–Uganda, Kampala, Uganda
Mark T. van Wijk
For correspondence: m.vanwijk@cgiar.org
Edited by B. L. Turner, Arizona State University, Tempe, AZ, and approved November 17, 2015 (received for review September 15, 2015)
We collated a unique dataset covering land use and production data of more than 13,000 smallholder farm households in 93 sites in 17 countries across sub-Saharan Africa. The study quantifies the importance of off-farm income and market conditions across sites differing strongly in agroecology and derives generally applicable threshold values that determine whether farm households have enough food available to feed their families. These results show there is a strong need for multisectoral policy harmonization and incentives and improved interconnectedness of people to urban centers and diversification of employment sources, rather than a singular focus on agricultural development of smallholder farmers in sub-Saharan Africa.
We calculated a simple indicator of food availability using data from 93 sites in 17 countries across contrasted agroecologies in sub-Saharan Africa (>13,000 farm households) and analyzed the drivers of variations in food availability. Crop production was the major source of energy, contributing 60% of food availability. The off-farm income contribution to food availability ranged from 12% for households without enough food available (18% of the total sample) to 27% for the 58% of households with sufficient food available. Using only three explanatory variables (household size, number of livestock, and land area), we were able to predict correctly the agricultural determined status of food availability for 72% of the households, but the relationships were strongly influenced by the degree of market access. Our analyses suggest that targeting poverty through improving market access and off-farm opportunities is a better strategy to increase food security than focusing on agricultural production and closing yield gaps. This calls for multisectoral policy harmonization, incentives, and diversification of employment sources rather than a singular focus on agricultural development. Recognizing and understanding diversity among smallholder farm households in sub-Saharan Africa is key for the design of policies that aim to improve food security.
smallholder farmers
yield gap
resource scarcity
farm size
Achieving sustainable food security (i.e., the basic right of people to produce and/or purchase the food they need, without harming the social and biophysical environment) is a major challenge in a world of rapid human population growth and large-scale changes in economic development (1). In sub-Saharan Africa (SSA), production on smallholder farms is critical to the food security of the rural poor (2) and contributes the majority of food production at the national level. National policies and local interventions have profound impacts on the opportunities and constraints that affect smallholders (3). However, policy frameworks that aim to improve food security and rural livelihoods in the developing world face many uncertainties and often fail (4).
The formulation of effective policies needs adequate information on how different options affect the complex issues surrounding food security and sustainable development. A complication in generating such information is the large diversity within and among smallholder farming systems. Agroecological conditions, markets, and local cultures determine land use patterns and agricultural management across regions, whereas within a given region, farm households differ in many ways, including resource endowment, production orientation and objectives, ethnicity, education, past experience, management skills, and in the farm households' attitude toward risk. Policies by their nature have to be widely applicable, but recognizing this diversity in farm households is key to designing more effective policies to help poor farmers (5). Understanding the main drivers of household diversity and their relationship with livelihood strategies can help to better codesign and target agricultural innovations (6).
In this study, we brought together cross-sectional farm household characterization data from 93 sites in 17 countries of SSA (Fig. 1). Such a large database provides an immensely rich resource to derive descriptions linking indicators of food security and land use to the socioeconomic and biophysical environment of the smallholder farmers. We use these data to develop a simple farm household performance indicator (Fig. 2) that is robust and can be calculated based on the household information collected in different surveys. We hypothesized that a few simple but important household characteristics can be used to tease apart the large diversity in farm households and farming systems, thereby leading to an improved understanding of the main drivers of the complexity in household functioning and that these characteristics can be used predictively to inform policy options.
Map of the study sites in SSA. The size of points represents the number of households surveyed. International borders of countries are shown and labels of the countries are written following the ISO three-letter codes.
Schematic representation of the calculation of the FA at household level in energy (kcal) per capita and per day.
The Dynamics of Food Availability.
The importance of different household activities changed from households with insufficient food available to those households with ample food available across the whole dataset (Fig. 3 A and B). Households that had insufficient food available obtained their energy mainly from the consumption of food crops produced on-farm. By contrast, households with more adequate food availability (FA) depended more on cash-generating activities, although consumption of self-produced food crops still provided the base supply of energy. Consumption of self-produced food crops did not cover the food need for almost 80% of the households. Crop and livestock product sales were a substantial part of the FA indicator [expressed in potential food equivalent (PFE) energy (kcal) per capita per day] for these households, suggesting that the majority of households do not aim for full food self-sufficiency. Most households have a PFE value larger than the household's daily energy requirement (Fig. 3A), but it is important to note that the FA indicator is an indicator of potential supply (i.e., it overestimates actual FA). However, despite all of the assumptions, the FA indicator is a meaningful indicator for food security: the indicator is strongly correlated with self-scoring of food security status of food security status (Fig. S1), household level diet diversity, and the USAID Hunger and Food Insecurity Status indicator.
(A) Overall distribution of food available (FA) in energy per capita per day for the 13,567 households. Households are ordered on the x axis based on increasing FA. Each vertical bar represents one household, the colors represent its sources of energy, and the height represents the degree of FA. The red dashed line is a PFE of 2,500 kcal per capita per day, the daily energy need of a male adult (36). For the ease of interpretation, a moving average has been applied with a window length of 100 households (to smooth the curves). (B) Relative contribution of household activities to FA per FA class (not enough food available <1,500 kcal per capita per day, roughly enough food available between 1,500 and 4,000 kcal per capita per day and more than enough food available >4,000 kcal per capita per day).
Comparison between FA indicator (y axis) and reported Food Security (x axis). AFRINT (A) and N2Africa (B) report the number of meals per day (one, two, or three). CIALCA (C) reported a self-classification in (i) often not enough food, (ii) sometimes not enough food, (iii) enough food but not enough variety, and (iv) enough food and variety. The widths of the boxplot are proportional to the size of the class of reported food security. The horizontal dashed line represents the "enough food-available line" at 2,500 kcal per capita per day.
Overall, off-farm income was more important for households with higher FA: its importance as source of PFE energy increased from 12% for households with insufficient food to 27% for households with potentially more than enough food (Fig. 3B). The main intensification pathway was cash crop production, with an increasing relative importance from 4% for the households with insufficient food available to 11% in the sufficient food-available households. Across the three FA classes, the contribution of livestock to PFE was relatively conservative with a total contribution of about 20% (Fig. 3B). Within this overall contribution of livestock, though, there was a clear shift away from poultry to cattle as the level of FA increased. The contribution of food crop consumption decreased from 45% for the households with insufficient food available to 22% in the more than sufficient food-available households. The sale of food crops was stable at roughly 20%. Crop production was the major source of PFE energy, providing from 67% for the insufficient food-available households to only 55% for the households with more than sufficient food available.
Food-Availability Thresholds and Constraints.
Land, livestock, and household size explained a substantial part of FA variation (R2 of 0.33; Fig. 4D). The response curves identified by the artificial neural networks (ANNs) (these were used because they do not use a predefined response relationship and can fit highly nonlinear relationships) were robust (i.e., they had a small uncertainty). The relation between land used for cropping and FA was a nonlinear saturation curve, with a threshold at around 3 ha (Fig. 4A). This finding suggests that land productivity decreased in households with more land. FA without off-farm income increased gradually with increasing livestock ownership (Fig. 4B), although two rates of change in the response were visible: at tropical livestock unit (TLU) values below 0.2 (two goats or 20 chickens), there was a strong increase in FA per unit of increase in TLU, whereas for TLU values larger than 0.2 the slope was less steep. The FA—household size response was as expected (i.e., FA decreased as family size increased, but the overall response was flatter than the expected reciprocal relationship which is used in the calculations).
(A–C) The response curves of the ANN model using cropland (A), TLUs (B), and family size (in MAE) (C) as inputs (on the x axes) and the predicted FA response (in kcal per capita per day) on the y axes. The curves were calculated from the controlled variation of one variable, whereas the other two were set to their median values: crop land, 1.33 ha; TLU, 0.75; and household size, 4.4 MAE. The bold line shows the average response curve, and the dashed line is the average ± SD. (D) Model performance, with the observed FA (in kcal per capita per day; calculated using measured data) on the y axis and predicted FA (in kcal per capita per day) using the three earlier-mentioned variables as input of the ANN on the x axis. Both axes are in log scale. The dashed gray lines are the daily requirement thresholds of 2,500 kcal per capita per day (35).
The ANN response model with the three primary household resources was used to predict the FA "frontier" (Fig. 5). Based on the resources of the household and its size (crop land, livestock, and family size), the model predicted correctly the FA status (can a household, yes or no, produce and/or purchase enough food to feed the family?) of 72% of the households (with a PFE energy threshold set at 2,500 kcal per capita per day). We considered model performance to be satisfactory, given the fact that the FA status indicator is a binary variable with high levels of associated noise (with many farm households, close to the threshold value; Fig. 3A). Increasing family size shifted the livestock—land threshold curve upwards, so more land and livestock were needed to feed the family (Fig. 5). This simple model had a relatively good prediction power with an α error of 0.25 (probability erroneously to predict a household to have enough food available to feed the family) and a β error of 0.35 (probability erroneously to predict a household as having insufficient food available).
