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Agricultural And Food Sciences
Economics

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Manure recycling from urban livestock farms for closing the urban–rural nutrient loops

Rapid urbanization in developing countries is attracting urban livestock farming, which is associated with feed nutrient imports and manure nutrient accumulations. Here, we report on manure nutrients (N, P and K) production on urban livestock farms in Addis Ababa and Jimma in Ethiopia, and estimate manure recovery on these farms and its recycling in urban, peri-urban and rural crop farms. Estimates were based on data from farm surveys, statistics, literature and model calculations. Urban livestock farms had on average 13 tropical livestock units and positive N, P and K balances. Urban crop farms had negative N and K balances, and did not use manure from urban livestock farms. We estimated that 0.5–2.6 Gg N, 0.2–0.8 Gg P and 0.9–3.7 Gg K can be collected in manure from the 5,200 urban livestock farms in Addis Ababa year−1, and can be recycled in crop land. Two manure allocation strategies were explored: balanced P fertilization and balanced N fertilization. The former allowed greater precision in matching nutrient supply to demand, and was associated with greater manure transport and distribution, up to rural areas. Manure recycling has several benefits: synthetic fertilizer savings (75–300 million Ethiopian Birr year−1 for Addis Ababa), soil fertility replenishment, and reductions in water pollution. However, there are various cultural, educational and institutional barriers for manure recycling. We argue that more quantitative data and scenario analyses are needed for deliberative decision making and for exploring more sustainable development pathways of urban livestock farms, re-connected to crop farms via manure recycling.

Introduction
Global population is projected to reach 9-10 billion by 2050 (FAO 2017) and 68% of the population is projected to be urban (UN 2019). These changes are accompanied by fast growing food demand and dietary transitions in urban areas (Satterthwaite 2017). Most of the food for urban people is imported from rural areas (Jones et al. 2013), but significant amounts are provided also by urban and peri-urban farms (Roessler et al. 2016). Urban and peri-urban agriculture (UPA) contributes to the livelihood of urban dwellers, providing jobs, income and food security, especially in developing countries (Chihambakwe et al. 2018;Khumalo and Sibanda 2019;Mugisa et al. 2017). Thebo et al. (2014) reported that 11% of the global area of irrigated cropland was urban irrigated cropland, and 4.7% of global rain-fed cropland was urban. UPA farms mainly supply perishable vegetables and animal products to urban areas, due to the proximity to and high prices in urban markets (Djurfeldt 2015;Mugisa et al. 2017).
Significant amounts of nutrients are embedded in agricultural products that are transported from rural and peri-urban areas to urban areas (Lin et al. 2014;Zhu et al. 2017). This transfer contributes to nutrient accumulation in urban areas (Yu et al. 2017;Zhu et al. 2017) and to nutrient depletion in rural areas, especially when animal manure and human wastes from urban and peri-urban areas are not recycled back to rural areas (Goulding et al. 2008;Jones et al. 2013). The accumulation of manure and human waste in urban and peri-urban areas contributes to a range of unwanted human health and environmental effects (Herrero et al. 2018).
The 'circular economy' is a response to the inefficient use and management of resources in our current linear socio-economic systems (Michelini et al. 2017). It aims at more sustainable development through increased recycling. Alike, nutrient recovery from animal manure and human waste in urban and peri-urban areas and recycling in rural areas has direct environmental benefits (Rao et al. 2017;Neo 2010), and may replace synthetic fertilizers and thus reduce the costs, emissions and pollutions associated with the production and use of synthetic fertilizers (Buckwell and Nadeu 2016; Yu and Wu 2018). Also, recycled nutrients may reduce the dependency of countries on imports of fertilizers (Smits and Woltjer 2018), and are thought to have a vital role in achieving Sustainable Development Goals on food and nutrition security, clean water and sanitation, sustainable cities and life below water and on land (Zhu et al. 2017).
Although recycling of animal manures and household wastes has several advantages, the willingness and incentives for recycling manures and wastes back to crop land are still limited in developing countries (van Beek et al. 2016). For instance, less than 10% of the animal manures and crop residues available to smallholder farmers in Ethiopia were applied to soils (Nigussie et al. 2015), despite net nutrient depletion due to nutrient withdrawal with harvested crops, erosion and leaching, and limited applications of inorganic and organic fertilizers (van Beek et al. 2016). Some dung cakes are collected and used for building and cooking purposes, and some excrements deposited in animal confinements are partially recovered and applied to cropland, but most of the animal depositions are not recovered and recycled. There is lack of knowledge and information, and there are cultural barriers for recycling wastes. Moreover, governmental policies and regulations for manure collection and recycling are lacking (Komakech 2014;Tadesse et al. 2018) or incipient (Herrero et al. 2018) in developing countries. This leads to accumulation of nutrients in urban and peri-urban areas, and to pollution and eutrophication of rivers and lakes (Yu et al. 2017).
Tackling nutrient accumulation in urban livestock farms and nutrient depletion in rural crop farms simultaneously, through recycling of manures from urban areas in rural areas, is an opportunity and a necessity (Goulding et al. 2008). Recycling of agricultural wastes is in line with the FAO model for sustainable intensification of crop production ''Save and grow'' (Pergola et al. 2017), and represents an important contribution to the circular economy at regional level (Pergola et al. 2017). However, the amounts of nutrients in manure that can be recycled from urban farms to rural farms, and the associated fertilizer savings are currently not well known. The purpose of this study was threefold: (i) to estimate the amounts of manure nutrients that can be recycled from urban livestock farms to crop farms in urban, periurban and rural areas, (ii) to explore strategies for increased manure recycling, and (iii) to estimate fertilizer saving and reductions in nutrient pollutions in urban, peri-urban and rural areas, in Addis Ababa and Jimma in Ethiopia.

Study site and farm characteristics
Addis Ababa (Finfinnee) is the capital city of Ethiopia (Fig. S1); it had a total population of 3.4 million in 2015 and an annual population growth rate of 3.8% . Jimma is one of the secondary cities in Ethiopia and the commercial center of the Southwestern part of the country. It had a total population of 0.2 million in 2014 and an annual population growth rate of 3% (Abrha et al. 2015). The two cities differ in their bio-physical characteristics and agro-ecology. Addis Ababa has ten sub-cities; most of the UPA farms are found in five sub cities (Abdalla et al. 2011;Tadesse et al. 2019) and these were selected for this study. In Jimma, all eight urban woredas (districts) within the city were selected (Table S1). Rural woredas near the two cities were included (Table S1). Although the selected rural woredas are situated in the highlands and designated as models for the Agricultural Growth Program (AGP) woredas by the Ethiopian government, due to their potential for agricultural production (Eyasu and van Beek 2015;Tefera et al. 2012), the smallholder farms are not as productive as claimed (van Beek et al. 2016). Most UPA farms are mixed crop-livestock farms, but the relative contributions of crops, vegetables and livestock to farm income differs greatly. Farm income is much higher on farms with focus on livestock production and vegetables, than on farms with focus on crops (Tadesse et al. 2018). Most farms in the rural areas are also mixed crop-livestock farms (Table S1), but crop production is here the most important activity; livestock are kept for draught power supporting crop production and for family milk production (Mukai 2018).
Concept of nutrient cycling between UPA farms and rural farms Urban, peri-urban and rural areas differ in population density, built-up areas, infrastructures and key economic activities (Allen 2003;Opitz et al. 2015). Makita et al. (2010) distinguished a city center with urban, peri-urban and rural areas around the center (Fig. 1). The city center is dominated by business buildings, high income residential areas and houses with high fences, and basically has no agriculture. Urban areas are densely populated built-up areas, with locally intensive farming activities. Peri-urban areas are urban-rural transition areas, with a high population increase, mainly through migration, and with space for agriculture. Rural areas have low population increase and often little economic activities; most people depend on agriculture, especially in developing countries. The width of the city center and urban and periurban areas greatly differ between cities and tend to increase over time when cities grow. Large cities have diameters of 30-50 km, and often exploit peri-urban and rural resources for urban uses (Simon 2008). However, the ecological, economic and social functions of rural areas are of mutual importance for urban, peri-urban and rural areas (Allen 2003). City centers and urban, peri-urban and rural areas are often an asymmetrical, uneven and multi-dimensional continuum. There are no distinct lines separating the center from peri-urban, urban and rural settings, but often a slow zone of change exists (Lupindu 2017). For the purpose of our study, we distinguished three zones around the city centers of Addis Ababa and Jimma (Fig. 1). Addis Ababa had a city center of 527 km 2 with a diameter of roughly 26 km in 2017 (Terfa et al. 2019), and Jimma a city center of 100 km 2 with a diameter of roughly 11 km in 2017 (Abebe et al. 2019). Following the concept of Fig. 1 Conceptual configuration of the studied cities, with a city center, and an urban zone (a), a peri-urban zone (b) and a rural zone (c) around the city center. The radius of the city center was estimated at 13 km and the width of each zone at 10 km for Addis Ababa, and at 6 and 5 km for Jimma, respectively. After Mohamed and Worku (2019) and Abrha et al. (2015) Zhu et al. (2017) and the approaches of Makita et al. (2010) and Opitz et al. (2015), we assigned three zones (A, B and C) around the city centers; 10 km wide zones around the city center of Addis Ababa, and 5 km wide zones around the city center of Jimma. Zone A is considered the 'urban periphery' (intra-urban). Zone B is the peri-urban area (urban-rural interface), and zone C is the rural area.
In each zone we identified the type, number and size of the farms on the basis of data statistics, literature and farms survey. In total, there were 5200 UPA livestock farms in Addis Ababa (Tamiru and Amza 2017) and 500 in Jimma (according to the agriculture office of the city). Tabor (2013) studied the land use of Addis Ababa and its fringes using Landsat images of 1973Landsat images of , 1986Landsat images of , 2000 and 2011 and collected land use data in 2011 for ground truthing (Table 1). Zewdie et al. (2018) also assessed the land use of Addis Ababa in 2015 using Landsat images of 1984Landsat images of , 1995Landsat images of , 2003Landsat images of and 2014. The images were acquired during the dry seasons to better differentiate agricultural cropland from natural vegetation, and attained an accuracy level of 87% for land use classification (Zewdie et al. 2018). They estimated that the studied districts in Addis Ababa had an area of 122,100 ha, including 55,400 ha agricultural land (Admasu and Runnström 2017). Abrha et al. (2015) studied the land use of Jimma city using remote sensing and attained 84% accuracy level of land use classification. The studied districts in Jimma had an area of 12,000 ha, including 7,908 ha agricultural land.
We assumed that manure transport and recycling will occur in the following sequence. (i) Manure disposal is based on the N, P and K demand of the crops; the nutrient element that meets the demand per crop/field first, determines the manure application rate (see also ''Assessment of manure recycling'' Section).
(ii) The demand is satisfied (saturated) first in the zone where the manure has been produced, to minimize transport distances (Rodionov and Nakata 2011). (iii) Excess manure from zone A is transported to periurban crop farms in zone B, until also here all remaining demand has been satisfied. (iv) Remaining Table 1 Land use and main crops in the studied districts of urban (A), peri-urban (B) and rural (C) areas of Addis Ababa and Jimma. For comparison, also data for a further rural area zone is presented. Note that total areas do match with areas of full circles. Sources: (Tadesse et al. 2019(Tadesse et al. , 2018Zewdie et al. 2018;Abrha et al. 2015) Land use types (ha) Distances of zones from city centers (km) Total Note that the three zones defined for the envisaged recycling are indicated in italic surpluses from zone A (and possibly from zone B) are then transported to rural crop farms in zone C.

