**The Effect of Agricultural Growing Season Change on Market Prices in Africa**

K. M. de Beurs and M. E. Brown

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56459

## **1. Introduction**

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188 Climate Variability - Regional and Thematic Patterns

Local agricultural production is a key element of food security in many agricultural countries in Africa. Climate change and variability is likely to adversely affect these countries, particu‐ larly as they affect the ability of smallholder farmers to raise enough food to feed themselves. Seasonality influences farmers' decisions about when to sow and harvest, and ultimately the success or failure of their crops.

At a 2009 conference in the United Kingdom hosted by the Institute of Development Studies, Jennings and Magrath (2009) described farmer reports from East Asia, South Asia, Southern Africa, East Africa and Latin America. Farmers indicate significant changes in the timing of rainy seasons and the pattern of rains within seasons, including:

These farmer perceptions of change are striking in that they are geographically widespread and are remarkably consistent across diverse regions (Jennings and Magrath, 2009). The impact of these changes on farmers with small plots and few resources is large. Farming is becoming riskier because of heat stress, lack of water, pests and diseases that interact with ongoing pressures on natural resources. Lack of predictability in the start and length of the growing season affects the ability of farmers to invest in appropriate fertilizer levels or improved, high

yielding varieties. These changes occur at the same time as the demand for food is rising and is projected to continue to rise for the next fifty years (IAASTD, 2008).

phenology is the analysis of changes in the vegetated land surface as observed by satellite images (de Beurs and Henebry, 2004). Land surface phenology distinguishes itself from species centric phenology in that it focuses on the analysis of the land surface in mixed pixels. White et al. (2009) described the complexity of comparing ground observations of the start of season with satellite-derived estimates due to the difficulty in understanding the myriad definitions

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Land surface phenology models rely on remote sensing information of vegetation, such as the dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) (Tucker et al., 2005) and the newer MODIS sensors on Aqua and Terra. Vegetation and rainfall data can assess variables such as the start of season, growing season length and overall growing season productivity (Brown and De Beurs, 2008, Brown, 2008). These metrics are common inputs to crop models that estimate the impact of weather on yield (Verdin and Klaver, 2002). Land surface phenology metrics have a strong relationship with regional food production, particu‐ larly those with sufficiently long records to capture local variability (Funk and Budde, 2009,

The integration of a market into the broader economy has been the objective of many devel‐ opment programs (Barrett, 2008), since the increased integration of food markets in developing countries is considered to be of vital importance for agricultural transformation and economic growth (Fafchamps, 1992). Market integration is also an important aspect of food security, since many sub-Saharan countries face food shortages as a result of crop failures caused by drought or other climatic hazards (Zant, 2013). Integrated markets offer the potential to reduce the impact of weather shocks by quickly moving food from surplus to deficit areas. Conversely, poorly integrated markets, such as those where inadequate trade infrastructure hinders market function, may result in food shortages (Zant, 2013). Poorly integrated markets are often isolated because of low participation in the market by farmers, resulting in 'thin' markets that have too little supply during times of high demand (before the harvest) and too much supply during times with low demand (after the harvest). Many households in developing countries seek to be as self-sufficient as possible in capital, labor and food to reduce exposure to variability in prices and extremely high transaction costs (Lutz et al., 1995), which are both a cause and a consequence of thinly traded, volatile markets. Thinly traded markets keep the difference between producer and consumer prices high, further reinforcing household incentives to

minimize their reliance on markets (Tschirley and Weber, 1994, Kelly et al., 1996).

Seasonality in food prices, as measured by the ratio of post harvest to harvest prices, is high in markets that are poorly integrated and isolated. Seasonal price changes may reflect changes in production, particularly in good years when infrastructure and trade constraints reduce the ability of traders to move excess grain out of an area. Seasonal price spreads can be explained by storage losses, large postharvest grain sales, and lack of trader participation in isolated markets during average and good years (Alderman and Shively, 1996). Thus price seasonality is negatively related to production anomalies, where higher (lower) production will create

of season metrics.

Vrieling et al., 2008).

**2.2. Price seasonality**

Long-term data records derived from satellite remote sensing can be used to verify these reports, providing necessary analysis and documentation required to plan effective adaptation strategies. Remote sensing data can also provide some understanding of the spatial extent of these changes and whether they are likely to continue.

Given the agricultural nature of most economies on the African continent, agricultural production continues to be a critical determinant of both food security and economic growth (Funk and Brown, 2009). Crop phenological parameters, such as the start and end of the growing season, the total length of the growing season, and the rate of greening and senescence are important for planning crop management, crop diversification, and intensification.

The World Food Summit of 1996 defined food security as: "when all people at all times have access to sufficient, safe, nutritious food to maintain a healthy and active life". Food security roughly depends on three factors: 1) availability of food; 2) access to food and 3) appropriate use of food, as well as adequate water and sanitation. The first factor is dependent on growing conditions and weather and climate. In a previous paper we have investigated this factor by evaluating the effect of large scale climate oscillation on land surface phenology (Brown et al., 2010). We found that all areas in Africa are significantly affected by at least one type of large scale climate oscillations and concluded that these somewhat predictable oscillations could perhaps be used to forecast agricultural production. In addition, we have evaluated changes in agricultural land surface phenology over time (Brown et al., 2012). We found that land surface phenology models, which link large-scale vegetation indices with accumulated humidity, could successfully predict agricultural productivity in several countries around the world.

In this chapter we are interested in the effect of variability in peak timing of the growing season, or phenology, on the second factor of food security, food access. In this chapter we want to determine if there is a link between market prices and land surface phenology and to determine which markets are vulnerable to land surface phenology changes and variability and which market prices are not correlated.

## **2. Background**

#### **2.1. Vegetation seasonality and change**

Early research on the impact of global climate change on the growing season in northern latitudes was based on satellite remote sensing observations of vegetation (Myneni et al., 1997, Nemani et al., 2003, Slayback et al., 2003). These direct observations of change in the onset of spring led to the development of phenological models using remote sensing information. Phenology is the study of the timing of recurring biological cycles and their connection to climate (Lieth, 1974). Phenology has the promise of capturing quantitatively the changes reported by farmers and providing evidence for its link to climate change. Land surface phenology is the analysis of changes in the vegetated land surface as observed by satellite images (de Beurs and Henebry, 2004). Land surface phenology distinguishes itself from species centric phenology in that it focuses on the analysis of the land surface in mixed pixels. White et al. (2009) described the complexity of comparing ground observations of the start of season with satellite-derived estimates due to the difficulty in understanding the myriad definitions of season metrics.

Land surface phenology models rely on remote sensing information of vegetation, such as the dataset derived from the Advanced Very High Resolution Radiometer (AVHRR) (Tucker et al., 2005) and the newer MODIS sensors on Aqua and Terra. Vegetation and rainfall data can assess variables such as the start of season, growing season length and overall growing season productivity (Brown and De Beurs, 2008, Brown, 2008). These metrics are common inputs to crop models that estimate the impact of weather on yield (Verdin and Klaver, 2002). Land surface phenology metrics have a strong relationship with regional food production, particu‐ larly those with sufficiently long records to capture local variability (Funk and Budde, 2009, Vrieling et al., 2008).

#### **2.2. Price seasonality**

yielding varieties. These changes occur at the same time as the demand for food is rising and

Long-term data records derived from satellite remote sensing can be used to verify these reports, providing necessary analysis and documentation required to plan effective adaptation strategies. Remote sensing data can also provide some understanding of the spatial extent of

Given the agricultural nature of most economies on the African continent, agricultural production continues to be a critical determinant of both food security and economic growth (Funk and Brown, 2009). Crop phenological parameters, such as the start and end of the growing season, the total length of the growing season, and the rate of greening and senescence are important for planning crop management, crop diversification, and intensification.

The World Food Summit of 1996 defined food security as: "when all people at all times have access to sufficient, safe, nutritious food to maintain a healthy and active life". Food security roughly depends on three factors: 1) availability of food; 2) access to food and 3) appropriate use of food, as well as adequate water and sanitation. The first factor is dependent on growing conditions and weather and climate. In a previous paper we have investigated this factor by evaluating the effect of large scale climate oscillation on land surface phenology (Brown et al., 2010). We found that all areas in Africa are significantly affected by at least one type of large scale climate oscillations and concluded that these somewhat predictable oscillations could perhaps be used to forecast agricultural production. In addition, we have evaluated changes in agricultural land surface phenology over time (Brown et al., 2012). We found that land surface phenology models, which link large-scale vegetation indices with accumulated humidity, could successfully predict agricultural productivity in several countries around the

In this chapter we are interested in the effect of variability in peak timing of the growing season, or phenology, on the second factor of food security, food access. In this chapter we want to determine if there is a link between market prices and land surface phenology and to determine which markets are vulnerable to land surface phenology changes and variability and which

Early research on the impact of global climate change on the growing season in northern latitudes was based on satellite remote sensing observations of vegetation (Myneni et al., 1997, Nemani et al., 2003, Slayback et al., 2003). These direct observations of change in the onset of spring led to the development of phenological models using remote sensing information. Phenology is the study of the timing of recurring biological cycles and their connection to climate (Lieth, 1974). Phenology has the promise of capturing quantitatively the changes reported by farmers and providing evidence for its link to climate change. Land surface

is projected to continue to rise for the next fifty years (IAASTD, 2008).

these changes and whether they are likely to continue.