Predicted FA threshold curves using the ANN with cropland, livestock (in TLU), and family size (in MAE) as inputs. Different curves are the threshold values at different family sizes from 3–6 MAE. Households with productive resources larger than the values determining the curve corresponding to their size in MAE equivalents are predicted to have enough food available to feed the family.
The frontier curves shifted substantially when the environmental factors were taken into account (Fig. 6). In land but not market constrained systems (densely populated and more land intensive Africa, "L"), the land threshold was smaller (i.e., farmers were food secure with less land than predicted by the overall model of Fig. 5). With around 0.4 ha, a family of 4.4 male adult equivalents (MAEs) was predicted to be able to produce enough food and cash to feed the family. Households in market-constrained environments ("M," "LM") needed more land to achieve sufficient FA values, with livestock being important ("LM") or necessary ("M"). There was only a small increase in the predictive power of the threshold model (75% correctly predicted status of FA) when environmental factors were included. This limited increase in predictive power is likely to be caused by the noisy nature of this binary dependent variable.
The predicted FA threshold curves of the ANN model with cropland, livestock (in TLU), and family size expressed in MAE as inputs, together with the environmental constraint variables (for an explanation, see Methods). The bold line shows the average response and threshold curves (as in Fig. 5 but now for a family size of 4.4 MAE). The dashed lines show thresholds for some contrasting constrained environments: land and market constrained environment (LM) (18% of households), land constrained (L) (11% of households), and market constrained (M) (10% of households).
Bridging Yield Gaps Is Important, but Improving Market Access Is Essential.
Consumption of food crops produced on the farm forms the base level of energy supply in all smallholder farms, but most farm households start selling food crops before the households' consumption reaches food self-sufficiency. Farm households sell produce even when they do not produce enough food to be self-sufficient: 83% of the farm household sell part of their crop produce, and only 4% of the farmers do not sell anything of their crop or livestock produce. Thus, market access is crucial to ensure or improve the livelihoods of these families. Simply increasing production by closing the yield gaps of important food crops does not necessarily lead to improved food security because many of these food crops (e.g., maize) typically have low market prices (7). Closing yield gaps might allow families to buy more food at cheaper prices, thereby helping the food security of the poorest families. However, our results show that the majority of smallholder farmers rely on the sale of food crops to generate cash. Decreasing food prices might be attractive for the poorest families but would have adverse consequences for market-orientated smallholder families. A more logical entry point for the poorest families is therefore to stimulate the labor market for off-farm activities. Unless farmers are able to use improved production levels of food crops to reach the base level of food consumption on less land, thereby freeing up land to produce cash crops and to market these in return for good prices, our analyses indicate that increased food crop production is not necessarily an attractive entry point for improving the livelihood of these smallholder farmers. However, the success of this depends on market access.
Livestock Matters for the Poor and the Rich.
Livestock provided roughly 20% of the energy of the households. A clear shift in livestock species was observed: the poorest farmers rely on poultry, and those with better FA own cattle. This shift follows what has been described as the "livestock ladder" across different farms (8, 9). The livestock ladder depicts a system that poor smallholders can use to ascend from keeping small-stock to acquiring larger animals, so a dynamic change over time within the same smallholder farm. Our results show that the upper rungs of the ladder are associated with better FA. However, the ladder does not show whether limiting resources, for example, fodder availability, will limit the ability of farmers to climb the ladder (10).
Off-Farm Income: The Stabilizer in the Equation.
Off-farm income was strongly related to the degree of FA: its importance as a source of energy increased from 12% for the insufficient food-available households to 27% for the more than sufficient food-available households. Off-farm income is one of the key options for achieving food security (11, 12). In previous work, off-farm income has been shown to be a more important part of the livelihood of the poorest farmers (13), but our analyses, in which consumption of self-produced crop and livestock products is valued highly in calorie intake terms, show the opposite. A wide body of literature (e.g., refs. 14⇓–16) showed that off-farm activities are a key source of social and economic stratification in rural areas (i.e., the better-off households tend to have access to the "better paid" nonfarm incomes, whereas the poorer farmers can only work as seasonal laborers on other farms in the region). Separate analyses based on the AFRINT (Africa Intensification) surveys showed that half of the farm households had no access to nonfarm income (16), so nonfarm income is not a pathway to food security available to all.
Simple Models and Indicators Needed for Targeting and Upscaling Policy and Development.
We developed a simple response model that explained 33% of the calculated variation in the agriculture based FA indicator (so excluding off-farm income). Based on the number of livestock and the size of the household, a threshold value of land size could be defined, above which a smallholder farm is likely to be able to produce enough food and cash to feed the family. This is a powerful minimodel, because all three variables can be easily and rapidly collected for large numbers of households, in contrast to variables like productivity, consumption, and sales, which need detailed survey instruments and often display high variability and imprecision. Despite all of the approximations (hypotheses of the model, problem of merging datasets, simplicity of the analysis, limited number of predicting variables) and noise in the data, our model predicted correctly the FA status of 72% of the households (Fig. 5). The relationships in Fig. 5 were strongly affected by market access. When farmers have good market access, the size of the farm needed to produce and/or purchase enough food to feed the family secure can be small (Fig. 6). With good market access, farmers are able to generate cash through the production of high-value crops alongside a base supply of food crops, and buy the food they need. This observation confirms earlier findings (17) on farmers intensifying with cash crops as a result of higher relative factor prices in densely-populated areas with good market access and suggests that indeed Boserup's endogenous intensification of farming systems in response to mounting land constraints can be found in our data (18, 19).
Our results show that, when focusing on FA (a key indicator of food security), a substantial part of the smallholder farmer population in Africa will face large difficulties in reaching a sufficient level of FA given their small farm sizes. This is a critically important finding given that around 80% of the smallholder farms in SSA are now smaller than 2 ha (20, 21). However, we also show that many could potentially increase FA sufficiently to feed the family on relatively small parcels of land through intensification practices involving cash crops and the use of livestock (e.g., ref. 22). This scenario is only possible if market access is ensured and overproduction does not depress prices of the farmers' produce. Most of the farm households in our database do not have good market access, and therefore their farm size and livestock-holding thresholds are still very important, as shown in the average response threshold of Fig. 5. Current trends in farm size development in SSA are strongly negative in many countries (22, 23), making the future of smallholder farming bleak in many places unless market access can be ensured (24).
More Land Does Not Automatically Mean More Food Is Available Throughout SSA.
The saturated FA—land size curve of Fig. 5 is caused by a decline in productivity per unit land (expressed in kcal per ha) when land sizes increase in the overall dataset. More detailed analyses presented in Fig. S3 show that this decline in land productivity with increased land holdings per farm was visible across all datasets available from SSA but also occurred within regions with contrasting agroecological and socioeconomic conditions. Per region, the range of cropland holdings was different, but across these different ranges, land productivity systematically declined with an increase in cropland holding. This finding supports the inverse land size productivity relation that has been found in many studies for smallholder farmers (25, 26). Recent studies qualify this relationship (27), showing that medium size farms are most efficient per unit area. The results in Fig. 5 change when the environmental constraints are taken into account (Fig. 6), and these results indicate that the inverse land size productivity relation is less severe in land-constrained sites with market access. The relationship between our FA indicator and cropland size is almost flat in sites where there is no land constraint and where there is no market access (e.g., in semiarid regions with low population densities), illustrating that the only way to become food secure in those sites is through livestock holdings in the face of an extremely severe inverse land size productivity relation (Fig. 6; also see Figs. S3 and S4).
Agricultural-intensification constraints. The black points represent sites with the given constraint, and the white points are sites without that constraint. Country borders were based on Global Administrative Areas (2014) (University of California, Berkeley, CA). Open access data is available online at www.gadm.org (accessed June 15, 2015).
The crop yield per unit land area versus the total cropland area used per farm household. Results are shown for five regions; annual rainfall threshold used to separate (semi)arid from (sub)humid was 850 mm per year. The slopes of the regression lines [the drawn lines; all expressed in ln(kcal per ha) per ln(ha)] are −0.9 for East Africa (semi)arid, −0.62 for East Africa (sub)humid, −0.45 for Southern Africa, −0.78 for West Africa (semi)arid, and −0.19 for West Africa (sub)humid. Both axes are in log scale.
The response curves (A–C) using the artificial neural network model with cropland expressed in ha (A), herd size expressed in TLU (B), and family size expressed in MAE (C) as inputs, together with the environmental constraint variables (for an explanation, see Methods). The dashed lines show the average response for some contrasting constrained environments: land and market-constrained environment (LM) (18% of households), land-constrained (L) (11% of households), and market-constrained (M) (10% of households).
Targeting More Than Agricultural Development Is a Necessity.
The results of these analyses can help with the targeting of food policies because the results quantify the importance of different on and off-farm activities to FA, and the importance of market access on the potential of farming systems to intensify. The role of off-farm income and market access clearly shows that rural development in SSA has to be more than closing yield gaps and agricultural development per se. Connecting people to urban centers and generating other employment sources will directly affect food security in a manner that boosting production cannot (see also 12, 15). As discussed earlier, farmers start selling produce at levels below fulfilling food self-sufficiency, and increasing productivity of food crops will only lead to substantial improvement in food security if cash crops and intensified livestock production can take place, both needing good market access (17, 28, 29).