Data collection
In 2014, a total of 299 UPA farms (Addis Ababa: 175, Jimma: 124) were visited and examined; the diversity among the farms and the N, P and K balances have been reported by Tadesse et al. (2018). In 2016, a random selection of 40 livestock farms (Addis Ababa: 20, Jimma: 20) and 38 crop farms (Addis Ababa: 19, Jimma: 19) out of the 299 UPA farms were revisited and examined in more detail. In 2016 we also visited 164 farms in rural areas: 72 from five rural woredas (districts) linked to Addis Ababa, and 92 from five rural woredas linked to Jimma. Farmers were interviewed face-to-face and data was collected using the standardized questionnaire of MonQIt (Monitoring for Quality Improvement toolbox) (van Beek et al. 2010(van Beek et al. , 2016. MonQIt is a toolbox for monitoring and evaluating the management and performance of smallholder farms (www.monqit.com; Smaling et al. 2013;Vlaming 2012). The information about farm management and farm activities is obtained from the farmer using the MonQIt questionnaire (see the supplementary information). The following data were collected: farm characteristics, crop and animal husbandry practices, nutrient management (inputs via fertilizer, manure, crop residues; outputs via harvested crops and animal products), sales, and input and output prices. The data were entered into the MonQIt model, which also contains a data base with information on nutrient contents of products, soil data and conversion factors. The MonQIt model produces the following performance indicators: NPK balances at plot, activity and farm levels, and gross margin and net farm income (van Beek et al. 2016).
The 78 UPA farms and 164 farms in the rural area were visited twice in 2016. In the first round, farm characteristics and nutrient input flows (purchased mineral fertilizers, organic fertilizers, seeds, feed and concentrates) were quantified at the beginning of the cropping season. In the second round, output flows (harvested crop products, residues and animal products, and sales) leaving the farm were quantified after harvest. Data on nutrient (N, P and K) contents of harvested crop products, crop residues, milk and manure were obtained from literature (Alvarez et al. 2014;Negash et al. 2017;Wang et al. 2016) and included in the MonQIt database.

Quantification of farm level nutrient balances
The N, P and K input and output flows were estimated as follows (Bekunda and Manzi 2003;Nigussie et al. 2015): where, F is the flow of N, P or K (kg ha -1 yr -1 ); n is the number of nutrient inputs and outputs in a year; Q is the quantity of inputs or outputs (kg ha -1 yr -1 ); D is the dry matter content of inputs or outputs (%); C is N, P or K content of inputs or outputs (%). Nutrient (N, P and K) balances were calculated per farm as the difference between total input and total output flows of N, P and K, annually (Eq. 2; unit: kg ha -1 yr -1 ): where, NB is nutrient balance; IN1 and IN2 are farmer managed inputs (fertilizer, compost, concentrate, organic feed), IN3 and IN4 are atmospheric deposition and biological N fixation, respectively, and OUT1 and OUT2 are outputs in harvested crop, residue, milk and manure and OUT3, OUT4 and OUT5 are losses via leaching, gaseous emissions and erosion, respectively (van Beek et al. 2016).
Net farm income (NFI) was derived from gross margin (GM) and the total fixed costs (FIXCOST) of the farm (Eq. 3; unit: Ethiopian Birr yr -1 ).
where, GM is calculated as the difference between the gross value of all outputs and the variable costs of all inputs of the farm, over a year. FIXCOST is the total fixed costs of the farm (e.g. depreciation costs of barns, tools, and rent of the land).

Assessment of manure recycling
In total, there were about 5200 urban livestock farms in Addis Ababa (Tamiru and Amza 2017) and 500 LS farms in Jimma (according to the information obtained from the urban agriculture office of the city) in 2016.
In general, these farms had dairy cattle, on average 13.5 tropical livestock units (TLU) in Addis Ababa and 11.5 TLU in Jimma ( Estimates of manure production per animal, in kg N, P and K per tropical livestock unit (TLU; 1 TLU has a live weight of 250 kg), were based on literature data and farm surveys. Snijders et al. (2009) arrived at average manure production of 9.43 kg TLU -1 day -1 (fresh weight), with a dry matter content of 35%. This translates to an annual manure production of 25 kg N, 6.7 kg P, and 33 kg K per TLU, when assuming mean contents of 2.1% N, 0.56% P, and 2.75% K (Negash et al. 2017). Sheldrick et al. (2003) estimated the global manure N, P and K production based on linear relationships between slaughtered weight and N, P and K production. Using their model, we arrive at an annual manure production of 28 kg N, 5.6 kg P and 25 kg K per TLU (with a slaughtered weight for cattle of 140 kg TLU -1 ). Snijders et al. (2009) reviewed variations in cattle manure composition and management practices in East Africa. Variations in nutrient content of manures were large and associated with differences in (i) feed rations, (ii) collection, storage and processing of excreta, and (iii) additions of bedding materials. Further, excretion rates were related to milk production; they indicated that milk production ranged from \ 500 kg to more than 4500 cow -1 year -1 , and that manure N production ranged from 30 to 90 kg cow -1 year -1 . Our survey data also revealed large variability in cattle productivity and management between livestock farms; based on these observations we decided to use three sets of manure N, P, and K production coefficients: 25, 5 and 25 kg cow -1 year -1 for cows producing \ 500 kg milk year -1 ; 50, 10 and 50 kg cow -1 year -1 for cows producing 500 to 3000 kg milk year -1 , and 75, 15 and 75 kg cow -1 year -1 for cows producing [ 3000 kg milk year -1 , respectively. Hence, N: P: K ratios (5:1:5) in excreta were kept constant. Further, we assumed that young stock (calves and heifers) produced on average 25% of the amounts of adult cows.
Manure recovery fractions (RF) are defined as the fractions of N, P and K excreted in manures that are recovered for recycling. These fractions mainly depend on the manure collection efficiency and nutrient losses during storage. Snijders et al. (2009) estimated nutrient losses for different manure collection and storage systems for East African conditions. Losses were highest for open kraal/boma (30-100% for urine N; 10-50% for faeces N, 20-30% for faeces P and 10-80% for K) and least for leak tight and covered slurry pits (20-40% for urine N; 5-10% for faeces N, 3-10% for faeces P and 5-15% for K). Liu et al. (2017) estimated recovery fractions of 0.3-0.7 for N and 0.5 to 0.95% for P as function of housing systems and manure management. A recovery fraction of 0.3 indicates poor manure management whereas 0.95 indicates best manure management (Liu et al. 2017). Rufino et al. (2007) reported a recovery of 0.4 for N in manure stored in open piles in Kenya. Bicudo (2009) and Sutton (1994) reported recovery fractions of 0.4-0.6 for N, 0.6-0.8 for P and 0.5-0.7 for K in manures from open manure storages. Most livestock farms in Addis Ababa and Jimma have no leak-tight stables and manure storage systems (Tadesse et al. 2019), but recovery fractions have not been quantified. Based on this data, we used two sets of recovery fractions for N, P and K in manure: 0.3-0.5 for N, 0.6-0.8 for P, and 0.5-0.7 for K. The recyclable amounts of N, P and K in manure were estimated from the manure N, P and K production per zone and the recovery fractions.
Total N, P and K demands were estimated from crop-specific N, P and K demands (kg ha -1 year -1 ) per zone, crop type-specific nutrient losses (van Beek et al. 2016), and surface areas of the crops per zone, using MonQIt (van Beek et al. 2016). The fraction of the N, P and K demand covered through recycling per zone depended on the specific crop demands, the manure composition and manure application strategy. Two variants of manure application strategies were explored: (i) balanced P application, i.e., manure application defined by P demand (and assuming top-up N and/or K fertilization through other nutrient sources), and (ii) balanced N fertilization, i.e., manure application defined by N demand (and thus accepting over fertilization of P and/or K).