190 Climate Variability - Regional and Thematic Patterns

world.

market prices are not correlated.

**2.1. Vegetation seasonality and change**

**2. Background**

The integration of a market into the broader economy has been the objective of many devel‐ opment programs (Barrett, 2008), since the increased integration of food markets in developing countries is considered to be of vital importance for agricultural transformation and economic growth (Fafchamps, 1992). Market integration is also an important aspect of food security, since many sub-Saharan countries face food shortages as a result of crop failures caused by drought or other climatic hazards (Zant, 2013). Integrated markets offer the potential to reduce the impact of weather shocks by quickly moving food from surplus to deficit areas. Conversely, poorly integrated markets, such as those where inadequate trade infrastructure hinders market function, may result in food shortages (Zant, 2013). Poorly integrated markets are often isolated because of low participation in the market by farmers, resulting in 'thin' markets that have too little supply during times of high demand (before the harvest) and too much supply during times with low demand (after the harvest). Many households in developing countries seek to be as self-sufficient as possible in capital, labor and food to reduce exposure to variability in prices and extremely high transaction costs (Lutz et al., 1995), which are both a cause and a consequence of thinly traded, volatile markets. Thinly traded markets keep the difference between producer and consumer prices high, further reinforcing household incentives to minimize their reliance on markets (Tschirley and Weber, 1994, Kelly et al., 1996).

Seasonality in food prices, as measured by the ratio of post harvest to harvest prices, is high in markets that are poorly integrated and isolated. Seasonal price changes may reflect changes in production, particularly in good years when infrastructure and trade constraints reduce the ability of traders to move excess grain out of an area. Seasonal price spreads can be explained by storage losses, large postharvest grain sales, and lack of trader participation in isolated markets during average and good years (Alderman and Shively, 1996). Thus price seasonality is negatively related to production anomalies, where higher (lower) production will create lower (higher) prices during the post harvest season because of the inability or unwillingness of households and traders to store grain.

available from the FAO at the Global Information and Early Warning System (GIEWS) website: http://www.fao.org/giews/pricetool/. We selected all the available markets that are located on the African continent. We have a total of 933 time series with market price information for 51 different products ranging from fuel to cattle to grain crops. The most common commodities were maize (137 price series), sorghum (118 price series), rice (102 price series), millet (56 price series), beans (43 price series) and cowpea (40 price series).The monthly time series differ in length with some starting as early as 1997 while others started in 2009. For time series with long data, only the period from 2000 was analyzed, and for those beginning after 2000, we report only the stations with at least four years of continuous data. We created a 0.5° buffer

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around the markets and determined the percentage of cropland within these buffers.

To extract the peak height in NDVI and the peak timing based on accumulated evapotranspi‐ ration, we fit quadratic models with accumulated evapotranspiration based on MOD16 as the independent variable and NDVI as the dependent variable (Figure 1). We have used these models before based on Accumulated Growing Degree Days (AGDD) in other parts of the world (de Beurs and Henebry, 2008a,b, 2010). We have demonstrated recently that models

including Africa (Brown et al., 2012). In that paper we based our analysis on AVHRR data and we extend that work here with MODIS data. We fit the model two times for each pixel and year, once with data beginning in January and ending in December, and once with data beginning in July and ending in June. Figure 1 provides an example of the quadratic models for a market in Niger. For each 0.05° we calculate the NDVI peak height and the amount of accumulated evapotranspiration necessary to reach this peak NDVI. We also derive the day

Market prices are provided in monthly time series. Most market price series show a steady increase between 2000 and 2011 as a result of inflation and changes in world mar‐ ket prices. In this study we are not interested in the trend in these time series but rather in its seasonality. We calculate in which month, on average, the maximum price occurred, and we calculate the seasonal price spread. In addition, we calculate the difference be‐ tween the maximum market price in each year and the minimum market price in the pre‐

We apply Spearman's rank correlation to calculate the correlation between these price differences and the peak height based on our land surface phenology metrics (Figure 3).

values in large areas around the world

**4. Methods**

**4.2. Market analysis**

ceding eight months (Figure 2).

**4.1. Land surface phenology metrics**

based on moisture variables resulted in higher R2

of the year for which the peak NDVI is reached.

Another source of seasonality in food prices in thin markets is the seasonality of transaction costs, as well as transportation costs. Little is known about the variability of transportation costs in each of the markets of this study, but rainfall and poor roads, increased demand for movement of goods and people during the rainy season, and the increased difficulty of distributing fuel and other necessities for transportation make it likely that transportation costs will be higher during the growing seasons (Alderman and Shively, 1996). Transaction costs are the costs incurred in making an economic exchange: determining the price and the demand for a good in a market, the cost of bargaining for a fair price, and the cost of policing and enforcement in the market (Asante et al., 1989, Fafchamps, 2004). All of these costs are also likely to be seasonal. These sources of non-food production variability in the seasonality of food prices can also be estimated with remote sensing data.

## **3. Data**

#### **3.1. MODIS data**

MODIS Nadir BRDF-Adjusted Reflectance data (Schaaf et al., 2002) at 0.05° spatial resolution with temporal resolution of 16 days (MCD43C4) and temporal coverage from 2001 through 2011 were employed to derive the Normalized Difference Vegetation Index as:

$$\text{NNDVI} = \left( \text{NIR} \cdot \text{RED} \right) / \left( \text{NIR} + \text{RED} \right) \tag{1}$$

Where, NIR is the near infrared reflectance of MODIS band 2 (841-876 nm) and RED is MODIS band 1 (620-670 nm). NDVI is an often used vegetation index (Brown et al., 2006, Karnieli et al., 2010) which exploits the significant difference between NIR and Red reflectance for living vegetation. Healthy living vegetation strongly absorbs red reflectance and strongly reflects NIR.

#### **3.2. MOD 16 evapotranspiration data**

We used the MOD16 global evapotranspiration product at 0.05° spatial resolution and 16-day temporal resolution. The ET algorithm is based on the Penman-Monteith equation (Monteith, 1965, Mu et al., 2011). We accumulated the global evapotranspiration starting in January and July to account for the different growing seasons in the Northern and Southern Hemisphere.

#### **3.3. Market data**

The data used in this paper is from a continuously updated price database comprised of food prices from 232 markets in 39 countries, collected by the FAO and the US Agency for Interna‐ tional Development's Famine Early Warning Systems Network (FEWS NET). The data is available from the FAO at the Global Information and Early Warning System (GIEWS) website: http://www.fao.org/giews/pricetool/. We selected all the available markets that are located on the African continent. We have a total of 933 time series with market price information for 51 different products ranging from fuel to cattle to grain crops. The most common commodities were maize (137 price series), sorghum (118 price series), rice (102 price series), millet (56 price series), beans (43 price series) and cowpea (40 price series).The monthly time series differ in length with some starting as early as 1997 while others started in 2009. For time series with long data, only the period from 2000 was analyzed, and for those beginning after 2000, we report only the stations with at least four years of continuous data. We created a 0.5° buffer around the markets and determined the percentage of cropland within these buffers.

## **4. Methods**

lower (higher) prices during the post harvest season because of the inability or unwillingness

Another source of seasonality in food prices in thin markets is the seasonality of transaction costs, as well as transportation costs. Little is known about the variability of transportation costs in each of the markets of this study, but rainfall and poor roads, increased demand for movement of goods and people during the rainy season, and the increased difficulty of distributing fuel and other necessities for transportation make it likely that transportation costs will be higher during the growing seasons (Alderman and Shively, 1996). Transaction costs are the costs incurred in making an economic exchange: determining the price and the demand for a good in a market, the cost of bargaining for a fair price, and the cost of policing and enforcement in the market (Asante et al., 1989, Fafchamps, 2004). All of these costs are also likely to be seasonal. These sources of non-food production variability in the seasonality of

MODIS Nadir BRDF-Adjusted Reflectance data (Schaaf et al., 2002) at 0.05° spatial resolution with temporal resolution of 16 days (MCD43C4) and temporal coverage from 2001 through

Where, NIR is the near infrared reflectance of MODIS band 2 (841-876 nm) and RED is MODIS band 1 (620-670 nm). NDVI is an often used vegetation index (Brown et al., 2006, Karnieli et al., 2010) which exploits the significant difference between NIR and Red reflectance for living vegetation. Healthy living vegetation strongly absorbs red reflectance and strongly reflects

We used the MOD16 global evapotranspiration product at 0.05° spatial resolution and 16-day temporal resolution. The ET algorithm is based on the Penman-Monteith equation (Monteith, 1965, Mu et al., 2011). We accumulated the global evapotranspiration starting in January and July to account for the different growing seasons in the Northern and Southern Hemisphere.