Our approach is based on farmer-reported data, with all of its constraints and limitations (30, 31). Except for the off-farm income estimates, which were biased by the survey of the project in which they were collected, the overall results were consistent across surveys. The analyses presented in this study show how big datasets can be used to identify generic patterns that can be used to prioritize policies, despite the huge diversity in smallholder farming systems in SSA.
Household Data.
We collated recent cross-sectional farm household characterization data from six different projects in countries of SSA. The projects were AFRINT (www.keg.lu.se/en/research/research-projects/current-research-projects/afrint), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) (https://ccafs.cgiar.org), Consortium for Improving Agriculture-based Livelihoods in Central Africa (CIALCA) (www.cialca.org), Conference des Responsables de Recherche Agronomique Africains–Australian Aid (CORAF-AUSAID) (www.coraf.org/csiroV2013), N2Africa (www.n2africa.org), and Sustainable Intensification of Maize and Legume Systems for Food Security in Eastern and Southern Africa (SIMLESA) (simlesa.cimmyt.org). These data sources were chosen for (i) their availability of recently collected information on crop production and livestock at household level; (ii) their random sampling of households within sites and a relatively high number of households per site; and (iii) their locations to cover different farming systems, agroecologies, and countries across SSA (Fig. 1 and Table S1). In total, 93 different survey sites were sampled in 17 countries across contrasting regions (Fig. 1), with between 35 and 400 randomly selected farm households per site (Table S1).
Description of the six datasets used in the analysis
Calculation of the Food Availability Indicator.
A simple food security indicator was developed building on earlier work by Hengsdijk et al. (32). FA was calculated from on-farm consumption of food crops, and food that could be purchased on the basis of money earned through on-farm and off-farm activities (Fig. 2). This indicator of food security does not cover all of the complexity contained in the concept of food (in)security (33, 34). Our indicator estimates the potential annual amount of energy available at household level, and we therefore refer to it as "food availability." The indicator provides a continuous "food-availability scale" that allows us to quantify the contribution of key determinants of FA for individual households within and across sites.
FA was expressed in PFE energy (kcal) per capita per day and is calculated according toPFE=Econs + Eincome365×nhh,[1]
where Econs is the direct consumption of PFE energy from on farm food produce in kcal (calculated from Eq. 3), Eincome is the indirect consumption of the PFE energy from income (farm sales, off-farm) in kcal (calculated from Eq. 4), and nhh is the household size in MAE (calculated from Eq. 2).
Household members were disaggregated by sex and age brackets following Food and Agricultural Organization of the United Nations methodology (35) to quantify household size nhh in MAE, based on energy requirements for members of each age bracket (Eq. 2).nhh=∑ini × αi,[2]
where ni is number of person in class i, and αi the percentage of energy requirement of class i [compare with the energy requirement of an adult male with average daily activity, 2,500 kcal/d (36)].
The PFE energy from direct consumption of on-farm produce was calculated asEcons= ∑cEc×mc×θc+∑lEl×ml×θl,[3]
where Ec is the energy content of the yield of crop c, mc is the yield of crop c in kg, and θc is the percentage of the yield of crop c consumed. For livestock, El is the energy content of livestock product l, ml is the produced amount of product l in kg, and θc is the percentage of livestock produce l consumed. Energy contents were based on a standard product list developed by the US Department of Agriculture (source: ndb.nal.usda.gov/ndb/search/list).
The PFE energy from indirect consumption of income was calculated asEincome=IUSD × EstaplePstaple.[4]
IUSD is the money earned by the household (by selling farm production and off-farm income) in US dollars (USD) (calculated from Eq. 5), Estaple is the PFE energy content of the staple crop (kcal/kg), and Pstaple is the price per kg of the staple crop (USD/kg). Only the staple crop was purchased with the money earned because in most surveys, information was lacking on the actual purchase of food items or was only available for a limited period of the year. By only assuming purchase of the staple crop, the actual energy supply of food is likely to be overestimated, and the FA indicator is therefore only an indicator of the potential to generate enough energy to feed the family by the different livelihood activities.
The money earned by the household was calculated asIUSD=∑cPc×mc×(1−θc)+∑lPl × ml×(1−θl)+Φ,[5]
where PC and Pl are the price of the crop yield c (or livestock product l) in USD⋅kg−1, and Φ is the off-farm income (in USD).
Data Availability and Quality.
We defined the following categories that contribute to FA: food crops consumed, food crops sold, cash crops sold, livestock and livestock products consumed or sold, and off-farm income. Cash crops were defined as crops of which more than 90% of the annual produce is sold. Prices were converted to USD⋅kg−1 using the currency-conversion rate of the first of January of the year of the survey. To control for the large variability in reported price values for the farm products, we took the median value of these prices per kilogram per site.
The FA analysis was applied to all 13,567 households in our database. We first quantified the energy contribution of different on and off-farm activities to FA for all households. We tested our FA indicator by comparing it to self-assessed food security status where available. In both the AFRINT and N2Africa surveys, information per household was available on the number of meals per day; in the CIALCA dataset, information on FA classes was available: each household classified itself in one of four food security classes. Statistically significant correlation coefficients between 0.18 and 0.37 were found between the reported class or level of food security (either expressed as number of meals per day or as a food security class scoring) and the (log-transformed) FA indicator (Fig. S1). Therefore, strong variations in the FA indicator were related to variations in the overall food security indicators used for comparison, and the FA indicator, despite its strong underlying assumptions, gave a reasonable insight in the overall food security status of individual farm households.
Preliminary analyses showed that the median value for the base level of food crop consumption (expressed as PFE energy) across all individual farm households was roughly 1,500 kcal per capita per day. The overall median value of FA was roughly 4,000 kcal per capita per day. These values were used as a proxy to define three FA classes: "insufficient food available" with less than 1,500 kcal per capita per day, "sufficient food available" with between 1,500 and 4,000 kcal per capita per day, and "more than sufficient food available" with more than 4,000 kcal per capita per day. These FA classes were used to explore the relative contribution of different on-farm and off-farm household activities in detail.
After this data-exploration step, we tried to explain variations in the FA indicator. In this step, we focused on the drivers of the agriculture-related contribution to FA, thereby excluding off-farm activities. This exclusion of off-farm activities was for two reasons: first, we wanted an explanatory model of FA and off-farm activities are less related to on farm resources and therefore less predictable; and second, initial analyses showed that values in off-farm income were systematically influenced by the way information about this resource was collected in the surveys (a decision tree analysis showed that the name of the survey was one of the key determinants of variations in off-farm income).
To quantify the relationships between farm level resources and FA, we used three key farm household level variables to describe variation in FA among farm households: the crop land used by the farm household (in ha), the livestock herd size (expressed in TLU), and the family size (in MAE). These variables were also identified in other studies as important variables of household-level food security (2, 15, 36, 37).
Site-level variables were used as discrete variables, because initial test results showed that a functional interpretation of the relationships found between FA values and continuous site variables was extremely difficult. We characterized the sites based on key constraints identified in literature or in the survey data themselves. Three constraints were used: land, livestock, and market (19, 38, 39). Whether a site was defined as land-constrained or not was based on the country classification of Headey and Jayne (39). Whether market access was a constraint was based on the importance of cash crops in a site in the survey data: sites with less than 25% of the farm households growing cash crops were labeled as "market-constrained." "Livestock-constrained" sites were sites where less than 50% of the households owned 0.7 TLU [i.e., at least one cow (Fig. S2)]. Variables describing the agroecological environment of the sites (e.g., rainfall, soil) were included in initial analyses, but either the variables did not result in balanced data divisions or the site-level GPS information was too coarse. Therefore, we did not include the biophysical site-level variables in this analysis.
Because the relationships between FA and the explanatory variables were expected to be nonlinear, with strong interactions present between the drivers, we used ANNs to quantify these relationships. ANNs produce the best possible empirical relationship between the input and output variables presented to the network. A standard three-layer, back-propagation network was used with, after testing different numbers, four hidden neurons. The networks were trained on 75% of the available data, whereas 25% was used for testing. The ANNs were cross-validated 500 times, and in each sampling, 50 initializations of the neural network architecture were used. At the end of the analysis, 500 different networks were available per combination of input variables, which were used to quantify the uncertainty in the network performance and the response curves that were generated with the networks. We checked the relationships found by the ANNs between FA values and changes in household size to make sure the ANN models were not simply identifying the inverse household-size relationship used in the FA calculations, resulting in circular reasoning.
We thank all the people involved in collecting the field data that formed the basis for the analyses and especially the farmers for sharing their valuable information. We thank the two anonymous referees and the editors of PNAS for their useful comments that helped improve this manuscript. This work is a joint output of the CGIAR Research Programs on Livestock and Fish, Humid Tropics, and CCAFS and the following bilateral donor projects: AFRINT [Sida and the Swedish Research Council (VR)], CIALCA [Belgian Directorate General for Development Cooperation (DGDC)], AFSI CORAF-AUSAID [Australian Department of Foreign Affairs and Trade (DFAT)], N2Africa (a Bill & Melinda Gates Foundation grant to Wageningen University), SIMLESA [Australian Centre for International Agricultural Research (ACIAR)], and LiveGAPS (a Bill & Melinda Gates Foundation grant to CSIRO).
↵1To whom correspondence should be addressed. Email: m.vanwijk{at}cgiar.org.
Author contributions: R.F., K.E.G., M.H., and M.T.v.W. designed research; R.F. and M.T.v.W. performed research; K.E.G., M.H., S.D., A.A.D., O.E., M.K., D.R., and P.J.A.v.A. supplied data; R.F., S.D., B.H., B.K.P., R.S.R., and M.T.v.W. analyzed data; and R.F., S.L.-R., K.E.G., M.H., S.D., A.A.D., O.E., B.H., M.K., B.K.P., C.R., R.S.R., D.R., P.J.A.v.A., and M.T.v.W. wrote the paper.
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You are going to email the following Drivers of household food availability in sub-Saharan Africa based on big data from small farms
Household food availability in sub-Saharan Africa
Romain Frelat, Santiago Lopez-Ridaura, Ken E. Giller, Mario Herrero, Sabine Douxchamps, Agnes Andersson Djurfeldt, Olaf Erenstein, Ben Henderson, Menale Kassie, Birthe K. Paul, Cyrille Rigolot, Randall S. Ritzema, Daniel Rodriguez, Piet J. A. van Asten, Mark T. van Wijk