Data analysis
Data obtained from MonQIt model on nutrient flows, balances, farm characteristics (e.g. TLU and farm size) and economic performance indicators (e.g. net farm income) per farm were subjected to ANOVA to test for significant differences in nutrient flows and balances, net farm income and farm characteristics among urban, peri-urban and rural farms. Mean values of nutrient flows and balances were compared using LSD value at 5%. For all data analysis, we used SPSS statistical software version 23.

Farm characteristics
Farms were relatively small, with on average 0.6-2.6 ha of land per household (Table 2). Households had on average 7 family members, both in the urban and rural areas. Urban livestock farms had on average 11.5-13.5 tropical livestock units (TLU) per farm, peri-urban farms 5-6 and rural farms 6-7 TLU. Urban crop farms also had 4-7 TLU per farm; basically, these were mixed crop-livestock farms. Livestock density was high; in Addis Ababa urban livestock farms had [ 22 TLU per ha of land and in Jimma 16 TLU per ha. Even the rural farms in both Addis Ababa and Jimma had a livestock density of 2-3 TLU per ha.
Farm characteristics were slightly different between the farms in Addis Ababa and Jimma regions (Table 2). UPA farms in Addis Ababa had more livestock, less crop area and less crops per farm than the UPA farms in Jimma. As a result, UPA farms in Addis Ababa had on average a higher net farm income than the UPA farms in Jimma (Table 2).

Farm level nutrient flows and balances
Nutrient balances were significantly different (P \ 0.05) among urban livestock farms and crop farms in urban, peri-urban and rural areas (Table 3). Crop farms had negative N and K balances, and positive P balances, in all three zones. This indicates soil N and K depletion and soil P accumulation. Positive P balances on crop were related to the use of subsidized di-ammonium phosphate (DAP) fertilizers. Note that the N, P and K output of crop farms include losses via leaching, erosion and denitrification. As a result, the estimated N/P ratios of the output ranged from 10 to as high as 40, and K/P ratios of the output ranged from 2 to 6.
The remainder of the ingested feed N, P and K is excreted in urine and faeces. Note that the output of N, P and K of livestock farms were not corrected for losses from manure storage systems in Table 3. The N, P and K output of urban livestock farms were higher in Addis Ababa than in Jimma, mainly because of the higher animal productivity and higher crop yields in Addis Ababa. We infer that significant amounts of surplus manures from urban livestock farms can be recycled to nearby crop farms.

Potentials of manure nutrient recycling
We estimated that 0.5-2.6 Gg N, 0.2-0.8 Gg P and 0.9-3.7 Gg K may be collected in manure from urban livestock farms in Addis Ababa and recycled to crop land per year (Table 4). The ranges reflect the uncertainty (roughly a factor 3) in the estimates due to the variation in N, P and K excretion coefficients, and in N, P and K recovery fractions (variants 1 to 6). The estimated quantities were more than one order of magnitude lower for Jimma: 0.04-0.22 Gg N, 0.02-0.07 Gg P and 0.07-0.30 Gg K (Table 4), because the size and the number of urban livestock farms were less in Jimma than in Addis Ababa. Total N, P and K demands by crop-based farms were much smaller in the urban area than in the periurban and rural areas ( Table 5). The mean N/P ratio of the demand was 7 and the mean K/P ratio was 6, with little differences between Addis Ababa and Jimma, and between zones A, B and C. Figure 2 presents the supply of manure N, P, and K from urban livestock farms relative to the demand of N, P and K by cropbased farms in the three zones A, B and C, for six variants and two application strategies. The N/P ratios in the available manure ranged from 2.5 to 3.1, and the K/P ratio from 4.2 to 4.4 (Table 4), and were much lower than the N/P and K/P ratios of the demand by the crop farms. As a result, the N and K demand by the crop was not matched by N and K supply by manure in the urban area, if manure application rates were determined by balanced P fertilization (Fig. 2a). The same applies for the peri-urban and rural areas; in Addis Ababa there were three variants (4, 5 and 6) that  supplied sufficient manure P from urban livestock farms to match the P demand in the peri-urban area. Conversely, there is a large oversupply of P and K, if the N demand by the crop determines the manure application rates (Fig. 2b). Note, there is also oversupply of P and K in the peri-urban area in variant 6 (and partly in variant 5), and there is no manure transport to the rural areas in the balanced N fertilization strategy. Rather similar results were obtained for Jimma. For balanced P fertilization, 5 of the 6 variants supplied sufficient manure P to meet the demand for P in the urban area (not shown). Variant 6 supplied also sufficient manure P to meet the demand for P in the peri-urban area, and also 40% of the P demand in the rural area, but demands for N and K were not matched in urban, peri-urban and rural areas. For balanced N fertilization, 3 of the 6 variants (4, 5 and 6) supplied sufficient manure N to meet the demand for N in the urban area (and up to 50% in the peri-urban area), but at the cost of a large oversupply of P and K in the urban area (up to 220%).

Recycling benefits
Through the envisaged recycling, the total amounts of N, P and K recovered in manure from urban livestock farms in Addis Ababa is equivalent to 1-4 Gg diammonium phosphate (DAP), 0.7-3.9 Gg urea, and 2.3-9.6 Gg potassium chloride (KCl) fertilizers.  year. These amounts are equivalent to savings of 75-300 and 6-24 million Ethiopian Birr (ETB), respectively (Table 6). In addition, some 5-50 Gg organic carbon in Addis Ababa and 0.6-6 Gg organic C in Jimma will be returned with the manure to soil, which is equivalent to roughly 1 to 2 Mg ha -1 (assuming C/N ratios that range from 10 to 20; Snijders et al. (2009)). Further, an unknown amount of basic cations and secondary nutrients (Ca, Mg, S) and micro nutrients (e.g. Zn, Cu) will be returned with the manure to cropland, which are essential for growth and development of crops, animals and humans (Berkhout et al. 2017).
There are additional environmental benefits, because much less manure nutrients (e.g., 0.5-2.6 Gg N and 0.2-0.8 Gg P in Addis Ababa) from urban livestock farms pollute the urban environments. Demand matched (%) (b) Fig. 2 Supply of N, P and K from urban livestock manure relative to demand of N, P and K by crops in urban farms (zone A), periurban farms (zone B) and rural farms (zone C), as function of 6 variants (combinations of excretion coefficients and recovery fraction; see Table 4) in Addis Ababa. Upper graph (a): demand for phosphorus determines manure application rates (balanced P fertilization), first in urban areas, then peri-urban and finally rural areas. Lower graph (b): demand for nitrogen determines manure application rates (balanced N fertilization) Table 6 Amounts of N, P and K recycled, and cost of synthetic fertilizers saved through recycling of manure from urban livestock farms to crop farms in urban, peri-urban and rural areas of Addis Ababa and Jimma. Ranges reflect the uncertainties in the amounts of manure recycled (Table 4) Cities Manure nutrients recycled (Gg yr -1 ) Fertilizer equivalents saved (Gg yr -1 ) Savings (million ETB yr -1 ) Addis Ababa 0.5-2.6 0.2-0.8 0.9-3.7 0.7-3.9 1-4 2.3-9.6 75-300