The data used in this paper is from a continuously updated price database comprised of food prices from 232 markets in 39 countries, collected by the FAO and the US Agency for Interna‐ tional Development's Famine Early Warning Systems Network (FEWS NET). The data is

NDVI = NIR - RED / NIR + RED ( ) ( ) (1)

2011 were employed to derive the Normalized Difference Vegetation Index as:

of households and traders to store grain.

192 Climate Variability - Regional and Thematic Patterns

**3. Data**

NIR.

**3.3. Market data**

**3.2. MOD 16 evapotranspiration data**

**3.1. MODIS data**

food prices can also be estimated with remote sensing data.

#### **4.1. Land surface phenology metrics**

To extract the peak height in NDVI and the peak timing based on accumulated evapotranspi‐ ration, we fit quadratic models with accumulated evapotranspiration based on MOD16 as the independent variable and NDVI as the dependent variable (Figure 1). We have used these models before based on Accumulated Growing Degree Days (AGDD) in other parts of the world (de Beurs and Henebry, 2008a,b, 2010). We have demonstrated recently that models based on moisture variables resulted in higher R2 values in large areas around the world including Africa (Brown et al., 2012). In that paper we based our analysis on AVHRR data and we extend that work here with MODIS data. We fit the model two times for each pixel and year, once with data beginning in January and ending in December, and once with data beginning in July and ending in June. Figure 1 provides an example of the quadratic models for a market in Niger. For each 0.05° we calculate the NDVI peak height and the amount of accumulated evapotranspiration necessary to reach this peak NDVI. We also derive the day of the year for which the peak NDVI is reached.

#### **4.2. Market analysis**

Market prices are provided in monthly time series. Most market price series show a steady increase between 2000 and 2011 as a result of inflation and changes in world mar‐ ket prices. In this study we are not interested in the trend in these time series but rather in its seasonality. We calculate in which month, on average, the maximum price occurred, and we calculate the seasonal price spread. In addition, we calculate the difference be‐ tween the maximum market price in each year and the minimum market price in the pre‐ ceding eight months (Figure 2).

We apply Spearman's rank correlation to calculate the correlation between these price differences and the peak height based on our land surface phenology metrics (Figure 3).

**Figure 1.** Example of a quadratic model fit for a station as far north as Ouallam, Niger. The example is created based on data from 2009. The NDVI peak height and the accumulated evapotranspiration at peak are calculated based on the quadratic regression model.

**5.2. Correlation between vegetation peak height and price increases**

highest price is found.

We found that the significance of the correlation between price increases and NDVI peaks in the surrounding areas differed strongly by product. For example, we found that only 13% of the rice price series correlated significantly (p<0.1) with NDVI peaks, while 35% of the cowpea

**Figure 2.** Example of a price time series of millet prices in Ouallam, Niger. For each year we determine the highest price (in grey dots). We also determine the difference between the highest price and the lowest price in the preceding 8-month period (shown in black triangles). Based on the highest prices we calculate in which month on average the

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price time series correlated strongly with NDVI peaks in the surroundings (Table 1).

**Table 1.** Percent of significant correlation between market price increases and NDVI at peak by crop type.

Table 2 provides the 10 countries for which we have the largest number of market price series. We found a fair bit of variability in the number of significant correlations by country. For

*Crop Number of markets % of sign. correlations*

**Cowpea** 26 35% **Sorghum** 76 32% **Beans** 30 23% **Millet** 41 22% **Maize** 89 22% **Rice** 46 13%

#### **5. Results**

#### **5.1 Seasonal timing of vegetation and prices**

The timing of the annual price maximum reveals a basic north-south pattern similar to the timing of the peak of the growing season observed by the land surface phenology models (Figure 4). The highest prices in West Africa occur in June and July in the far west, and in August and September in the central region. In East Africa the peak times occur in October and November, although a fair bit of variability can be observed. Southeastern Africa reveals highest prices in the months December through March. Figure 4 reveals that a great number of price time series peak around or slightly before the time that the vegetation as observed by satellite data peaks. There are a fair number of outliers, which are likely the result of nonenvironmental factors weighing more strongly on the price time series.

Figure 5 shows the seasonal price spread. The spread is highest for southeastern Africa and lowest for Western Africa. A low seasonal price spread indicates a certain amount of predict‐ ability about when prices may peak during the year.

**Figure 2.** Example of a price time series of millet prices in Ouallam, Niger. For each year we determine the highest price (in grey dots). We also determine the difference between the highest price and the lowest price in the preceding 8-month period (shown in black triangles). Based on the highest prices we calculate in which month on average the highest price is found.

#### **5.2. Correlation between vegetation peak height and price increases**

**5. Results**

the quadratic regression model.

194 Climate Variability - Regional and Thematic Patterns

**5.1 Seasonal timing of vegetation and prices**

The timing of the annual price maximum reveals a basic north-south pattern similar to the timing of the peak of the growing season observed by the land surface phenology models (Figure 4). The highest prices in West Africa occur in June and July in the far west, and in August and September in the central region. In East Africa the peak times occur in October and November, although a fair bit of variability can be observed. Southeastern Africa reveals highest prices in the months December through March. Figure 4 reveals that a great number of price time series peak around or slightly before the time that the vegetation as observed by satellite data peaks. There are a fair number of outliers, which are likely the result of non-

**Figure 1.** Example of a quadratic model fit for a station as far north as Ouallam, Niger. The example is created based on data from 2009. The NDVI peak height and the accumulated evapotranspiration at peak are calculated based on

Figure 5 shows the seasonal price spread. The spread is highest for southeastern Africa and lowest for Western Africa. A low seasonal price spread indicates a certain amount of predict‐

environmental factors weighing more strongly on the price time series.

ability about when prices may peak during the year.

We found that the significance of the correlation between price increases and NDVI peaks in the surrounding areas differed strongly by product. For example, we found that only 13% of the rice price series correlated significantly (p<0.1) with NDVI peaks, while 35% of the cowpea price time series correlated strongly with NDVI peaks in the surroundings (Table 1).

**Table 1.** Percent of significant correlation between market price increases and NDVI at peak by crop type.

Table 2 provides the 10 countries for which we have the largest number of market price series. We found a fair bit of variability in the number of significant correlations by country. For

**Figure 3.** Example of correlation between the peak height of NDVI (Figure 1) and the price difference calculated based on market prices (Figure 2) for Ouallam, Niger. Each dot indicates a peak height x price difference combination for one year. We are showing the years 2004 – 2011. The final reported rank correlation for this area was -0.56, with a p-value of 0.037.

example, Nigeria, Somalia and Niger all revealed significant correlations between price increases and NDVI peak height for a large percentage of their markets (50%, 42% and 33%, respectively), while only 11% of the markets in Burundi revealed significant correlations.

**Figure 4.** Mean month with the highest prices corresponds reasonably well with the peak timing based on NDVI.

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**Table 2.** Percent of significant correlation between market price increases and NDVI at peak by country.

The Effect of Agricultural Growing Season Change on Market Prices in Africa http://dx.doi.org/10.5772/56459 197

example, Nigeria, Somalia and Niger all revealed significant correlations between price increases and NDVI peak height for a large percentage of their markets (50%, 42% and 33%, respectively), while only 11% of the markets in Burundi revealed significant correlations.

**Figure 3.** Example of correlation between the peak height of NDVI (Figure 1) and the price difference calculated based on market prices (Figure 2) for Ouallam, Niger. Each dot indicates a peak height x price difference combination for one year. We are showing the years 2004 – 2011. The final reported rank correlation for this area was -0.56, with a

*Country Number of markets % of sign. correlations*

**Table 2.** Percent of significant correlation between market price increases and NDVI at peak by country.

**Nigeria** 22 50% **Somalia** 45 42% **Niger** 24 33% **Kenya** 18 22% **Tanzania** 28 18% **Mali** 22 18% **Mozambique** 22 14% **Burkina Faso** 15 13% **Uganda** 17 12% **Burundi** 18 11%

p-value of 0.037.