MINNEAPOLIS, Jan. 15, 2013 (GLOBE NEWSWIRE) -- XRS Corporation (formerly Xata Corporation) (Nasdaq:XRSC), the leader in mobile fleet optimization software, announced today that early registration is now open for its second annual XRS User Event (XUE), to be held Aug. 18-20, 2013, at the Hyatt Regency in Minneapolis, Minn.
The 2013 event will build upon last year's momentum with renowned keynote speakers Don Mann, author of "Inside SEAL Team Six: My Life and Missions with America's Elite Warriors." Annette M. Sandberg, a longtime transportation consultant, former FMCSA administrator and member and leader of the Washington State Patrol, will also join the conference as the luncheon keynote speaker.
Mann will share his high-octane narrative of physical and mental toughness, giving unprecedented insight to the inner workings of the training and secret missions of the world's most respected and feared combat unit and drawing parallels to strategic business leadership challenges and opportunities.
Sandberg will share her outlook on safety and compliance issues impacting fleet managers, operations directors, IT staff and drivers, with an emphasis on how technology can improve safety and efficiency in all facets of the trucking industry.
"Following our 2012 event, the entire XRS team looked for ways to provide even more valuable content to our users in 2013," said Jay Coughlan, chairman and CEO of XRS. "This year, we're taking XUE to the next level, with a pair of headlining speakers that boast deep strategic insight, not just in the trucking industry but across the business and leadership spectrum. Just as 2012 represented our company's transformation into a mobile-focused enterprise, XUE 2013 will build upon our positive momentum as an organization and showcase our most deeply held strategic philosophies."
XUE is the premier event for XRS users and other decision-makers in the trucking industry who use technology to achieve fleet optimization, compliance and safety goals. The event will feature an extensive education program, presentations by industry experts and opportunities to meet, socialize and share success stories with other users. XRS team members will be on hand to answer questions, offer advice and interact with users to learn more about how XRS can better meet their needs.
During the inaugural XUE event in 2012, XRS made two major industry announcements: the company's name change from Xata Corporation to XRS Corporation, and the introduction of a revolutionary all-mobile platform for trucking compliance, performance and fleet optimization, also called XRS.
Those interested in attending XUE 2013 can visit www.xueconference.com to register online and receive a special early registration rate through Mar. 29, 2013.
XRS Corporation (formerly Xata Corporation) delivers fleet management and compliance software solutions to the trucking industry to help maintain regulatory compliance and reduce operating costs. XRS is leading the trucking industry's migration to mobile devices for collecting and analyzing compliance and management data. Its existing mobility-based products have no upfront hardware costs and run on smartphones, tablets and rugged handhelds. XRS has sales and distribution partnerships with the major wireless carriers supporting the U.S. and Canadian trucking industries. Through our mobile products, fleet managers, dispatchers and drivers collect, sort, view and analyze data to help lower costs, increase safety, attain compliance with governmental regulations, and improve customer satisfaction – all through their mobile devices. For more information, visit www.xrscorp.com or call 1-800-745-9282.