Discussion
Urban agriculture and nutrient balances Urban livestock farms are important for providing nutritious food, jobs and income, but the environmental costs of manure nutrient accumulation and soil and water pollution, and the risks of pathogen transfer and zoonosis are relatively high (Steinfield et al. 2010;Herrero et al. 2018;Khumalo and Sibanda 2019). Both, its importance and the concerns are increasing, because urbanization is increasing, and associated with changes toward more animal-sourced diets. Traditionally, animals are and have been kept for different reasons and animals often have multiple functions. As a consequence, there is a huge diversity in urban livestock farming, which roughly falls in three main categories, i.e. (i) back yard/subsistence/ small mixed farms, (ii) intensive/industrial/large animal feeding operations, and (iii) company/leisure/care pets and horses (Robinson et al. 2011). Urban livestock farms in Ethiopia mainly fall in the first category, but the livestock sector is rapidly changing and in transition. There were about 5200 livestock farms with on average 0.6 ha of land and 13.5 TLU per farm in the urban zone of Addis Ababa. These farms produced 560-1690 kg N, 113-338 kg P and 560-1690 kg K ha -1 yr -1 in manure, which is much more than can be taken up by most crops, as follows also from the relatively large N, P and K surpluses of urban livestock farms (Table 3). Even when the recovery fractions of 30-50% for N, 60-80% for P and 50-70% for K were taken into account, amounts of manure nutrients available for the limited area of crop land on the farm remain much higher than what can be taken up by most crops.
Though the urban livestock farms were much wealthier than other farms in the urban, peri-urban and rural areas (Table 2), these farms were still small compared to many urban and peri-urban livestock farms in emerging markets economies such as China (Steinfield et al. 2010;Wei et al. 2016). These factory farms or confined animal feeding operations often have thousands of pigs or hundreds of dairy cows, and also have little or no land for manure disposal, and thereby create serious air, water and soil pollution (Bai et al. 2018). However, our data suggest that 5200 small livestock farms together may create rather similar manure nutrient surpluses in Addis Ababa as the few hundred large pig farms around Beijing do (Wei et al. 2016). The difference is that the small livestock farms in Addis Ababa are diffusively spread and perhaps less visible.
There were nearly 5 times less crop farms (1100) than livestock farms in the urban zone (Table 5). These crop farms had on average 1.7 ha of land and 4 TLU per farm (Table 2), and had negative N and K balances and slightly positive P balances (Table 3), despite 2 TLU ha -1 . Likely, most feed was produced on-farm and little imported, because mixed farms with relatively large area of crop land invest relatively little in animal production (Tadesse et al. 2019); the N, P and K excretion coefficients applicable for the livestock in these mixed crop farms were likely at the lower end of the range used in our calculations (Table 4). The negative N and K balances indicate that there was little exchange of manure between livestock farms and crop farms in the urban zone; crop farms with nutrient depletion co-existed next to livestock farms with large manure nutrient surpluses. Though crop farmers know the value of manure for crop production, there is need for education and extension services that encourage exchange and interaction between farmers for increasing adoption of good manure management practices (Ndambi et al. 2019). Lack of knowledge and clear guidelines were also identified as main reasons for poor nutrient management practices of urban gardeners in The Netherlands (Wielemaker et al. 2019).

Manure recycling between urban livestock farms and crop farms
Manure is a heterogeneous product and the composition is not known in practice, generally. The heterogeneity of cattle manure is related to variations in the composition of the diet, in nutrient retention in animal production, and in losses during storage. Snijders et al. (2009) reviewed literature on the composition of cattle manure in east Africa, and based on their review we derived the following composition (mean ± standard deviation; n [ 20): 1.7 ± 0.7% N; 0.5 ± 0.3% P and 1.8 ± 1.3% K, with a N/P ratio of 4.1 ± 1.7 and a K/P ratio of 4.6 ± 2.7 (medians were 3.8 and 3.6, respectively). The ratios for manures estimated in our study (N/P = 2.5-3.1 and K/P = 4.2-4.4; derived from Table 4) fall in the lower half of the indicated ranges, suggesting that our manures were relatively N and K poor and/or P rich .
In total 0.5-2.6 Gg N, 0.2-0.8 Gg P and 0.9-3.7 Gg K can be recovered in manure from urban livestock farms in Addis Ababa (Table 4), and utilized in crop farms in urban and peri-urban zones, following the 'balanced P fertilization' or 'balanced N fertilization' strategies (Fig. 2). Balanced P fertilization assumes that the crop P demand is met through manure, and that synthetic N and K fertilizers (or other nutrient sources) should be used to top up, to match the remaining N and K demands. Balanced N fertilization assumes that the crop N demand is met through manure and that P and K demands are also met. Balanced N fertilization led to a relative oversupply of P and K (Fig. 2), and to less manure transport and distribution than balanced P fertilization. Balanced P fertilization is a better strategy from nutrient management perspective, as it allows for greater precision through matching supply and demand for all three nutrients.
We made no corrections for the fertilizer N effectiveness value of the manure (defined as the fraction of manure N that has similar effect as common synthetic N fertilizer). This value may range from 0.3 to 1.0 depending on the composition and application rate of the manure, and the duration of manure use and accounting for period (Schröder et al. 2007;Zhang et al. 2020). Correcting for the fertilizer N effectiveness value of the manure would thus have increased the manure application rate in the balanced N fertilization strategy, and would have led to larger P and K over fertilization. The P and K effectiveness values of manure are generally set at 1 (equal to high-grade synthetic P and K fertilizers; Oenema et al. (2012)).
We made no corrections for manure application on crop land of livestock farms, and for manure production by livestock on crop farms. In the urban area the demand for P by the crop in livestock farms was similar to the supply of P in manure from the livestock in crop farms, and thus cancel each other more or less out. However, by neglecting the nutrient demand by the crop of livestock farms, and the nutrient supply in the manure produced in crop farms, we overestimated the need for manure transport and redistribution. Hence, such corrections should be made in future studies.
The estimated amounts of manure that may be recovered on urban livestock farms in Addis Ababa are rather similar to the amounts that may be recovered from urban livestock farms in other large cities in East Africa. De Zeeuw et al. (2011) reported that 2.2 Gg N, 2.2 Gg P and 3.7 Gg K could be recovered from manure and waste in Nairobi. Komakech (2014) reported rather similar numbers for urban cattle manure in Kampala, and estimated also significant synthetic fertilizer savings. Though significant, the amounts of N and P produced in cattle manure in urban farms are about one order of magnitude lower than the total N and P excretion by the human population in Addis Ababa. When assuming a mean protein N intake of 3 kg and a P intake of 0.6 kg capita -1 , the annual total N and P in human waste production will be about 10 Gg N and 2 Gg P. However, the recovery and recycling of N, P and K from human wastes are complicated, although there is an increasing need to do so (Hodson et al. 2012;Guerrero et al. 2013).

Recycling benefits and barriers
Urban manure recovery and recycling may have energy, economic and environmental benefits, the 3Es aspects (Rodionov and Nakata (2011). Estimated fertilizer saving for Addis Ababa were in the range of 75 to 300 million Ethiopian Birr (Table 6). Additional benefits relate to replenishing nutrients in crop land and to improving soil quality and nutrient balances (McConville et al. 2017;Szogi et al. 2015). More difficult to-account-for benefits include reductions in the pollution of soil, surface waters and groundwater, and in the threat of food safety, human health and biodiversity loss (Wen et al. 2017).
Though beneficial, manure recycling is not practiced in the study urban regions at present as shown by Tadesse et al. (2018) who reported that landless cLS (commercial livestock) farms with surplus LSM (livestock manure), simply dumped large quantity of LSM into ditches and farm surroundings or left it unmanaged on the ground. Similarly, in Kampala city, many livestock farms discard and dumped LSM into drainage channels, where it is carried off by running water, because there is no comprehensive national urban policy and institutional framework to regulate the use of solid waste (Komakech 2014). In addition to losses of nutrient resources, unmanaged wastes may contribute to pollution of the urban environment (Wen et al. 2017). Where waste is managed, Nigussie et al. (2015) reported that large quantity of waste was dumped in landfills in these urban areas. Landfilling of organic wastes contribute to pollution issues related to landfill leachate and gas as well as the loss of valuable nutrients and organic matter. There are many factors leading to these outcomes as Marshall and Farahbakhsh (2013) citing cultural, socioeconomic, governance and institutional aspects limiting solid waste management in many developing countries. There are currently no incentives or policies to encourage the recycling of LSM from urban farms to crop farms in the study urban regions. Guerrero et al. (2013) and Komakech (2014) reported the absence of satisfactory policies or mere presence of weak policies and regulations as the critical causes for the failed solid waste management systems in low-income countries. Shortage of land and lack of tenure rights in urban LS farms (Table 2) for manure use or disposal, lack of appropriate institutions and poor infrastructures in the study urban regions are also barriers for LSM recycling. Guerrero et al. (2013) stated as success of recycling depends on the efficiency of infrastructures. Improving animal housing and LSM storage conditions, may reduce nutrient losses and increase the nutrient recovery and thus can maximize the potential of recycling LSM nutrients.
Further, the possible presence of pathogens, pharmaceutical compounds and veterinary products such as hormones and antibiotics in manure are sometimes seen as additional barriers for recycling (Manyi-Loh et al. 2016;Hou et al. 2017;Martens and Bohm 2009). These barriers could be addressed through anaerobic digestion and/or composting (Cliver 2009;Manyi-Loh et al. 2016). Evidently, recycling manure from urban to peri-urban and rural areas requires awareness raising, guidelines and protocols, and appropriate institutions, incentives and advisory services (Mulugeta 2013). In the end, it requires also careful monitoring and control. Thus, policymakers and stakeholders should promote manure reuse through incentives, technologies and appropriate strategies and policies, to improve nutrient use and reduce the pollution of the environment (Herrero et al. 2018).