196 Climate Variability - Regional and Thematic Patterns

**Figure 4.** Mean month with the highest prices corresponds reasonably well with the peak timing based on NDVI.

consume rice, but need to import rice to meet domestic consumption needs. Thus, our lack of correlation between rice price series and NDVI in the surrounding regions appears reasonable (Table 1 and 3). Millet is one of the products that can be grown in semi-arid zones and is most widely available (Brown, 2008). The ability of millet to grow in semi-arid zones likely results in the higher number of markets that reveal significant correlations between prices and

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**Table 3.** Percent of significant correlation between market prices and amount of evapotranspiration at peak by crop

Eight of the countries investigated are classified as having low food security; Nigeria and Bur‐ kina Faso are classified as middle food security (Yu et al., 2010). All have unfavorable climates for agriculture, except for Uganda and Burundi, which also show the lowest amount of correla‐ tion between NDVI peak height and price increases (Table 2). It appears that price changes in these two countries are less affected by weather conditions. More than 70% of cereal needs are generally met through domestic production in Mali, Burkina Faso and Niger (Kelly et al., 2008). As a result, food prices tend to rise as a result of production short falls. We found that prices in these countries revealed very high correlation with peak height NDVI (Niger, Table 2) and

amount of accumulated evapotranspiration at peak (Mali and Burkina Faso, Table 4).

*Country Number of Markets % of sign. correlations*

**Table 4.** Percent of significant correlation between market prices and amount of evapotranspiration at peak by

**Mali** 23 48% **Kenya** 22 45% **Burkina Faso** 15 40% **Nigeria** 25 36% **Uganda** 22 32% **Somalia** 49 29% **Niger** 24 25% **Mozambique** 23 22% **Burundi** 22 18% **Tanzania** 34 15%

*Crop Number of markets % of sign. correlations*

**Millet** 46 35% **Sorghum** 84 32% **Maize** 112 31% **Rice** 54 30% **Cowpea** 26 23% **Beans** 36 16%

evapotranspiration (Table 3).

type.

country.

**Figure 5.** High seasonal price spread indicates more variability in the timing of the price peaks. Low price spread indi‐ cates that the annual maximum prices are found approximately in the same month every year.

#### **5.3. Correlation between the amount of accumulated evapotranspiration at peak vegetation and prices**

When we calculated the correlation between the peak timing in accumulated evapotranspira‐ tion and actual price values at their annual peak, we found a high number of markets with significant correlation, especially for millet. Beans revealed the lowest number of markets with significant correlation (16%). When we investigate the correlations by country, we find the largest number of significant correlations in Mali (Table 4), where 48% of the market price series correlate significantly with the peak timing in evapotranspiration units. Kenya and Burkina Faso also show a large number of significant correlations (45% and 40%). The lowest number of significant correlations was found in Tanzania, where only 15% of market price series correlated significantly with the amount of accumulated evapotranspiration at peak timing.

#### **6. Discussion**

In Sub-Saharan Africa, rice is the dominant commodity that appears to reflect changes in world prices (Kelly et al., 2008). Consequently, it is profitable to import rice, while arbitrage oppor‐ tunities for grain trade are significantly lower. Further, several countries (many in West Africa) consume rice, but need to import rice to meet domestic consumption needs. Thus, our lack of correlation between rice price series and NDVI in the surrounding regions appears reasonable (Table 1 and 3). Millet is one of the products that can be grown in semi-arid zones and is most widely available (Brown, 2008). The ability of millet to grow in semi-arid zones likely results in the higher number of markets that reveal significant correlations between prices and evapotranspiration (Table 3).

**Table 3.** Percent of significant correlation between market prices and amount of evapotranspiration at peak by crop type.

Eight of the countries investigated are classified as having low food security; Nigeria and Bur‐ kina Faso are classified as middle food security (Yu et al., 2010). All have unfavorable climates for agriculture, except for Uganda and Burundi, which also show the lowest amount of correla‐ tion between NDVI peak height and price increases (Table 2). It appears that price changes in these two countries are less affected by weather conditions. More than 70% of cereal needs are generally met through domestic production in Mali, Burkina Faso and Niger (Kelly et al., 2008). As a result, food prices tend to rise as a result of production short falls. We found that prices in these countries revealed very high correlation with peak height NDVI (Niger, Table 2) and amount of accumulated evapotranspiration at peak (Mali and Burkina Faso, Table 4).

**5.3. Correlation between the amount of accumulated evapotranspiration at peak vegetation**

**Figure 5.** High seasonal price spread indicates more variability in the timing of the price peaks. Low price spread indi‐

cates that the annual maximum prices are found approximately in the same month every year.

When we calculated the correlation between the peak timing in accumulated evapotranspira‐ tion and actual price values at their annual peak, we found a high number of markets with significant correlation, especially for millet. Beans revealed the lowest number of markets with significant correlation (16%). When we investigate the correlations by country, we find the largest number of significant correlations in Mali (Table 4), where 48% of the market price series correlate significantly with the peak timing in evapotranspiration units. Kenya and Burkina Faso also show a large number of significant correlations (45% and 40%). The lowest number of significant correlations was found in Tanzania, where only 15% of market price series correlated significantly with the amount of accumulated evapotranspiration at peak

In Sub-Saharan Africa, rice is the dominant commodity that appears to reflect changes in world prices (Kelly et al., 2008). Consequently, it is profitable to import rice, while arbitrage oppor‐ tunities for grain trade are significantly lower. Further, several countries (many in West Africa)

**and prices**

198 Climate Variability - Regional and Thematic Patterns

timing.

**6. Discussion**

**Table 4.** Percent of significant correlation between market prices and amount of evapotranspiration at peak by country.

#### **7. Conclusions**

Preliminary conclusion based on this analysis is that we can get a better understanding of where satellite data could aid in the prediction of local market prices. For example, prices may be driven by different factors in Uganda and Burundi than in Niger and Nigeria. If we combine our knowledge of the effect of large scale climate oscillations on the land surface phenology (Brown et al., 2010) with the links between price time series and land surface phenology, we may be able to get an understanding of where we could predict local prices. Satellite data may be most effective in predicting local prices in poorly integrated markets. Poorly integrated markets are often 'thin' markets that have too little supply during times of high demand, such as right before harvest, resulting in food shortages. Seasonal price changes may reflect changes in production; in good years infrastructure and trade contraints reduce the ability of traders to move excess grain out of the area, and in poor years food is not moved into the area.

[6] Brown, M.E., de Beurs, K.M. (2008). Evaluation of multi-sensor semi-arid crop season parameters based on NDVI and rainfall. Remote Sensing of Environment, 112, 5,

The Effect of Agricultural Growing Season Change on Market Prices in Africa

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[7] Brown, M.E., de Beurs, K.M., Marshall, M. (2012). Global phenological response to climate change in crop areas using satellite remote sensing of vegetation, humidity and temperature over 26 years. Remote Sensing of Environment, 126, 16, 174-183.

[8] Brown, M.E., Pinzon, J.E., Didan, K., Morisette, J.T., Tucker, C.J. (2006). Evaluation of the consistency of long-term NDVI time series derived from AVHRR, SPOT-vegeta‐ tion, SeaWiFS, MODIS, and Landsat ETM+ sensors. IEEE Transactions of Geoscience

[9] de Beurs, K.M., Henebry, G.M. (2004). Land surface phenology, climatic variation, and institutional change: analyzing agricultural land cover change in Kazakhstan. Remote Sensing of Environment, 89,4, 497-509. doi:410.1016/j.rse.2003.1011.1006

[10] de Beurs, K.M., Henebry, G.M. (2008a). Northern Annular Mode effects on the land surface phenologies of Northern Eurasia. Journal of Climate, 21, 4257-4279.

[11] de Beurs, K.M., Henebry, G.M. (2008b). War, drought, and phenology: Changes in the land surface phenology of Afghanistan since 1982. Journal of Land Use Science, 3,

[12] de Beurs, K.M., Henebry, G.M. (2010). A land surface phenology assessment of the northern polar regions using MODIS reflectance time series. Canadian Journal of Re‐

[13] Fafchamps, M. (1992). Cash crop production, food price volatility, and rural market integration in the third world. American Journal of Agricultural Economics, 72, 1,

[14] Fafchamps, M. (2004). Market Institutions in Sub-Saharan Africa: Theory and Evi‐

[15] Funk, C., Budde, M. (2009). Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe. Remote Sensing of Environment, 113,1, 115–125.

[16] Funk, C., Brown, M.E. (2009). Declining global per capita agricultural production and warming oceans threaten food security. Food Security, 1, 3, 271-289. doi:10.1007/

[17] IAASTD (2008) International Assessment of Agricultural Knowledge, Science and

[18] Jennings, S., Magrath, J. (2009). What Happened to the Seasons? Seasonality Revisit‐ ed. Oxford, England: Institute of Development Studies. A paper for the Future Agri‐

cultures Consortium International Conference on Seasonality, July 2009.

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and Remote Sensing, 44, 7,1787-1793.

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Technology for Development. Island Press, London

### **Author details**

K. M. de Beurs1 and M. E. Brown2

1 Department of Geography and Environmental Sustainability, The University of Oklaho‐ ma, Norman, OK, USA

2 Biospheric Sciences Branch, Code 614.4, NASA Goddard Space Flight Center, Greenbelt, MD, USA

#### **References**

[6] Brown, M.E., de Beurs, K.M. (2008). Evaluation of multi-sensor semi-arid crop season parameters based on NDVI and rainfall. Remote Sensing of Environment, 112, 5, 2261-2271. doi:10.1016/j.rse.2007.10.008

**7. Conclusions**

200 Climate Variability - Regional and Thematic Patterns

**Author details**

K. M. de Beurs1

MD, USA

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and M. E. Brown2

Bank, Accra and Washington DC

Springer Verlag, Heidelberg.