text:
Free Kindle eBooks | Nature & Ecology |
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Discover the many underwater mysteries our planet boast. From lost civilizations to unidentified submerged objects, you will be surprised by the multitude of strange and unexplained phenomena that originates under the waters across the World.There is nothing on this planet more mysterious than our oceans, lakes, streams, and rivers. These sources of water have also and always been sources of legend. Human civilization has always depended on water, and land-based societies are inextricably linked to their waterways. Egypt was built on the Nile, ancient Mesopotamia was surrounded by the Euphrates and Tigris, and port cities dotted the entire Mediterranean coast. You see, although we live on dry land, we draw our inspiration, our livelihood, and our most captivating experiences from the water...
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redpajama_set_name: RedPajamaCommonCrawl


text:
Q: How does Kendo grid convert the object reference list in the parent row's columns into new row below the parent row? I have a data grid below, how can I made kendo grid display like this?

[ ]
num
month
Start
end
week
num
start
end
hour
Lock

[x]
7
July
07/05
07/05

week 1
1
07/05
07/05
40
Yes

week 2
2
07/05
07/05
40
No

week 3
3
07/05
07/05
40
NO

week 4
4
07/05
07/05
40
Yes

[x]
8
August
08/07
08/05

week 1
1
08/05
08/05
40
Yes

week 2
2
08/05
08/05
40
No

week 3
3
08/05
08/05
40
Yes

week 4
4
08/05
08/05
40
Yes

[x]
9
September
09/05
09/05

week 1
1
09/05
09/05
40
Yes

week 2
2
09/05
09/05
40
No

week 3
3
09/05
09/05
40
Yes

week 4
4
09/05
09/05
40
No

I created 2 model with one-to-many ralationship
Month.cs
 public int Id { get; set; }
 public int num { get; set; }
 public string month{ get; set;}
 public DateTime Start { get; set; }
 public DateTime End { get; set; }
 public List<Week> Weeks{ get; set; }

Week.cs
 public int Id { get; set; }
 public int num{ get; set; }
 public string week { get; set;}
 public DateTime Start { get; set; }
 public DateTime End { get; set; }
 public int hours { get; set; }
 public Boolean Lock { get; set; }
 public int MonthId { get; set; }
 public Month Months { get; set; }

I can only make the kendo grid show one of the 2 models above like this
Month.cs

[ ]
num
month
Start
end

[x]
7
July
07/05
07/05

[x]
8
August
08/05
08/05

[x]
9
September
09/05
0/05

Week.cs

[ ]
week
num
start
end
hour
Lock

[x]
week 1
1
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If I use ViewModel with both properties from Month.cs and Week.cs then kendo grid like this

[ ]
num
month
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end
week
num
start
end
hour
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[x]
7
July
07/05
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Yes

[x]
8
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[x]
9
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40
Yes

redpajama_set_name: RedPajamaStackExchange


text:
Dorojneanka (în ) este o comună în raionul Huleaipole, regiunea Zaporijjea, Ucraina, formată numai din satul de reședință.

Demografie

Conform recensământului din 2001, majoritatea populației comunei Dorojneanka era vorbitoare de ucraineană (%), existând în minoritate și vorbitori de rusă (%).

Note

Dorojneanka, Huleaipole, Zaporijjea
Dorojneanka, Huleaipole, Zaporijjea
Dorojneanka, Huleaipole, Zaporijjea

redpajama_set_name: RedPajamaWikipedia


Since 2012, the GaN market has blossomed with new players. However, since the technology is still improving, no standard yet exists and we see many different coexisting solutions. Manufacturers propose different approaches for epitaxy, gate structure, device design, and packaging, all focused on solving the problems linked to GaN's intrinsic properties and its integration with silicon. To minimize the obstacles linked to high-frequency operations and offer a driverintegrated solution, Texas Instruments has introduced the first 80V half-bridge GaN FET power stage device in advanced QFM packaging.
In this report, System Plus Consulting reveals TI's technical choices, from device design through packaging. This is the first time we have found a half-bridge GaN FET design, with driver, all assembled in an advanced multichip package (PCB with embedded via and flip-chip dies).
TI's new LMG5200 features an outsourced (see report for details) GaN FET with a breakdown voltage of 80V for a current of 10A (25°C). The transistor is driven by a National Semiconductorssilicon IC gate driver with a 1 µm technology node.
The epitaxy structure is composed of different GaN and AlGaN layers and multiple AlGaN heterojunction structures between the GaN and the AlN layer. A complex buffer and a template layers' structure reduces stress and dislocation.
Based on a complete teardown analysis, this report also provides an estimated production cost for the IC gate driver, FET, and package. Moreover, this report proposes a comparison with the packaging and epitaxy from GaN Systems, Transphorm, and Panasonic. This comparison highlights the differences in design and manufacturing processes, and their impact on device size and production cost.