Limitations and uncertainties of our study
Coupling livestock production to crop production via direct exchanges of animal feed and animal manure is key for increasing the sustainability of food production systems. In our study, we focused on the recycling of manure from urban livestock farms to crop farms only. Though livestock farms and crop farms were visited twice, there is as yet little accurate quantitative information about animal feed origin and rations and animal productivity. Hence, we have no accurate information about N, P and K excretion rates and N, P and K contents of recovered manures. We addressed this uncertainty through using ranges (Table 4). Uncertainties arise also from the uncertainties in crop areas and number of farms and animals. Further, the N, P and K demand of crops were derived from MonQIt, which is a static model and does not account for seasonal variations in N, P and K demand and supply, (e.g., Krause and Rotter (2018)).
The livestock sector responds to changes in markets and the availability of new technology, which are influenced subsequently by urbanization and globalization. Though livestock farms in the urban zones of Addis Ababa and Jimma are still relatively small compared to farms in for example east Asia, it cannot be excluded that rapid changes may occur. This calls for more dynamic approaches including considerations of long-term effects of manure and fertilizer applications on soil fertility level and the response of crops to these changes (Zoboli et al. 2016). Further insights may be accomplished through scenario analyses and measurements of feed, manures and soil.

Conclusions
The [ 5000 livestock farms in the urban zones of Addis Ababa and the 500 livestock farms in Jimma produce more N, P and K in manure than the [ 1000 crop farms in the urban zones of these cities demand for. Yet, crop farms in the urban area have negative N and K balances (but positive P balances because of subsidized fertilizers) and use little or no manure from livestock farms. We estimated how much N, P and K in manure may be recovered in urban livestock farms, and how much may be used in urban, peri-urban and rural crop farms.
A balanced P fertilization strategy was preferred to a balanced N fertilization strategy for estimating the allocation of manure in crop farms. The former will require a top-up with N and K from other nutrient sources, while the latter will lead to over fertilization with P and K, especially on the long term.
Recycling manure from urban livestock farms to crop farms has several benefits, including fertilizer savings, nutrient replenishment in crop land, restoring nutrient balances, and minimizing soil, air and water pollution. However, lack of knowledge, advisory services, and policy incentives, and cultural and institutional barriers appear to prevent cashing in these benefits. There is also a significant uncertainty in the estimated benefits, due to lack of accurate data. Our study may help policy makers to prioritize and define research and policies related to re-connecting animal production and crop production systems through manure recycling in the urban environment. Further studies should focus on collecting quantitative data related to improving the performances of animal and crop production systems in the urban area, and should explore more sustainable development pathways.


provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2020.jsonl.gz.gz

text:
Quantitative Analysis of the Sustainable Development Capacity of Regional Water Resources ： A case study of Sichuan Province

. Limited water resources have become a serious problem in recent decades. Based on previous research results, this article develops an index system to evaluate sustainable water resource development that includes a water resource condition system, a water resource development and utilization system, a water resource protection and management system, and a socio-economic system. A measurement model is then constructed based on a principal component analysis (PCA) -entropy weights-weighted average method to optimize the evaluation index system for dimensionality reduction, to assign weights to the principal component factors, and allow for a comprehensive evaluation of water resource sustainability. The measurement model is applied to an empirical analysis of sustainable water resource development in Sichuan Province from 2008 to 2017, from which it is found that coordinated sustainable regional water resource and social economic development can be achieved through rational exploitation, efficient utilization, and environmental water pollution control. This research could provide a reference for regional sustainable development of water resources and policy developments.

Introduction
Water is basic to the earth's survival and essential for socio-economic development. With the intensification of human activities, increasing populations, and rapid urbanization, global water shortages have been increasing exponentially, which was recognized in the 1992 Dublin declaration on Water and Sustainable Development resulting from the international conference on water and the environment (ICWE) [1]. At present, due to poor management, a series of problems such as low water use efficiency, serious water pollution, and deterioration of ecological environment have seriously affected the function of water resources, which further challenges the development and utilization of water resources. Therefore, we must thoroughly study the problems faced by regional water resources and explore efficient methods and principles to achieve sustainable and coordinated development of regional water resources and social economy.

Literature review
For the first time, the "Common Future" published by the World Commission on Environment and Development defined sustainable development; development that meets the needs of the present generation without compromising the ability of future generations to meet their needs [2]; with sustainable water resource development being as follows; in a certain spatial range, the water resource utilization that can meet the needs of the people of today, and does not pose a hazard to future generations in meeting their needs. In recent years, extensive and indepth research has been conducted on sustainable water resources from several different perspectives.
There have been three main foci in water resource sustainable research, one of which has been the ecological footprint associated with water resources. Rees and Wackernagel first proposed the ecological footprint model and then continued to improve it, thereby providing new methods for the quantitative analysis of sustainable water resources [3][4][5]. Zhang et al. developed an improved water ecological footprint model to measure water quantity and water quality supply and demand, which overcame the drawbacks in the traditional freshwater ecological footprint model [6].
Another research perspective has been based on the water footprint. Hoekstra was the first to propose the water footprint concept, which was defined as the amount of water needed to produce the products and services consumed by certain populations over time, and which provided a comprehensive and true accounting of the water needed to sustain the human consumption of products and services [7]. Using the Drivers, Pressures, State, Impact, Response model of intervention (DPSIR), Yu and Han built a water footprint index system for a time-space analysis of sustainable water resource use, which overcame the limitations associated with current year analyses [8].
A further research perspective for sustainable water use has been based on evaluation models. Zhou  shortcomings of traditional indicator empowerment [9][10].
Therefore, there have been significant research achievements in sustainable water resource development. However, there remain flaws and shortcomings in these previous research methods and perspectives. For example, the ecological footprint and water footprint methods did not include social and economic development factors in the measurement systems, did not establish a corresponding model evaluation index system, and did not quantitatively assess sustainable water resource development. While the DPSIR method, which is based on an evaluation model, comprehensively considers economic, social, environmental, and policy factors, it is not suitable for regional sustainable development research as it only represents a traditional "responsive" concept to environmental protection. Further, most previous research has been focused only on water resource use. Given the deficiencies in previous studies, this article comprehensively considers the economic and social system, and innovatively builds a more comprehensive evaluation index system from the water resource condition system, the water resource development and utilization system, the water resource protection and management system, and the socio-economic system. Finally a water resource sustainability measurement model based on a principal component-entropy weightweighted average method is proposed to make a quantitative analysis of the sustainable development of water resources and tested in an empirical analysis of Sichuan Province.

Indicator construction
The determination of a sustainable water resource development level requires a comprehensive assessment of existing water resource conditions, water resource development and utilization efficiency, and protection and management efficiency, all of which affect sustainable socio-economic system development and the realization of a harmonious coexistence of the ecology, the economy, and the society. Under the principles of scientificity, comprehensiveness, salience, operability, and hierarchy, and based on previous research, the sustainable water resource development indicator system in this paper is divided into three layers: the target layer, the criterion layer, and the indicator layer. The target layer is the target required for sustainable water resource development. The criterion layer is made up of the water resource condition system, the water resource development and utilization system, the water resources protection and management system, and the socioeconomic system, for which there are 31 specific indicators, as shown in Table 1. Total water resources(G11) Sum of surface water and non-repetitive groundwater [8,9,10] Per capita water resources(G12) Resources total water resources/ population, per capita water resources [10,11,13] Surface water resources(G13) Dynamic precipitation of surface water through atmospheric precipitation [11,13] Groundwater resources(G14) Shallow groundwater volume directly linked to surface water [10,11,13] Annual precipitation(G15) Characterizes the source of precipitation and climate wetness [11,13] Water module(G16) Total water resource/area, water resource per unit area [8,11,13] Water production coefficient(G17) (total water resources/ area)/annual precipitation, precipitation conversion rate [8,11,13] Water Resource Development and Utilizati-on System (G2) Water Resource Development Utilization(G21) Total regional water supply/total water resources [8,9] Surface water resource utilization(G14) Groundwater supply/groundwater [8,10] Groundwater extraction rate(G15) Groundwater supply/groundwater [8,10] Industrial water quota(G24) Industrial water consumption/industrial GDP, reflecting industrial water use efficiency [11,8] Urban per capita daily water consumption (G25) Characterizing the domestic water use efficiency of urban residents [8,14] Unit crop area water consumption (G26) Total agricultural water use / Total crop acreage [8,14] Ecological water use rate(G27) Eco-environmental water consumption/total water consumption, co-environmental water use [8,10,14] Million GDP water use rate(G28) The amount of water consumed per million yuan produced in that year [8,10,14] Reuse rate of water resources(G29) Repeat amount of water / total amount of water [8,10] , Represents the protection of water resources along with the development of industry [9,10,14] Industrial wastewater treatment capacity(G32) Represents the amount of wastewater treated daily [11,13] Sewage discharge(G33) Represents the protection of water resources in people's life [9,10,14] Sewage treatment rate(G34) Sewage treatment volume/sewage discharge [10,12,14] Environmental pollution control investment accounts for the proportion of GDP(G35) Represents the intensity of water environmental pollution control [10,12,13] Total water conservation(G36) Represents conservation and protection of water resources [11,12,13] Water-saving irrigation proportion(G37) Water-saving irrigation area/total irrigation area [8,11,13] Forest cover rate(G38) Forest area/land area [9,11,13]

Principal Component Analysis
The essence of principal component analysis is to describe the original data information using less dimensions while maintaining as much of the original information as possible. Using dimensionality reduction, a few dominant and unrelated principal components are constructed to avoid problem correlation difficulties in the index data [15]. It is assumed that there are indicators for the principal component analysis and n evaluation objects, thereby constituting an initial matrix of m*n, of which the − th index of the − th evaluation object is set to . The calculation steps are as follows: (1) Raw data standardization: After normalizing the original data, a standardized matrix is determined, where ̅ is the average value of the − th indicator sample, and is the standard deviation of the − th indicator sample; (2) Calculate the correlation coefficient matrix: R = ( ) × ; = λ (2) (6) Finally, this method will calculate each principal component score.
represents the principal component index formed by the linear combination of the original variables.