114, 10, 2286-2296. doi:10.1016/j.rse.2010.05.005

nomics, 2nd Edition.

Preliminary conclusion based on this analysis is that we can get a better understanding of where satellite data could aid in the prediction of local market prices. For example, prices may be driven by different factors in Uganda and Burundi than in Niger and Nigeria. If we combine our knowledge of the effect of large scale climate oscillations on the land surface phenology (Brown et al., 2010) with the links between price time series and land surface phenology, we may be able to get an understanding of where we could predict local prices. Satellite data may be most effective in predicting local prices in poorly integrated markets. Poorly integrated markets are often 'thin' markets that have too little supply during times of high demand, such as right before harvest, resulting in food shortages. Seasonal price changes may reflect changes in production; in good years infrastructure and trade contraints reduce the ability of traders to move excess grain out of the area, and in poor years food is not moved into the area.

1 Department of Geography and Environmental Sustainability, The University of Oklaho‐

2 Biospheric Sciences Branch, Code 614.4, NASA Goddard Space Flight Center, Greenbelt,

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## *Edited by Aondover Tarhule*

As societies transition to evidence-based adaptation and management there is increasing recognition of the need for understanding climate change and variability dynamics and impacts at regional levels and for various activities. This book is a contribution toward that goal. Readers interested in climate change management will find detailed discussions of climatic variability dynamics in selected regions as well as new innovative ways of monitoring climate change, assessing climate risks, and predicting impacts. Those interested in refreshing the fundamentals of climate change and climate variability will find a very accessible review of the status of knowledge on the subject, including a balanced interrogation of available evidence. In an attempt to keep the book accessible, every effort was made to minimize technical jargon without compromising scientific accuracy. The result should be useful to researchers, practitioners, and policy makers.

Climate Variability - Regional and Thematic Patterns

Climate Variability

Regional and Thematic Patterns

*Edited by Aondover Tarhule*

Photo by f9photos / iStock


**3. EV involvement in energy management system**

Europe owns the assets and it belongs to certain stakeholders, but has a regulation to guarantee its independence. On the other hand, ISO is fully unbundled operator having no assets

In Japan, community energy management system (CEMS) has been proposed and demon‐ strated. The main purpose of CEMS is realizing a resilient and smart community, especially related to efficiency in energy utilization and minimization of CO2 emission. The concept of CEMS comes from the demand to optimize the energy services, maximize the potential economy, and minimize the environmental impacts. CEMS coordinates and monitors all the energy supply and demand throughout the community, hence improving the comfort, security, and safety of the whole community members. In CEMS, the streams of energy and information are flowing simultaneously covering supply, demand, storage, and distribution. As a system, CEMS must be robust and secured because it deals with individual information

**Figure 1** shows the basic concept of CEMS. CEMS has an important role of monitoring and controlling all the energy involved in the community. It becomes the core of efficient, secured, and optimized energy utilization throughout the community. CEMS is able to communicate with other entities inside and outside its authority. Inside the community, CEMS communi‐ cates intensively with its lower EMSs such as home energy management system (HEMS), building energy management system (BEMS), and factory energy management system (FEMS). In addition, it also may communicate directly with EVs distributed in the community which are not controlled under certain EMS. CEMS is also able to communicate with other

although still belongs to certain stakeholders.

128 Modeling and Simulation for Electric Vehicle Applications

and its authentication.

**Figure 1.** Basic concept of CEMS.

EVs can be applied to support the grid electricity, mainly providing energy storage and ancillary services, due to the above-mentioned characteristics. As energy storage, EVs can be charged when the electricity price is relatively low because of surplus electricity in supply side, including RE and excess power, and lower demand (such as during night time). Furthermore, the ancillary services which can be performed by EVs to support the grid electricity mainly cover frequency regulation (up and down regulations), electricity storage, and spinning (synchronous) reserve. Compared to other energy storages or generators, EVs have a very advantageous characteristic which is its ability to charge and discharge instantaneously their electricity once they received the command from the operator or EMS. The above-mentioned ancillary services are required to preserve the balance between supply and demand of electricity, therefore the electricity can be appropriately reliable.

To participate in the ancillary services, the EV owners are requested to have any service contract with the operator or EMS. Possible utilization contract schemes of EVs to support the grid electricity or any electricity-related system are shown in **Figure 2**. In general, the partici‐ pation of EVs in the electricity market could be conducted through two types of participation contracts, they are direct and aggregator-based contracts. To facilitate a communication between EVs and operators, a real time data collection is conducted by vehicle information system (VIS) in a certain time interval from EVs covering battery state of charge (SOC), position (GPS data), and predicted arrival time. VIS might be owned and managed directly by EMS, aggregator or it is independent as service operator which provides EV information services to EMS and aggregator.

**Figure 2.** Possible contract schemes of EV in EMS: (a) direct contract and (b) aggregator-based contract.

In a direct contract, the EV owners have the service contract directly with the energy service providers (EMS). In this contract scheme, both the electricity and information are transferred privately and directly between EVs and energy service provider. Therefore, this type of con‐ tract is considered applicable for a relatively small-scale EMS, including BEMS and FEMS, and in where EVs are parked and connected to the chargers for relatively long time, for ex‐ ample during working hours. The main advantage of this type of contract scheme is the abil‐ ity to optimize the profit for the involving entities (EV owners and EMS). Furthermore, controls for both charging and discharging are more simple and faster as EVs are directly and fully under control of EMS. The report in this chapter describes mainly on this type of contract scheme.

On the contrary, in an aggregator-based contract scheme, EV owners have service contracts with the aggregators which are providing and managing electricity services. Therefore, there is no direct contract between EV owners and EMS or other electricity-related entities. The information including EV position (GPS), battery condition (SOC), and estimated arrival time is basically coordinated by aggregator via VIS. Based on these data, the aggregator calculates the available number of EVs as well as the total capacity of the battery which is potential for power services. Furthermore, the aggregator can offer and negotiate for electricity services with other electricity-related entities including aggregators, EMS, and/or electricity utilities. It is assumed that this kind of contract scheme is suitable to be adopted for comparatively largescale EMS or electricity utilities. The participating EVs are not only located in certain single place, but they might be distributed in different places as long as both electricity connection and communication are available and possible to perform ancillary services. The electricity absorbed from and discharged to the grid can be transferred through power wheeling service utilizing the available distribution and transmission lines. Aggregator acts as service operator, hence they offer some possible ancillary services to the EV owners. On the contrary, the EV owners have the right to select the offered ancillary service programs and receive the service fee from the aggregator.

Load leveling has a strong correlation with the management of both electricity demand and supply. The main objective is to lower the total grid load (electricity purchased from grid) during peak-load hours and avoid the electricity usage higher than the contracted power capacity by shifting the load from peak to off-peak-load hours. In this study, load leveling is conducted by employing both peak-shift and peak-cut. The former is defined as dislocating the electricity load during peak-load time to off-peak-load time. It can be performed by utilizing the stationary battery or other power storage devices which can absorb and store the electricity during off-peak-load time and discharge it during peak-load time. The latter is described as the effort to reduce the electricity which is purchased from the grid. In real practice, this can be conducted by generating its own energy supply especially during peakload hours, such as RE, or by purchasing the electricity from other entities including the connected EVs. In the case that additional power supply is purchased from EVs, EVs are considered as energy storage and carrier which are storing and transporting the electricity from and to different times and places. Therefore, the economic performance of EVs can be improved by participating in this kind of ancillary services.


**1. Introduction**

The zebrafish and its larvae are becoming prominent vertebrate models in biological and medical research [1–6]. The transparency of zebrafish larvae facilitates convenient observation of the experimental process. Compared to the observations made by naked eyes in early days, the development of modern microscopy and imaging technology has facilitated the implementation of more complicated experiments by obtaining and storing massive zebrafish phenotype imaging data digitally [7].

Early methods of phenotype image analysis mostly rely on manual work which is labour intensive and error prone. Due to the increasing amount of image data, efficient image-processing methods extracting meaningful information from a massive amount of phenotype images are increasingly important [8]. So far, automatic approaches have been proposed for classifying the stages of zebrafish embryo development [3,9,10] and recognition of adult fish behavior [11–13], following classical image-processing techniques like wavelet analysis and fractal geometry [14–16]. These studies opened a new era of computerized zebrafish microscopic image analysis, liberating biologists from tedious manual work and increasing the objectivity of the task.