(KANSAS CITY, Mo.) Nov. 19, 2015 – Many entrepreneurs need external funding to launch or grow their startup, but the idea of finding investors can seem daunting. In Financing Your Venture: Angel Investment, a new video series from Kauffman Founders School, Ian Sobieski teaches entrepreneurs how to find and work with investors.
As a founding general partner and managing director of the Band of Angels Fund, L.P., Silicon Valley's oldest angel investment group, Sobieski has counseled hundreds of entrepreneurs and understands their funding dilemmas. In seven brief videos, he helps entrepreneurs understand different types of investors, walks entrepreneurs through the process of locating and securing investors, and shares his insights on maintaining productive relationships over time.
Sobieski's videos complement another Kauffman Founders School video series, Financing Your Venture: Venture Capital, presented by Jeff Bussgang, a venture capitalist with Flybridge Capital Partners.
Kauffman Founders School, a free online education program available at www.KauffmanFoundersSchool.org, brings together some of America's startup experts to educate entrepreneurs in key areas of business formation and success. Other Founders School topics include The Lean Approach, Startups, Entrepreneurial Selling, Powerful Presentations and Entrepreneurial Marketing.
The Kauffman Foundation offers additional resources, educational information and tools for entrepreneurs at Entrepreneurship.org.


text:
SAN FRANCISCO — The Chinese government this week summoned major tech companies from the United States and elsewhere to warn that they could face dire consequences if they cooperate with the Trump administration's ban on sales of key American technology to Chinese companies, according to people familiar with the meetings.
Held on Tuesday and Wednesday, the meetings came soon after Beijing's announcement that it was assembling a list of "unreliable" companies and individuals. That list was widely seen as a way of hitting back at the Trump administration for its decision to cut off Huawei, the Chinese electronics giant, from sales of American technology. The United States has accused Huawei of stealing trade secrets and conducting surveillance on behalf of Beijing.
Details about the meetings, the latest move in two weeks of high-stakes economic brinkmanship between the United States and China, were shared by two people familiar with them, who asked not to be named because they were not authorized to discuss them and could face retribution. The companies summoned by Chinese officials included a number of the world's most important semiconductor firms, as well as other tech giants.
The breakneck unraveling of the world's most important trade relationship has left companies and governments around the world scrambling. While the dispute had already been nettlesome for Chinese-U.S. relations, the sudden ban on Huawei last month caught many by surprise, raising the stakes by striking at the heart of China's long-term technological ambitions.
Now, each of the two superpowers appears to be crafting new economic weapons to aim at the other. What was once a fraught, but deeply enmeshed, trade relationship is threatening to break apart almost entirely, raising the specter of a new geopolitical reality in which the world's two superpowers would compete for economic influence and try to freeze each other out of key technologies and resources.
"This is now extremely delicate because the Trump administration, through its brinkmanship tactics, has destabilized the entire relationship, commercial and otherwise," said Scott Kennedy, a senior adviser at the Washington-based Center for Strategic and International Studies who studies Chinese economic policy.
The meetings this week were led by China's central economic planning agency, the National Development and Reform Commission, and attended by representatives from its Ministry of Commerce and Ministry of Industry and Information Technology, who addressed their remarks to a broad range of companies that export goods to China, according to the two people familiar with the gatherings.
The involvement of three government bodies suggested a high level of coordination and likely approval from the very top of China's opaque leadership structure. The intervention seemed designed to rally support for Huawei, though the company was not specifically mentioned, the two people said.
"There is a strong perception in Beijing that the U.S. government is intent on blunting China's technology rise, and that if this process is not slowed or stopped, the future of China's entire digital economy is at risk," said Paul Triolo, the head of geotechnology at the consultancy Eurasia Group, adding that the spat had major political implications for Xi Jinping, China's president and the head of its ruling Communist Party.
"Mr. Xi and the party will be seen as unable to defend China's economic future" if the confrontation with the United States does major damage to Huawei and throws off China's rollout of the next generation of wireless technology, called 5G, Mr. Triolo added.
More broadly, the warnings also seemed to be an attempt to forestall a fast breakup of the sophisticated supply chains that connect China's economy to the rest of the world. Production of a vast array of electronic components and chemicals, along with the assembly of electronic products, makes the country a cornerstone of the operations of many of the world's largest multinational companies.
As the trade relationship between the United States and China has broken down, fears have risen in China that major companies will seek to move production elsewhere to avoid longer-term risks. In the meetings this week, Chinese officials explicitly warned companies that any move to pull production from China that seemed to go beyond standard diversification for security purposes could lead to punishment, according to the two people.
The Chinese officials appeared to have differing messages for the companies, depending on whether they were American or not, the people added.
To those from the United States, they warned that the Trump administration's move to cut off Chinese companies from American technology had disrupted the global supply chain, adding that companies that followed the policy could face permanent consequences. The Chinese authorities also hinted that firms should use lobbying to push back against the government's moves.
"The Chinese government has regularly resorted to jawboning multinationals to try to keep them in line when there are disputes between China and others that could lead these companies to reduce their business in China," Mr. Kennedy said.
In the past, China has used America's tech behemoths as a tool of diplomacy. For example, during a high-profile visit to the United States in 2015, Mr. Xi stopped in Seattle before heading to Washington. There, he met with a who's who of American and Chinese tech executives as a way to emphasize the depth of the countries' economic ties, even as President Barack Obama's administration sought to chart a course that would push back against China's anticompetitive trade practices and investment rules.
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This time, such a play is less likely to be effective, said Mr. Kennedy, because it forces the companies to choose between complying with pressure from Beijing and violating U.S. law.
"American companies aren't going to violate American laws, especially in such a high-profile context where their actions are scrutinized," he said. "The companies are between a rock and a hard place, but that hard place will win out."
The Chinese officials told companies from outside the United States that as long as they kept up their current relationships and continued to supply Chinese companies normally, they would face no adverse consequences. They also stressed China's commitment to open trade and its protections of intellectual property, according to the people familiar with the meetings.
The Trump administration's ban on sales to Huawei was a blow to semiconductor companies that supply parts used in Huawei's telecom gear. Several American companies slashed tens of millions of dollars from their quarterly revenue expectations. About 60 percent of all semiconductors sold are connected in some way to China's supply chain, the consulting firm KPMG estimates.
Resolving the mercantilist standoff may be difficult, said Mr. Triolo, because progress in the trade talks has become linked to progress on the Huawei tech ban, but the Huawei issue cannot be dealt with through the official trade channels. For example, the dispute has been inflamed by fraud charges the United States has brought against the Huawei executive Meng Wanzhou, who is awaiting extradition proceedings in Canada, and obstruction and other charges against the company itself.
A separate negotiation between high-level officials over Huawei is probably the only way forward, Mr. Triolo said — that, or an even less likely possibility: "Huawei would have to send a team to Washington to admit guilt and negotiate what could be a humiliating agreement with U.S. authorities."
"So far, there is no sign that either Beijing or Huawei is considering that option," he said.