Entropy Method
The entropy weight method is an objective weighting method that eliminates the influence of subjective factors, for which a judgment matrix is built using the index values for each evaluation object. After the matrix is normalized, the index entropy is calculated based on the entropy definition, after which the entropy weight of each principal component is calculated. As entropy is a measure of the degree of disorder in a system, the smaller the indicator's information entropy, the greater the information provided, and the greater the role it plays in the overall evaluation. The specific calculation steps for the entropy weight are as follows: (1) Build an index judgment matrix. There are n measurement objects and p principal component factors. Build the standardized matrix R = ( ) × ( = 1,2,3, ⋯ , m) on the score for each principal component.
(2) Calculate the entropy and entropy weight of each principal component. By definition, the entropy and the entropy weight of the j-th principal component factor are: The original expression for was = ∑ ; however, when = 0, ln is meaningless; therefore, the original formula was revised to formula (4).

Weighted average method
The weighted average method objectively evaluates the comprehensive evaluation value of the measured object .The comprehensive evaluation value is summed by multiplying the principal component factor weight and the principal component factor score.
is the comprehensive evaluation value for the measured object, is the main component factor weight, is the main component factor score, and ∑ = 1, ( = 1,2,3, ⋯ , p).

Case study 4.1 Study area and data source
The Sichuan Basin is located between 27 -34 o N and 100 -108 o E and has abundant water resources. The main rivers are the Yangtze River and the Jinsha River, which together have more than 1400 tributaries such as the Yalong River, the Lancang River, and the Jialing River. The annual water runoff is about 300 billion cubic meters, ranking first in China. Therefore, because of the significant water resources in Sichuan Province, it is vital to quantitatively measure the sustainability of the water resource development.
All research data from 2008 to 2017 were sourced from the Sichuan Statistical Yearbook [11], the Water Resources Bulletin of Sichuan Province [12], the China Environmental Statistical Yearbook [13], and some relevant provincial statistics. The data for some indicators were derived from original data and formula conversions.

Main component extraction
This paper used SPSS 14.0 software to standardize the original data. As the principle of cumulative variance states that the contribution rate should be greater than 85%, four main components that contained 90.38% of the sustainable water resource system information and one main components that contained 89.43% of the socioeconomic system information were extracted. From formula (1) The formulas for the one principal sustainability socio-economic system components was: = 0.158 + 0.157 − 0.107 + ⋯ + 0.158

Determination of the Main Component Factor Weights
The principal component decision matrix was obtained by bringing the 2008 -2017 normalized values into the above principal component formulas. By combining formulae (4) -(6), the entropy and weights for the principal component factors were obtained, as shown in Table 2. In combination with formula (7), the principal component scores were multiplied with the weights for each year and then summed to calculate the measures for sustainable water resource development in Sichuan Province from 2008 to 2017. Further,in order to more intuitively observe the sustainable development of water resources in Sichuan Province, the sustainable development of water resources is divided into four categories by cluster analysis method, namely very high sustainability, high sustainability, average sustainability and poor sustainability. The results are shown in Table 3.

Analysis of evaluation results
Overall, Sichuan Province's sustainable water resource development capacity was a fluctuate upward trend, the development status from 2008 to 2011 and 2015 was in a poor sustainability state; the development status from 2012 to 2014 was average sustainability; the development status in 2016 was high sustainability; the sustainable development status in 2017 was very high sustainability.
From the comprehensive analysis, the decline in sustainability in 2009, 2011 and 2015 mirrored the water resource fluctuation trends. As the water resources declined, the sustainable development capacity also declined. Therefore, when seeking sustainable socioeconomic development, it is vital to ensure that there are improvements in the ecological water replenishment efficiency and a strengthening of the water cycle.

Conclusion
This paper comprehensively considers the water resources condition system, water resources development and utilization system, water resources protection system, social economic system, establishes a more complete evaluation index system, and then proposes a principal component-entropy-weight-weighted average method. The evaluation method overcomes the defects of the measurement index system which is not comprehensive and the measurement method is qualitative, and makes the evaluation result more scientific and reasonable. The comprehensive evaluation of the sustainable development of water resources in Sichuan Province shows that industrial water use efficiency, wastewater, domestic sewage discharge, treatment investment, treatment purification capacity, and ecological water supply rate need to be the focus of sustainable development of water resources.


text:
Performance evaluation of a motor operated oil palm crusher

The oil palm tree (Elaeis guineensis) is a perennial plant which is indigenous to tropical areas. It produces more oil per hectare than any of the oil producing crops. The primary products of the oil palm are palm oil (from the mesocarp) and palm kernel oil (from the kernels (seeds). Palm oil accounts for about 13% of the total world production of oils and fats, and is expected to overtake soybean oil as the most important vegetable oil (Sundram et al., 2003). The best quality of crude palm oil quality must have FFA percentage of less than 5% whereas the moisture content is less than 0.1% and the percentage of dirt is not more than 0.01%. The highly rated commercial oil palm fruit is type Tenera, a hybrid of Durra and Pisifera (Baryeh, 2001 and Sundram et al., 2003). Tenera has a shell of less than 3mm thick 60-96% mesocarp, 3-20% shell and 3-15% kernel making it tenable to high extraction yield. Vegetable oil consumption is increasing compared to animal fat due to its health implication. In 2007, the value of world trade in oilseeds and oilseed products was estimated to be 83 billion dollars; Abstract

Introduction
The oil palm tree (Elaeis guineensis) is a perennial plant which is indigenous to tropical areas. It produces more oil per hectare than any of the oil producing crops. The primary products of the oil palm are palm oil (from the mesocarp) and palm kernel oil (from the kernels (seeds). Palm oil accounts for about 13% of the total world production of oils and fats, and is expected to overtake soybean oil as the most important vegetable oil (Sundram et al., 2003). The best quality of crude palm oil quality must have FFA percentage of less than 5% whereas the moisture content is less than 0.1% and the percentage of dirt is not more than 0.01%. The highly rated commercial oil palm fruit is type Tenera, a hybrid of Durra and Pisifera (Baryeh, 2001 andSundram et al., 2003). Tenera has a shell of less than 3mm thick 60-96% mesocarp, 3-20% shell and 3-15% kernel making it tenable to high extraction yield. Vegetable oil consumption is increasing compared to animal fat due to its health implication. In 2007, the value of world trade in oilseeds and oilseed products was estimated to be 83 billion dollars; equivalent to 13% of the total agricultural trade and it was the third most valuable component in total world agricultural trade, next to meat and cereals (Ndukwu and Asoegwu, 2010). Therefore, high yields of the oil palm throughout the year are essential to meeting the high global expert market demand. Bangladesh is deficit in oils and fats since long. It produces only 10 percent of its requirement. At present, annual requirement of oil and fats in Bangladesh is about 1.76 million tons. Against this requirement, the local production of oils and fats has remained below 200,000 tons (Alam and Fakhru, 2013). In 2015-2016, 23 lakh ton oil was consumed in Bangladesh and to fulfill the requirement 20.4 lakh ton of oil had to imported (USDA, 2017). For increased demand the cultivation of palm is increasing in Bangladesh. About 10 lakhs of oil palm tree are planted in various region. Maximum palm trees are started flowering and bears huge fruit branches every year. Growers are facing problems with processing of these fruit. Due to lack of processing every year lots of fruits are wasted. So, it is necessary to supply a crusher and evaluate the performance of the crusher. Adeniyi et al., (2014) found that 35.3 % of the manual processors, 36.1 % of the semimechanized processors and 85.7 % of the fully mechanized processors produce palm oil of Grade A quality respectively and fully mechanized method given the best quality. Ismail et al., (2015) developed an improved palm kernel shelling and sorting machine. The machine optimum shelling-sorting efficiency was found to be 90 percent, throughput capacity was 59kg/h, and whole kernel recovery was 70 %, low costs of production and maintenance. Islam (2015) designed and developed a manually operated oil palm crusher at the workshop of farm power & machinery department, BAU. crushing capacity, rotating speed, crude oil percentage, oil extraction efficiency was respectively 0.84 kg/hr, 26 rpm, 22.14%, 82%. The weight of the crusher was 11kg. The Food and Agriculture Organization (FAO, 2002) of the United Nations compiled a bulletin that described method of palm oil processing. According to the bulletin, often small-scale facilities, which process two or less tonnes of fresh fruit bunch per hour, employ batch processes that utilize manual labor and had low operating costs. Large-scale facilities typically use continuous systems and required skilled labors and greater management. Large-scale plants process more than ten and often up to sixty tonnes of fresh fruit bunches per hour (Kwaski, 2002).
There are, however, no available records or published materials showing that the screw press has ever been evaluated or characterized. The objective of this study, therefore, was to evaluate the performance of a screw press with respect to its throughput capacity, feed rate, percentage of cake oil content, sediment percentage, percentage of crude oil yield, oil extraction efficiency, and oil quality so as to pronounce on the suitability and the need for any design improvements.