Comparing to the development of image-analysis methods for zebrafish adults or eggs, the studies on zebrafish larvae are relatively rare. The analysis of the larvae image remains a challenging task due to the complexity and diversity of larvae phenotypes [17–19]. The distinctions among different phenotypes are difficult to identify even for human experts. Among the limited number of existing zebrafish larvae image-analysis studies, Nathalie Jeanray et al. proposed an automatic approach for larvae defects classification from microscopic images using a supervised machine-learning approach [20]. They used dense random sub-windows to extract hand-crafted image features for phenotype defects classification. For nine out of 11 classification categories, their method reached 90% to 100% agreement with consensus voting of biologists. This method gives new insights into automatic recognition of zebrafish larvae phenotypes by replacing human labour with the computerized method.

Even though the machine-learning method using hand-crafted features achieved comparable performance to human experts, there is still significant scope to improve the accuracy and automation of the method. The accuracy of these methods mostly depends on the quality of the extracted features. The process for feature extraction involves subjective factors and human interventions. In contrast to the classical machine-learning methods using hand-crafted features, the newly developed deep-learning (DL) methods automatically extract image features and achieved a dramatic improvement of the classification accuracy. In the last decade, DL methods, especially convolutional neural networks (CNNs), have revolutionised the field of image analysis [21–23]. Despite the success of deep learning in image processing, there are some unique difficulties of zebrafish larvae microscopic images hampering the application of DL approaches:

Considering these difficulties, it is necessary to develop a specific deep-learning method for zebrafish phenotype classification from brightfield microscopic images. In this work, to cope with the scarce training data problem, we adopted the idea of separable convolution from the Xception (extreme inception) network [25] to overcome the overfitting issue caused by limited training data. To tackle the fuzzy inter-class difference and multi-label problems, a two-tier classification strategy is used. The phenotypes that are easy to recognize are firstly recognized in the first-tier classification process. The remaining difficult phenotypes are then recognized with dedicated trained classifiers in the second tier. For each of the difficult phenotypes, we train one network for each single phenotype, so that the networks are more focused on the specific phenotypes. Since each test image is processed with multiple networks, multiple phenotype labels can be assigned to one image, thus the multi-label problem is tackled.

To test our algorithm and compare it fairly with the state-out-the-art solutions, we used a publicly available zebrafish larvae microscopic image dataset [20] which has corresponding human expert annotation labels. Based on the researching achievements of the predecessor, we worked further on this dataset and developed a DL-based zebrafish larvae classification model that is more robust and accurate. In Section 2, the dataset annotated elaborately by the Nathalie Jeanray et al. is described. Section 3 introduces the detailed structure and method. Experimental results are reported in Section 4, with further discussions presented in Section 5.

#### **2. Materials and Methods**

#### *2.1. Data Collection*

To train and develop an effective model for phenotype classification, the quality of the carefully collected dataset is crucial. Ground-truth datasets acquired and annotated by domain experts with the depiction of various classes are required [26]. Research based on publicly available ground-truth datasets has the advantage of enabling better evaluation and comparison of algorithms to enable continuous progress. Therefore, we chose the dataset from the Jeanray et al. group which were produced and collected with rigorous processes [20].

To obtain a collection of different defect phenotypes, Jeanray et al. used increasing concentrations of varied chemicals for zebrafish embryo treatment, including propranolol, amiodarone, acetaminophen, valproic acid caffeine, theophylline 4,5-dichloroanilin, as well as heavy metals such as thallium, methylmercury, lead acetate and zinc sulfate. Phenotype images of control or treated embryos were captured in a lateral view with a high-resolution Olympus SZX10 microscope coupled with a camera and transmitted light illumination. The acquired images were in the size of 2575 × 1932 pixels.

A sophisticated workflow of manual annotation was performed to build a high-quality ground-truth dataset. Three biologists labelled images with different categories of phenotypes by observations only in the first round. For each phenotype assigned to each image, the ground-truth was calculated by majority voting. Ten categories of phenotypes are identified, i.e., "normal", "dead", "chorion", "down curved tail", "hemostasis", "necrosed yolk sac", "edema", "short tail", "up curved tail" and "up curved fish". Sample images for different phenotypes are shown in Figure 1. By contrast with a regular classification dataset, there may be more than one phenotype assigned to a single image since more than one deformity may be found on one larva. Next, all annotations were reviewed by three biologists at the same time in order to reach agreement on phenotypes assigned to each image. After repeating the annotation and voting session, consensus was reached on 870 images. In total, 529 of them acquired in five independent acquisition days were set as a training dataset and other 341 images acquired in three additional acquisition days were integrated into the test dataset. The rigorous process of building the ground-truth dataset has laid the foundation of high-performance phenotype classification algorithms. Table 1 reports the number of training and testing images in this dataset. The training images are to be used for training the machine learning model for phenotype classification. The testing images are used for validating the trained model. The testing images are not included in the training images.

**Table 1.** The number of training and testing zebrafish larvae microscopic images of each phenotype.

**Figure 1.** Example images of ten zebrafish larvae phenotypes from the publicly available dataset provided by Jeanray et al. A detailed description of these phenotypes are referred to the original paper of this dataset [20]. Note that some larvae have multiple phenotype labels, such as the larva of "edema 1" and "up curved fish 1", as well as the larva of "necrosed yolk sac 2" and "up curved fish 2".

#### *2.2. Data Pre-Processing*

One of the greatest impediments to developing deep-learning networks for biological images is that the sizes of training datasets from biological experiments are usually too small. The zebrafish larvae image dataset used in this study is the biggest dataset we could find, but its data size (hundreds of images) is still up to four orders of magnitudes less than the number of images needed to train a deep neural network. Training a deep network using such a small dataset will inevitably cause overfitting, leading to imperfect generalization ability of the network. Another obstacle is that the numbers of each category of the dataset are seriously imbalanced (as Table 1 reports). Imbalanced training samples will result in poor classification performance for those phenotypes with insufficient training samples.

To address the problem of the small and imbalanced training dataset, a data argumentation strategy is applied. Data augmentation is an effective way to extend an existing dataset by creating altered versions of the training data according to possible varied experimental situations. The trained network will be more robust against possible image variations due to the added possible diversification. Image augmentation is proved to be one of the effective solutions for preventing over-fitting. In this way, a wider variety of images is available for training the classification network to make it more robust to possible image variations.

Data-augmentation techniques were chosen based on the peculiarity of the zebrafish larvae microscopic image dataset. The most possible variations of the microscopic images were caused by random larvae placement which resulted in slight differences of the larvae location and orientation. Therefore, each training image was shifted and rotated in different directions to generate pseudo augmented images. We applied small shifting in four directions of the objects, i.e., up, down, left and right. Similarly, we also added small rotations to the original and shifted images in both clockwise and counterclockwise directions to simulate possible deviation caused by manual operation during experiments.

To cope with the imbalanced training data problem, the images of different phenotypes were augmented a different number of times to produce a more balanced dataset. The phenotypes with relatively abundant images (i.e., normal, dead, necrosed yolk sac, and edema) were shifted in each direction four times with 10 pixels step and rotated in each direction three times with 10 degrees interval, therefore they were augmented 44 × (3 × 2) = 1536 times. The phenotypes with middle sample sizes (i.e., hemostasis, short tail, upcurved tail and upcurved fish) were shifted in each direction four times with 10 pixels step and rotated in each direction five times with six degrees interval, therefore they were augmented 44 × (5 × 2) = 2560 times. The phenotypes with scarce training sample (i.e., chorion and down curved tail) were shifted in each direction five times with eight pixels step and rotated in each direction 10 times with three degrees interval, they were augmented 54 × (10 × 2) = 12,500 times. Table 2 summarizes the number of training images of each phenotype before and after data augmentation.

**Table 2.** The number of training images of each phenotype before and after data augmentation.

#### *2.3. Network Structure*

Augmented and normalized images were then fed into our classification module to identify the malformation type of each larva. Unlike regular classification datasets of which one image belongs to one category only, images of zebrafish larvae can be classified to have more than one label. Therefore, we used a two-tier classification pipeline to address the multi-label problem. As Figure 2 shows, the first-tier classifier performs a tri-category classification, classifying the input image as edema, chorion or other phenotypes. We separate edema and chorion from other classes because they are quite easy to recognize, and it is not necessary to train individual classifiers for them. If the image is classified as others, it will go into the second-tier classification process in which eight binary classifiers are applied in parallel. Each of the classifiers is only responsible to judge whether the larva belongs to one of the eight classes. The objective of the second-tier classification is to find out whether the larva has one or more of the labels out of the eight phenotypes.