If you test the site online, you will get result saying that Video2mp3 is safe to use on your computer. I moved my Nero 12 to Windows 8 and it seemed to work there. These videos are used on Twitter, and in many cases on YouTube especially for streaming videos. This opens the YouTube home page. I tried your suggestion but still doesn't work.
Unlike other services the whole process is performed on their platform. This opens the YouTube home page. That simply depends on your Internet connection and the size of the file. It's in the top-right corner of the Chrome window. Doing so opens a file selection window.
What is the Best youtube downloader in your opinion? There are various YouTube video downloaders and converters that easily allow you to do so. Pros: 10 characters minimum Count: 0 of 1,000 characters 4. The fact that it is free is yet another feature that has made it a popular choice among the masses. Doing so uploads the folder to Google Chrome, thus installing the extension. This feature will become available in Opera's stable version after testing and when we're sure it works as expected.
This extension will allow you to choose any format that you may want to convert your video format to. Whenever a webpage presents a video or audio that can be downloaded, the blue light will become a green icon. It allows to enable or disable YouTube cinema mode and dark theme for the viewers. It even autosorts them into folders based on uploader. No problem, the next update should fix the playlist problem, I'd like to make it so that the user is prompted with all the items in the playlist and then can choose which to download. While as for video capture, you can try other kinds of downloaders to meet your needs. Which may not be ideal for you.
. You can keep on streaming videos while the extension does its job. If you have a degree in computer science, maybe this is the ideal choice for you. It is displayed right under the video you are playing at any time. I checked everyone of the products that came with Nero 2104 Platinum, but I can't find anything close to capturing Youtube video.
If you would like to capture audio tracks from YouTube, I high recommend you to use audio recording app on mac, which can record any sound from YouTube audio and video files. Find the quality you want to use. Click on the download button. This problem is keeping me from going to Opera full-time. Here you can choose between standard quality and high definition. One thing we noticed about this site is the huge selection of different languages on offer.
So i would need to download to mp3 i dont care about the video. If it continuously fails, it might be caused by copyright restriction. To download the video, select the green icon when it appears. Let me know of this helps! Method 2: Use Online Helper Site One of the best online helper sites for YouTube download with Internet Explorer is. Click or double-click the Chrome app icon, which resembles a red, yellow, green, and blue sphere. Also, download a video, rip the audio out if it, then delete or save the video afterwards.
Paste in the copied link. Once these steps have been performed, be sure to select the format in which you would prefer your video to be saved. For Windows users or if the Download button doesn't show on your browser page, follow this guide on install. With the following three methods, you can most easily download right from Internet Explorer without needing to rely on smart phones or app stores that ask for tons of storage and mobile devices. This article was co-authored by our trained team of editors and researchers who validated it for accuracy and comprehensiveness. Now with YouTube Direct, you can add more interactivity to your site by soliciting video submission from your site visitors.


We know how hard it is to find the perfect venue, that's why we have created this referral form. Simply fill out some basic information about your event, and select the venues you would like to get information from. We'll send your form out to those venues, and all you have to do is wait to hear back on their availability and pricing information.


text:
Ohio StateBuckeyesOSU
OSU 41 35 76
PSU 30 34 64
Penn StateNittany LionsPSU
Ohio State opens Big Ten play with 76-64 win over Penn State
Liddell closes out Ohio State's win with late dunk, steal (0:18)
Ohio State pulls away from Penn State in the final minute thanks to E.J. Liddell. (0:18)
UNIVERSITY PARK, Pa. -- — Kyle Young scored 16 points, E.J. Liddell added 14 points and Ohio State beat Penn State 76-64 on Sunday night in a Big Ten opener for both teams.
Ohio State made seven 3-pointers in the opening 10 minutes of the game. Six different Buckeyes made a 3 during the spree for a 27-19 lead, and Ohio State led 41-30 at halftime after going 7 of 16 from distance.
Ohio State led by double figures for the opening 15 minutes of the second half. Penn State got as close as six points until Justin Ahrens sank a 3-pointer from the corner for a 70-61 lead at 2:15.
Malachi Branham added 11 points for Ohio State (6-2). Young made all four of his 3-pointers and Ahrens went 3 of 8 from distance. Liddell picked up his fourth foul with 5:53 remaining and went 4 of 9 from the field.
Ohio State was coming off the program's 10th win over a No. 1 ranked team with a 71-66 upset of Duke in the Big Ten/ACC Challenge. Ohio State hosts Towson and No. 23 Wisconsin next week.
Jalen Pickett led Penn State (4-4) with 23 points and Seth Lundy added 13. John Harrar, who leads the Big Ten and is in the Top 15 nationally at 10.9 rebounds per game, grabbed 11 of his 12 rebounds in the second half. Harrar also scored 12 points.
The Nittany Lions play Wagner and No. 22 Michigan State next week.
For more AP college basketball coverage: https://apnews.com/hub/college-basketball and http://twitter.com/AP--Top25
Arbitrator rules UConn improperly fired Huskies men's basketball coach Kevin Ollie
UConn improperly fired former coach Kevin Ollie and must pay him more than $11 million, an independent arbitrator ruled Thursday. Ollie was fired in 2018 after the school reported numerous NCAA violations.
— Caleb Mills scored 21 points, freshman Matthew Cleveland added a season-high 21 points with 10 rebounds and Florida State beat North Florida 86-73 on Thursday for its fifth straight victory.
2021-22 Big Ten Conference Standings
Wisconsin 6-1 - 15-2
Illinois 6-1 - 13-4
Michigan State 5-1 0.5 14-3
Ohio State 5-2 1 12-4
Rutgers 5-2 1 11-6
Purdue 4-2 1.5 15-2
Indiana 4-3 2 13-4
Iowa 3-4 3 13-5
Penn State 3-4 3 8-7
Michigan 2-3 3 8-7
Northwestern 2-5 4 9-7
Minnesota 1-5 4.5 10-5
Maryland 1-6 5 9-9
Nebraska 0-8 6.5 6-13