Materials and Methods
Experimental Site: The experiment was conducted at the engineering workshop of the Department of Farm Power and Machinery, Bangladesh Agricultural University, Mymensingh.

Physical properties of palm fruit:
The dimension of palm fruit was measured with the help of slide calipers. The average length and breadth of palm fruit was (3.375 ± 0.55) cm and (2.67 ± 0.65) cm respectively. The weight of an individual palm fruit and palm kernel were measured with the help of electrical weighing balance (ENTRIS822-1S, 0.01g, Sartorius Lab Instruments GmbH & Co. KG, Germany). Weight of ten palm fruits with kernel were taken by electric weight machine. The average weight of palm fruit was 22.57 gm. The weight of palm fruit was varied from 17 to 26.94 gm. Weight of palm kernel varied from 8.7 to 12 gm. Average weight of palm kernel was 10.44 gm.
To determine the moisture content, palm fruits were placed in the electric oven at 105 o C for 24 hrs (Razavi and Taghizadeh, 2005). After 24 hrs final weight was taken. By using following formula moisture content of palm fruit was determined.
The average moisture content was 20.73%.

Different parts of the crusher:
The crusher was designed and developed by Ahamad (2015). Photographic view of the oil palm crusher is shown in Figure 1.  (Ahamad, 2015).

Cylinder:
The cylinder was constructed with mild steel. Outside diameter of cylinder was 85 mm and inside diameter was 68 mm. The length of cylinder was 254 mm.
Hopper: Hopper was constructed with mild steel. It is the most important part for designing and developing of the crusher. The shape of the hopper was square. Feeding diameter of hopper was 63 mm.
Barrel: Barrel was constructed using mild steel. The length of the barrel was 279.4 mm. outside diameter of barrel was 85 mm and inside diameter was 68 mm.
Pulley: It was made of cast iron (CI). Diameter of pulley was 254 mm. It was adjusted to gear box. Whole diameter of pulley was 28 mm.
Gear and pinion: Among the gears, the largest one is gear, and the smallest one is pinion. These were made of carbon steel (CS). Number of teeth in gear was 45 and in pinion were 12.
Idle shaft and line shaft: Idle shaft was made of CS and Line shaft was made of CS. Two bearing (UCP206) were connected with idle shaft.

Screw shaft:
The screw shaft was constructed with mild steel. The length of the screw shaft was 939.8 mm. The length of helical screw portion was 533.4 mm. The diameter of helical screw shaft was 66 mm.
The length of small helical screw portion was 203.2 mm and diameter of small shaft was 38mm.
Jamnut and pressure cone: Jamnut and pressure cone was constructed using mild steel. Outside diameter of jamnut was 63 mm and inside diameter was 35 mm. The length of the jamnut was 60 mm. The outside diameter of pressure cone was 90 mm and inside diameter was 38 mm.
Spring: Spring was constructed with CS. Diameter of spring was 62mm and the length of spring was 110 mm.
Frame: Frame was consist of mild steel (MS). Crusher was mounted on frame. Table 1. The construction was easy and simple.

Specification of the machine: Specification of the crusher is shown in
Working principle: In operation, the steamed palm fruit was introduced into the machine through the feeding hopper; the machine convey presses the palm fruit inside the cylindrical barrel with the aid of the worm shaft until crude oil is pressed out of the mash. The crude oil extracted was drained through the oil channel into the oil tray where it was collected. The residual cake is discharged at the cake outlet and collected at the cake tray. The machine was powered Cylinder by a 7.5hp three -phase electric motor with the construction materials being locally available at affordable costs. Flow diagram of unit operations in palm oil processing by motor operated crusher is given in Figure 2.

Collection of Fresh Fruit Bunch (FFB):
The palm oil production process started after the FFB were harvested. After harvesting, it is important to process fresh fruit bunches as soon as possible to prevent rapid rise in free fatty acids, which could adversely affect the quality of the crude palm oil. FFB were harvested using chisels. These fruit bunches (each bunch weighing about 3 kg) were then collected and FFB were weighed accordingly.

Selection of fresh ripen fruit:
Matured fruits were collected on the basis of odor, size and color. The unripe fruit contains very little oil but the mesocarp of ripe fruits has an oil content of 70-75 % of its total weight. Palm oil has a balanced ratio of saturated and unsaturated fatty acids.

Figure 2.
Unit operations in palm oil processing by motor operated crusher.

Separation of fresh fruit from bunch:
The FFB consists of fruits embedded in spikelets growing on a main stem. Manual threshing is achieved by cutting the fruit-laden spikelets from the bunch stem with an axe and then separating the fruits from the spikelets by hand. Fresh fruits were separated from the bunch carefully.
Cleaning the fruits: After separation, the fruits were cleaned with water to remove the foreign materials from the fruits.

Weighed the fruit:
The palm fruits were weighed for streaming with the help of weighing machine.

Steaming of palm fruits:
Steaming typically destroys oil-splitting enzymes and arrests hydrolysis and auto oxidation, weakens the fruit stem and makes it easy to remove the fruits from bunches, helps to solidify proteins in which the oil-bearing cells are microscopically dispersed, weakens the pulp structure, softening it and making it easier to detach the fibrous material. It prevents free fatty acid build up in the oil. Steaming was performed for two to five minutes. Some moisture was removed through streaming in the pan.

Drying the fruits:
After weighing, the fruits were kept in open air for one hour to remove the extra water from the palm fruits.

Weighing of steam fruit:
The palm fruits were again weighed. Some weight was lost for moisture loss during steaming.

Pressing the fruit by machine to extract crude palm oil:
To extract crude oil, the fruits were poured into the hopper of the machine and pressed (Figure 3) to separate oil from fiber and seeds. Then crude palm oil was collected.
(a) Crude palm oil with scum (b) Oil cake Boiling of crude palm oil: After extracting, the crude oil with scum was boiled for five minutes. Then it was kept still for a few minutes to extract the crude oil and separate the scum as shown in Figure 4.  Where, W 1 = Weight of crude palm oil (kg)

Determination of oil cake percentage in fruit:
The amount of oil cake was weighed and the amount of fruit was weighed before crushing.

Determination of scum percentage in fruit:
Scum was collected after crushing and weighed.

Determination of oil extraction efficiency of the crusher:
The standard crude oil percentage of oil palm is 26-27 % (FAO, 2002). For calculating oil extraction efficiency, CPO percentage obtained by the motor operated oil palm crusher is divided by this standard value.
Oil extraction efficiency= Where, i= Rate of interest.

Break-even point analysis:
The break-even point for machine was calculated by determining annual cost & comparing it with manually operated crushing machine.

Determination of quality parameters of crude palm oil:
The quality of crude palm oil can vary widely depending upon the processing technique. The present work was undertaken to evaluate some quality parameters of crude palm oil. The experiment was conducted at the lab of Food Technology & Rural Industries, BAU. The parameters evaluated were free fatty acid (FFA), Iodine value (IV), Peroxide value (PV), Saponification value.

Determination of acid value:
The acid value is defined as the number of milligrams of potassium hydroxide required to neutralize the free fatty acids present in one gram of fat (FSSAI, 2015).  (12) Where, B=volume in ml of standard sodium thiosulphate solution required for the blank; S=volume in ml of standard sodium thiosulphate solution required for the sample; N=normality of the standard sodium thiosulphate solution.

Determination of peroxide value:
The peroxide value of oil is the amount of peroxide present and expressed as milli-equivalents of peroxide per 1000gm of sample (FSSAI, 2015).

……………………(13)
Where, B=Volume in ml of standard sodium thiosulphate solution required for the blank; S=Volume in ml of standard sodium thiosulphate solution required for the sample; N=Normality of the standard sodium thiosulphate solution.