**Figure 2.** The pipeline of the two-tier classification strategy.

#### *2.4. Larva Classification Network*

For the entire pipeline, we use the same network structure for both the first- and second-tier classification. We adopt the Xception network as the classifier model. The network structure is illustrated in Figure 3. For conciseness of the article, we refer the readers to [25] for a detailed explanation of the network architecture. For the first-tier classification, the network outputs the probabilities of three phenotypes, i.e., edema, chorion and others. The phenotype label with the maximum probability is assigned to the target larva. For the second-tier classification, the same network is used for each binary classifier, outputting the probability of belonging to each of the eight phenotypes. If the probability of a certain phenotype is over 0.5, the larva is classified to have the label of this phenotype.

**Figure 3.** The classifier network architecture used in this study. The abbreviations 'conv' and 'ReLU' stands for 'convolution kernel' and 'rectified linear unit', respectively.

So far, various types of convolutional networks have been developed for image classification. The reason we chose the Xception network instead of other classical networks (e.g., VGG16 [27]) is that the Xception network uses the so-called 'separable convolution' mechanism (shown as the blue blocks in Figure 3) to reduce the number of network hyperparameters. Figure 4 compares the separable convolution architecture with conventional convolution architecture. The input of the convolutional architecture is a k-channel feature map (k = 4 in Figure 4 as an example) produced by the previous network layer. The separable convolution architecture (Figure 4a) conducts separated channel-wise and space-wise convolutions while the conventional convolution architecture (Figure 4b) conducts channel-wise and space-wise convolutions simultaneously using a k × 3 × 3 kernel containing 9k hyperparameters. The channel-wise convolution is firstly performed using a k × 1 × 1 kernel, producing a single-channel intermediate feature map which is further convoluted with a 1 × 3 × 3 kernel. Such separable convolution uses only 9 + k hyperparameters which is much less than the 9k parameters of the conventional convolution. The merit of separable convolution for zebrafish larvae image classification is that it avoids overfitting to the limited training data because the network has much fewer hyperparameters than the conventional CNN.

**Figure 4.** Comparison between (**a**) the separable convolution architecture and (**b**) the conventional convolution architecture. Different colours of the input feature map indicate different feature channels. k is the number of feature channels and this figure uses k = 4 as an example.

To further relieve the problem of scarce training data, a technique called transfer learning is used to train a robust network for complicated zebrafish larvae classification. By transfer learning, we can obtain deep features that an existing model has learned from tens of thousands of natural images, therefore the burden of training such a deep network from scratch is alleviated. We used an existing Xception network pretrained on the imagenet datasets [25] and froze the hyperparameters of the feature extraction layers, leaving only the classification layers trainable (i.e., the global average pooling layer, the fully-connected layers and the logistic regression layer as marked in Figure 3). These classification layers were trained on our augmented dataset using the stochastic gradient descent (SGD) optimizer with a slow learning rate of 0.0001 and a momentum of 0.9. We used such a slow learning rate because the network was already pretrained, the learning rate should be small enough to fine tune the network to suit the limited training data of zebrafish larvae.

#### **3. Results**

Thanks to the availability of public zebrafish larvae microscopic images, we can compare our approach with the published state-of-the-art machine learning methods based on the same training and test data. As explained in the Introduction Section, the strength of our method lies in the usage of a two-tier classification pipeline and the Xception network. To prove the advantages of the two-tier pipeline, we compare our method with a single-tier classification pipeline. To evaluate the effectiveness of the Xception network, we run a comparison with an existing method using other classical CNN models, including AlexNet, the Visual Geometry Group networks of 16 and 19 layers (VGG16 and VGG19) and GooLeNet [24,27,28]. All these comparisons were conducted using the classification accuracy as the performance metric, i.e., Accuracy = (NTP + NTN)/Nall, where NTP, NTN and Nall denote the number of true positive, true negative, and all test samples respectively for each phenotype. Our networks were programmed with the Keras platform running on a server using NVIDIA TITAN X graphics processing unit (GPU). The training process took −200 min on the entire augmented training set and took −2 s on each test image.

#### *3.1. Comparison with the State-of-the-Art Larva Phenotype Classification Method*

In the previous study by Jeanray et al. [20], they published the zebrafish larvae image dataset along with a supervised phenotype classification algorithm using a randomized trees classifier, which is a conventional machine learning method dedicatedly adapted to the zebrafish larvae images. They also used a two-tier classification strategy quite similar to ours, and therefore the major difference between our and their method is whether the deep learning approach was used. We refer Jeanray's method as the state-of-the-art baseline method and conducted the comparison based on the same training images (as listed in Table 2) and testing images (as listed in Table 1). Figure 5 plots the classification accuracies of the two methods. The accuracy values of the baseline method were collected

from Jeanray's original paper [20]. Our method achieved evident accuracy improvement over the baseline method for most phenotypes. For the phenotypes which are easy to recognize (dead and chorion), our method yielded an accuracy of 1.0 which is quite promising. Jeanray's method also obtained accuracy over 0.95 for these phenotypes but did not reach 1.0. The biggest improvement is with hemostasis, for which our method obtained an accuracy of 0.73 vs. 0.51 of the baseline method. Normal and short tail are the two phenotypes for which our method yielded slightly lower accuracies than the baseline method, but the differences (0.010 and 0.005 for normal and short tail, respectively) were too small to be considered significant.

**Figure 5.** Comparison of classification accuracy between our method and the state-of-the-art baseline method.

### *3.2. Comparison with the Single-Tier Classification Pipeline*

In this study, we used the two-tier pipeline to make the classifiers focus on the fuzzy phenotypes which are difficult to recognize. To evaluate the effectiveness of the two-tier pipeline, we also conducted phenotype classification based on a single-tier pipeline, in which 10 separated binary classifiers were trained for each of the 10 phenotypes. By doing so, each classifier has to recognize one phenotype from 10 phenotypes, which might be more difficult than recognizing one from eight as in the two-tier pipeline. Figure 6 compares the accuracies of the two-tier vs. single-tier pipelines. It can be observed that although the two-tier pipeline does not outperform the single-tier pipeline for all the phenotypes, and it achieved much better accuracy for those difficult phenotypes including edema, hemostasis and down curved tail whose accuracies were improved by 0.06, 0.03 and 0.09, respectively.

**Figure 6.** Comparison of classification accuracy between the two-tier and the single-tier pipelines.

#### *3.3. Comparison with the Classical Convolutional Neural Network (CNN) Model*

To validate the advantage of using the Xception network instead of a conventional CNN, we compare our method with an existing study using the classical CNN model [19]. VGG16 is a widely used CNN model for image classification [27]. Tyagi et al. fine tuned a pre-trained VGG16 model for zebrafish larvae microscopic image classification [19]. Their experiments were also based on the same ground-truth dataset from the Jeanray group. In their experiment, the network was trained to classy different combinations of five, eight and all phenotypes, respectively. They aimed to validate the classicization performance for different numbers of target phenotypes. The combination of five phenotypes included hemostasis, necrosed yolk sac, edema, short tail and normal. The combination of eight phenotypes included the normal, dead, down-curved tail, chorion, up-curved tail, short tail, edema and necrosed yolk sac.

To conduct a fair comparison, we followed the same experiment setup of Tyagi et al. [19] That is, our Xception network was also trained to classify the same combinations of five, eight and all phenotypes, respectively. The averaged classification accuracy of each combination is compared with Tyagi's accuracy in Table 3. For all the combinations, the Xception network resulted in better accuracy than the VGG16 network. When more phenotypes are included in the combination, the improvement of accuracy becomes larger. This result means that the Xception network is more suitable for multi-phenotype classification and better suits the multi-label nature of the zebrafish larvae.

**Table 3.** Comparison of classification accuracy between our Xception-based method with Tyagi's VGG16-based method [19] and our finetuning based on AlexNet [24], VGG19 [27], and GoogLeNet [28].

Besides the VGG16-based method of Tyagi et al., we further compared our method with transfer learning using three other popular CNN models including AlexNet [24], VGG19 [27], and GoogLeNet [28] which were all pretrained with the ImageNet data. For a fair comparison, these networks were also validated on the combinations of five, eight and all phenotypes, respectively. The validation results are reported in Table 3 as well. For the five-phenotype combination, VGG19 achieved the best accuracy (0.88) while our Xception-based method yielded suboptimal accuracy (0.86). For both the eight- and all-phenotype combinations, our method was the most accurate.

### **4. Discussion**

In this study, we take advantage of deep learning to classify zebrafish larvae phenotypes from brightfield microscopic images. We aim to solve the classification problems especially for those phenotypes with fuzzy class features. Based on the same training and testing data, Jeanray et al. had already developed a larvae phenotype classification method using the classical machine learning method [20]. Their method provides a state-of-the-art baseline for our DL-based approach. The experimental results of Figure 5 reveals that our deep-learning method outperforms the baseline method in terms of classification accuracy. Besides the accuracy improvement, the advantages of the deep-learning method also include the avoidance of designing hand-crafted image features. Jeanray et al. used image edge and sub-window intensity distribution as the image features, but these features may not be optimal for classifying some difficult phenotypes like edema and hemostasis. Our deep-learning approach automatically learns the crucial image features for phenotype classification. The deep-learning approach not only relieves the algorithm developers from the burden of image

feature design but also automatically discovers the useful features which the human designers cannot conceive of. As a result, we observe dramatic accuracy improvement for the phenotypes with fuzzy class features, such as edema and hemostasis.