text:
How 4 Covid Experts Will Keep Their Families Safe on Thanksgiving
By The New York Times Opinion Nov. 23, 2020
Staying Safe This Thanksgiving
As the coronavirus pandemic intensifies, is there any safe way to celebrate Thanksgiving? We asked four experts to tell us about their plans and the science that shaped them.
Linsey Marr
Dr. Marr is a professor of civil and environmental engineering at Virginia Tech who studies how viruses and bacteria spread through the air and is one of the 239 scientists who signed an open letter in early July pressing W.H.O. to consider the risk of airborne transmission more seriously. Read more
Marc Lipsitch
Dr. Lipsitch is a professor in the Departments of Epidemiology and Immunology and Infectious Diseases at Harvard T.H. Chan School of Public Health, where he also directs the Center for Communicable Disease Dynamics. Read More
Natalie Dean
Dr. Dean is an assistant professor of biostatistics at the University of Florida who specializes in infectious disease epidemiology. Her research is on methods for clinical trial and study design for evaluating vaccine efficacy, with a focus on emerging infectious diseases. Read More
Dr. Osterholm is director of the Center for Infectious Disease Research and Policy at the University of Minnesota and a member of President-Elect Joe Biden's coronavirus task force. Read More
Photo Illustration by Andrew B Myers
What are your Thanksgiving plans?
In a normal year, my spouse, my two children and I would have made travel plans months in advance to see extended family members. This year, we're staying home to avoid the risks of travel and of transmitting COVID-19 to vulnerable family members. However, we will still roast a turkey, make cornbread stuffing and bake a pumpkin pie — and then eat too much of everything. But it may not be on Thanksgiving Day. Instead, we'll aim for the warmest day that week and invite Grandma, who lives nearby, to share the meal outdoors under our newly purchased patio heaters.
What science or data about the pandemic shaped your decision about how to gather?
Gathering outdoors takes advantage of the best ventilation out there — freely moving air in the great big sky — so that aerosols that come out of our mouths while we're talking don't have the chance to build up in the air, in case one of us happens to be infected. Family gatherings can easily become superspreading events, and the last thing we want to do is to give the disease to someone in a vulnerable category. There is overwhelming evidence that breathing in the coronavirus from the air is a major route of transmission of Covid-19. Thus, we need to avoid sharing each other's air as much as possible. The easiest way to do this is to move activities outdoors, where the likelihood of transmission is much lower.The quintessential Thanksgiving experience — gathering with friends and family, eating and talking boisterously around a table in a cozy room for hours — is the perfect recipe for an outbreak. It goes against almost all science-based guidelines to reduce the risk of transmission: Maintain distance, wear a mask, avoid crowds, ensure good ventilation, talk quietly and minimize the duration of events.
What's your advice to Americans who are feeling unsure about how to celebrate?
Keep your gatherings small to reduce the chance that an infected person will be present and reduce the opportunity for the virus to spread to a large number of people. Gather outdoors (like the pilgrims!) instead of indoors as much as possible. Maintain your distance and wear a mask when you're not eating. Open the windows (yes, even with the heat running), and consider adding a portable air cleaner. Take a break every 30 minutes and go for a walk to burn off the meal, so that the room has a chance to air out and allow virus that might have accumulated in the air to be removed.
Follow Dr. Marr on Twitter @linseymarr
In contrast to the gathering of 16 or so relatives and friends from around the country that we have hosted in the past, we are having Thanksgiving as a foursome (nuclear family) this year. We are staying in touch by phone/videochat and the like and thinking about possible smaller visits at less crowded times, but still uncertain.
The point of our Thanksgiving gathering has normally been to get everyone together with grandparents, and this does not seem advisable this year, given the exposure at the gathering but also in the travel that would be required. While I am not an expert on the risks on airplanes (especially with current precautions), airports at holidays are crowded and poorly ventilated (or at least they seem to be when one is in them). It seems like a lot of risk to take at a time when we are all trying to be very cautious about avoiding exposure. With current trends, prevalence may be at least double at Thanksgiving what it was in late October, so the risk of exposure is growing.
As case numbers grow almost everywhere in the country, large indoor gatherings of extended families are transmission risks. While one can imagine various approaches to testing that might reduce the risk, we have seen from the White House that a testing-only strategy is not effective in controlling spread, and most of us can't afford that many tests in any case, nor would testing rule out infection during travel that could become contagious during the stay. For these reasons, I think this is a good year to stay home and with nuclear families. I suggested to a neighbor with a big, local family that they get together in the back yard with a few of them at a time, maybe roast a chicken and make stuffing, and have an early (before it gets so cold it's impossible) and scaled-down thanksgiving — or more than one in smaller, outdoor groups. That is more feasible in Florida or California than Minnesota, of course. We really need to maintain human contact with people we like and love. Putting some of the considerable resources people put toward travel at the holidays toward helping seniors set up video chat if they don't already have it, making time for more interactions by phone or video or outdoors (maybe buying a warm jacket to take walks with friends and family), and the like all makes sense. And we should remember that millions of people likely can't afford travel now and in some cases even a good Thanksgiving dinner because they are out of work, so it's important for those who have jobs and resources to support (socially and financially) those who haven't been as fortunate.
Follow Dr. Lipsitch on Twitter @mlipsitch
This Thanksgiving, my husband, kids and I will make the five-and-a-half hour drive to Georgia to my mother-in-law's house. She has driven to see us a few times during the pandemic, to help with our three-year-old son and soon to be one-year-old daughter. We decided to drive up because we want to see my husband's 85-year-old grandma, who last saw the babies in January. On at least one sunny afternoon while in Georgia, we will drive to great-grandma's house for a socially distanced outdoor playdate. Great-grandma will sit on the porch in her rocking chair while the kids play in the yard (my daughter just learned how to walk!).
Last week we held a Zoom call with my husband's aunts and uncles to discuss our Thanksgiving dinner plans. We considered an outdoor event with everyone, but opted against it. Case numbers are rising too quickly, and we didn't want to bring our separate bubbles together. Instead, we will have a Zoom gathering to make toasts and play the game Codenames. Unfortunately, we won't see my family in Massachusetts at all this holiday season. We haven't seen them since February, and we really miss them.
So many areas are Covid-19 hotspots, and statistically that means many more people who are actively (and unknowingly) infected. For many reasons – accelerating case counts, flying with young kids and not wanting to place anyone at risk – we will not make the trip.
My advice to readers this Thanksgiving is to really limit the number of separate bubbles you bring together. But exactly because 2020 has been so challenging, we all need something to look forward to. With a little creativity, like a recipe exchange or custom family trivia, Thanksgiving can still be special and fun. If nothing else, it will be memorable!
Follow Dr. Dean on Twitter @nataliexdean
I plan to spend the day with my partner. Just the two of us celebrating all we have to be thankful for in our lives. We will do virtual celebrations with our kids and grandkids. Maybe even find a new magic trick to do virtually for them. And I will spend part of the day doing something I've never done before on Thanksgiving. I will call a number of people in my life to whom I owe so much. I will thank them for their love and kindness and remind them that I'm so very glad they were born.
To gather in person because of tradition or peer pressure ignores the real potential for someone — a person who is infected and yet, has no symptoms — in your special circle of family or friends to bring the virus to the table. It will be virtually impossible not to swap air with that infected person. Loved ones will become infected and as I can personally attest, some will get seriously ill and even die. Unless everyone at the Thanksgiving table has been isolated from all other people for the 14 days before Thanksgiving dinner, they pose a real and present risk of being infected.
My best personal and professional advice is: Do not risk it. This best way to show love for our family and friends is to not be together around the table laughing and enjoying a wonderful meal. Find other ways to celebrate with family and friends. Do a virtual celebration. Say grace together from multiple locations in the world. Spend time taking a wonderful meal to family members or others and leave it off at their front door. Then have a wonderful conversation from 20 feet away; you outside and they in their doorway.
Follow Dr. Osterholm on Twitter @mtosterholm