To address the scarce training data problem, we performed augmentation to the training data. We augmented different phenotypes with different scales to compensate for the imbalanced training data. As Figure 5 reveals, this strategy decreased the inter-class accuracy differences compared to the baseline method using original training data. However, some phenotypes with too few original training samples (e.g., hemostasis) still have relatively lower accuracy than other phenotypes. This is because our augmentation process only implemented image shifting and rotation, its capacity to improve data variability is limited. For a future study, we will introduce more variations into the data augmentation step, such as non-linear image deformation and image contrast variations [29].

To cope with the problems of fuzzy inter-class differences and multi-label issues, we used the two-tier classification pipeline. Experiment results proved the effectiveness of the two-tier strategy. Accuracy improvements against the single-tier pipeline were observed for the difficult classes like edema, hemostasis and down-curved tail. The improvements for these phenotypes are 0.06, 0.03 and 0.09, respectively. It is worth mentioning that Jeanray et al. also compared the two-tier and single-tier pipelines using their randomized tree classifier [20]. However, their two-tier pipeline only improved the accuracy of edema and down-curved tail by 0.01 and 0.03, respectively, which are much less than our improvements. For hemostasis, their two-tier pipeline even resulted in a 0.03 decrease in accuracy. It is obvious that the advantage of the two-tier strategy is more obvious for our method, thanks to the superior classification ability of the DL models.

Currently, there are various types of DL models for image classification. We chose Xception as the classifier because of its small hyperparameter scale [25]. Training a smaller network on scarce annotated data can potentially avoid the overfitting problem because larger networks are more flexible on data fitting and tend to overfit the limited training data. The conventional VGG16 network [27] has over 138 million parameters which is much more than the 22 M parameters of the Xception network. As we compared our Xception-based approach with the VGG16-based approach [19], an obvious improvement of classification accuracy was observed. More importantly, the improvement becomes bigger when more phenotypes are involved as the target classes. That means the VGG16 network has imperfect generalization ability for more classes. The Xception network successfully alleviated the overfitting issue without losing the accuracy of classification. Our study has explored an effective way to cope with scarce annotation data for biological image classification.

As we further compare our method with more CNN networks, it seems that deeper network tends to yield more accurate results. An intuitive explanation is that deeper networks are better at learning deep features from the large ImageNet dataset and thus performs better at transfer learning. However, the Xception network that we use is not the deepest, but it yielded the most accurate results for eightand all-phenotype combination tests. This might be attributed to the residual connections (i.e., the shortcut connections of 1 × 1 convolution in Figure 3) which does not exist in AlexNet, VGG16, VGG19 or GoogleNet. As demonstrated in the original paper of the Xception network [25], such a residual connection is essential for increasing the classification accuracy since it improved the convergence performance on a large pretraining dataset and helped the network to learn more distinctive image features for transfer learning.

As a limitation of this study, we only tested our method on a publicly available dataset. However, the appearance of zebrafish larvae microscopic images may vary between institutes, acquisition devices and sample preparation procedures. It is important to test our method with more diverse datasets. Moreover, our method also needs to be implemented in an on-site experimental environment to produce real-time classification results. Fortunately, the Xception model has small file size which can be implemented easily in embedded operating systems.

#### **5. Conclusions**

We developed a deep-learning pipeline for zebrafish larvae phenotype classification from brightfield microscopic images. Facing the difficulties of scarce training data, fuzzy inter-class difference and the multi-label problem, we used a two-tier classification pipeline and the Xception network architecture to address all these challenges. Experimental results proved the effectiveness of our solution. We look forward to applying our method in daily biological experiments and making further improvements to process more diverse datasets. The method in this paper is also transferable to other biological phenotype recognition problems for researchers who are facing similar challenges of biological image classification.

**Author Contributions:** Conceptualization, F.C. and S.L.; methodology, S.S.; software, S.S.; validation, S.S.; formal analysis, S.S.; investigation, S.L.; resources, S.L.; data curation, S.S.; writing—original draft preparation, S.S.; writing—review and editing, F.C.; visualization, S.S.; supervision, F.C.; project administration, F.C. and S.L.; funding acquisition, F.C. and S.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research is supported by the National Key R&D Program of China (2018YFC1803100), NSFC grant No. 21607115 and No. 21777116; the Xinghai Scholar Cultivating Funding of Dalian University of Technology, No. DUT14RC(3)066.

**Acknowledgments:** The authors appreciate Hongkai Wang for his advices on deep neural network training.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**3.1 The problem of the twenty first century: how the digital immigrants assess the digital natives?**

*"Our task, regarding creativity, is to help children climb their own mountains, as high as possible. No one can do more."*

*Loris Malaguzzi*

In the digital society changing fast, creativity is accepted as one of necessary basic skills of the twenty first century for professional and personal success [20]. For that reason, many countries have been focusing on the creativity in terms of their education process more and more. It has been a primary subject to determine and develop the creativity of the students in the early ages of them [48, 49]. So, reliable psychometric tests are necessary to select creative individuals and determine the potential of the creative individuals in the field [27, 28]. Consequently, the usage of the assessment scales that will measure the potential of the creativity of the individuals and can make automatic calculations, which are suitable for the conditions of the twenty first century, is not a choice but a necessity.

Education and instruction environments can be defined as the places preparing the individual for life. If it is the case, what kind of assessment scale(s) should we use to measure the creativity of these individuals? Teachers are not always the right people measuring or assessing the creativity in a best way. As an example of it, Torrance's [50] (the father of the creativity was an English teacher) 12-year-study can be given. According to the result of the study, the relationship between the assessment and the creative features of the students could not be found. The same students are seen to have shown their creativity in their adulthood [51]. The reason for this matter should be questioned. How much do teachers know their students? or how can teachers choose the students who have the creativity potential? If the teachers are expected to find answers for these questions, the system should also be regulated to serve for the aims of the teachers. So, where is the basis of the current system based on? Let's get ready to find out by going back in time.

The organizational processes, which see the creativity as a concrete aim, are very few. No matter thinking out of the box, being eager to take risk and being original are the words that are uttered frequently, it is open to discussion whether the system and decision-makers in the system truly want it. For example, the starting point of the current school-based education is based on the book of "The principles of Scientific Method", which is a book by mechanical engineer Frederick Taylor written in 1911 to

#### *Perspective Chapter: New Approaches to the Assessment of Domain-Specific Creativity DOI: http://dx.doi.org/10.5772/intechopen.102311*

increase the productivity in the workplace. The "Taylorism" approach, which was put forward in his book, still has its effect on the education system. The reason why this approach had so much impact on the education can result from the fact that it was supported by the father of the American psychologist Edward Thorndike. Taylorism claims that the individuals should be placed based on their talents and supports the idea that the scale of the system should be based on the speed. Because, the measurement of the speed is easier than determining the individual's talent on a field. Therefore, to assessment people's ability, they were looked into how much they were fast. As a result of this, standardization was focused not on the individualism and creativity [ 52 ]. The fact that how much this approach serves to meet the requirements of the human profile needed in the twenty first century is open to discussion.

 Individuals grown up as digital natives [ 53 ] and known as the twenty first century individual, can be defined as game age or application generation [ 54 ]. If we want to assess this age's native, we should design a scale appropriate for the conditions of this age. While assessing the creativity, we should get used to it. If not, as the digital immigrants, how can we guide to the digital natives who have a great potential to contribute their potentials to the universe?

 When the scales are checked in the worldwide, it is clearly seen that using paperpen-tests have been replaced with the web based tests. This change shows itself not only in the format of the tests but also in the process of the assessments (see [ 55 ]). For example, PISA, which is a test applied globally, was planned to be in the computer based environment and delayed to 2022 due to the global pandemic, but even so, it is seen that the test was revised according to the needs and the conditions of the period. The change in the application and assessment in PISA has also been seen in the question styles. According to the announcement published on the formal website,

#### **Figure 3.**

 *A question related to creativity in PISA.*

for the first time in the exam of PISA in 2022, students are expected to produce a visual artwork instead of giving a written answer or choosing one correct answer to measure the creative potentials of the students, The assessment includes open-ended tasks in which the answers do not have only one single answer but many of them [55]. This means that collecting answers from many students in the world and gives the hint of using computer-based (objective and automatic) assessment techniques. One example from the question of the PISA is shared below (**Figure 3**).

In addition to this, the tendency of preferring the subjective assessment approach to objective (and automatic) assessment is also observed in the field of creativity [44, 56–59]. This can be stemmed from the needs of the age. Furthermore, field experts should be open to progressive approaches to accelerate the growth of the proofs belonging to the assessment of the creativity [60].