Determination of saponification value:
The saponification value is the number of mg of potassium hydroxide required to saponify 1 gram of oil/fat. The oil sample was saponified by refluxing with a known excess of alcoholic potassium hydroxide solution. The alkali required for saponification was determined by titration of the excess potassium hydroxide with standard hydrochloric acid (FSSAI, 2015).
. (14) Where, B=Volume in ml of standard hydrochloric acid required for the blank; S=Volume in ml of standard hydrochloric acid required for the sample; N=Normality of the standard hydrochloric acid

Results and Discussion
Amount of crude palm oil, scum, oil cake: Amount of crude palm oil, oil cake, scum were collected from fruits are shown in Table 2.

Amount of crude palm oil, scum, oil cake percentage:
The crushing operation was done for 10 min. In treatment 1,2,3,4,5,6,7 and 8, the crude oil production was too little for huge time steaming and unripe fruit. The unripe fruits contain very little oil but the mesocarp of ripe fruits has an oil content of 70-75% of its total weight. Hence, crude palm oil percentage, scum percentage, and oil cake percentage was not determined. The crushing capacity of the machine, amount of Crude palm oil percentage, oil cake percentage, scum percentage were determined as shown in Table 3.

Crushing capacity of the crusher (kg/min):
The highest crushing capacity was found 0.290 kg/min and lowest was 0.18 kg/min. The average crushing capacity was 0.212 kg/min. So, crushing capacity was 12.72 kg/hr. In manual crusher, crushing capacity was 0.84 kg/hr (Islam, 2015).
So, crushing capacity of motor operated crusher is 15 times higher than manual crusher.

Crude palm oil percentage in fruit:
The highest crude palm oil percentage in fruit was 23.04 and lowest was 15.38. The average crude palm oil percentage in fruit was 19.67. In manual crusher, crude palm oil percentage in mesocarp of fruit was 22.14% (Islam, 2015). Here, mesocarp was separated from fruit and large amount of fruit was required than power operated crusher and amount of mesocarp was taken for calculation rather than amount of fruit. The standard crude oil percentage of oil palm is 26-27% (FAO, 2002).

Amount of crude palm oil per kg of fruit:
The highest amount of crude palm oil per kg of fruit was 0.23 kg and lowest was 0.15 kg. The average crude palm oil per kg of fruit was 0.20 kg. In manual crusher it was 0.221 kg from 1 kg mesocarp (Islam, 2015).

Oil cake percentage in fruit:
The highest oil cake percentage was 67.76% and lowest was 55.22%. The average oil cake percentage in fruit was 62.2%. In manual machine, it was 44.28 % in mesocarp (Islam, 2015).

Sediment /scum percentage in fruit:
The highest sediment percentage in fruit was 19.5% and lowest was 10.25%. The average sediment percentage in fruit was 14.04%. In manual it was 31.42% (Islam, 2015). It was because, in manually oil was extracted only from fruit mesocarp.
Oil extraction efficiency of the crusher: The highest oil extraction efficiency was found 85.33% and lowest was 56.96%. The average oil extraction efficiency of the crusher was 72.84 %. In manual crusher oil extraction efficiency was 82% (Islam, 2015). It was because, in calculation the amount of fruit was not calculated, the amount of mesocarp was taken for calculation.

Operating cost of the machine:
The manufacturing cost of the crusher was 44,500 tk. Total operating cost and annual cost were shown in Table 4. Break-even analysis: From Figure 5, it can be observed that the break even point is 700 hr. It means that if the machine is used more than 700 hr, it will be economically suitable. Crushing capacity of the machine is 12.72 kg/hr. So, if 8904 kg fruit is crushed in a yr, it will also be suitable. Assume that, weekly working hour is 30 hrs. So, 164 days or 5.5 months will require to reach the break-even point.

Quality parameters of crude palm oil:
Palm oil has the color of a rich, deep red which is said to be derived from its rich carotenoid contents. The parameters evaluated were: Free fatty acid (FFA): 1.33, Iodine Value (IV): 50, peroxide value (PV): 2 meq/kg, saponification value: 120. In palm oil FFA ranged between 1.06-2.9%; IV: 48-53; PV: 2. 93-10.02 andSPV: 190-205 (Sarwar et al., 2016 andOrsavova et al., 2015). The obtained value varies with expected range because the oil was crude and was not refined. Among palm oil, palm kernel oil, soybean oil, coconut oil, sunflower oil, mustard oil, castor oil, olive oil, rice bran oil and ground nut oil, the highest value of IV in soybean oil (130) and lowest in coconut oil (10); the highest value of PV in soybean oil (3.19) and lowest in palm kernel oil (0.3); the highest range of saponification value in coconut oil ( 250-265) and lowest in soybean (180-200) ( Table 5). Palm oil is unique among vegetable oils because of its high saturated acid content with a significant amount (10-16%) of saturated acid. Palm oil also contains appreciable amounts of free fatty acids, which can have a substantial effect on its physical properties.
The average peroxide value was 2 meq/kg and Frank et al., (2013) found 4.34 meq/kg. The average free fatty acid value was 1.33% and Frank et al., (2013) found 10.28%-0.64%. The result is near to the result of palm oil. But it is not suitable for eating. Boiling is not enough for using and marketing. The red palm oil is acceptable for consumption without further processing other than simple filtering or settling operation to remove solid impurities.

Conclusions
A motor operated oil palm crusher was tested for palm oil extraction from fresh fruit bunch. The crusher was simple enough for local fabrication, operation, repair and maintenance. The weight of the crusher is 70 kg which is easy to carry. It is powered by a three-phase 7.5 hp electric motor. The crusher has an average oil extraction efficiency of 72.84 % from palm fruit. That was obtained from 2.5 kg of fruit at the steaming time of 2min. The average crushing capacity of the crusher was 12.72 kg/hr. The average crude palm oil percentage in fruit was 19.67. The average crude palm oil per kg of fruit was 0.20 kg. That means 200 gm crude palm oil extracted from 1 kg of fruit. The average oil cake percentage in fruit was 62.2 and the average sediment percentage was 14.04.
The break even point of the crusher is 700 hr. It means that if the machine is used more than 700 hr, it will be economic. Crushing capacity of the machine is 12.72 kg/hr. So, If 8904 kg fruit is crushed in a yr,it will also be economic. The quality parameters evaluated were Free fatty acid, Iodine value, Peroxide value and Saponification value. Free fatty acid was 1.33; Iodine value was 50; Peroxide value was 2meq/kg; and Saponification value was 120. A palm fruit oil processing plant based on this technology can provide employment for at least two persons at the same time provide palm oil at affordable costs for rural and urban communities.The crusher can be used for small and medium scale palm fruit processors in order to mechanize the extraction process and increase production output. But some problem were found in crusher during crushing such as the hole of the cylinder was too small for oil extraction, the diameter of the feeding hopper was small for feeding, the shaft was sufficed out at the time of crushing and other small trouble observed in crusher during crushing.


extfieldsofstudy: Physics Chemistry

text:
The effects of different extraction conditions on the polyphenol, flavonoids components and antioxidant activity of Polyscias fruticosa roots

Polyscias fruticosa is an herbal plant having a myriad of medicinal purposes, especially in antioxidant activities, anti-inflammatory and antipyretic. This study was carried out to evaluate the effects of different extraction conditions on the polyphenol, flavonoids content extraction and antioxidant activity of the Polyscias fruticosa root extract. DPPH and ABTS methods were employed to measure free radical scavenging activities. The total polyphenol, flavonoids content was calculated by gallic acid and quercetin equivalent, respectively. The absorbance is measured by using UV-Visible spectrophotometry. The results shows that the conditions in which the highest extraction yield was attained consisted of 90% ethanol, the ratio between material and ethanol of 1:20 g/mL, extraction time of 3 hours and extraction temperature of 30°C. This extract showed the highest polyphenol content (96,09 μg gallic acid equivalent/mg) and the highest flavonoid content (58,30 μg quercetin equivalent/mg of dry extract). Moreover, DPPH and ABTS antioxidants activity results showed that Polyscias fruticosa root extracts exhibited the IC50 value of 96,14 μg/mL and 38,76 μg/mL, respectively. These results indicate that Polyscias fruticosa roots can be used in dietary applications with the potential to reduce oxidative stress.

Introduction
Nowadays, natural extraction from medicinal plants has been receiving a great deal of public attention due to the abundant content of bioactive molecules including vitamins, proteins, phenolic compounds [1][2][3][4]. These bioactive compounds play a vital role in different industrial fields such as cosmetics, pharmaceutical, food industries due to antimicrobial, antioxidant, anticancer activities [5][6][7]. Moreover, bioactive mixtures may cure different fatal diseases caused by Reactive Oxygen Species (ROS). Polyscias fruticosa belongs to the Araliaceae family which consisted of about 159 species and Energy Security and Chemical Engineering Congress IOP Conf. Series: Materials Science and Engineering 736 (2020) 022067 IOP Publishing doi: 10.1088/1757-899X/736/2/022067 2 cultivated mainly in tropical India, Malaysia, and Polynesia [8]. In recent years, cultivating Polyscias fruticosa have been receiving a great deal of public attention due to the essential oil extracted from its flowers and leaves.
The previous study reports that P. fruticosa essential oils play an important role in the treatment of different diseases such as ischemia, inflammation and increase blood in the brain [9]. P. fruticosa is one of spice and medicinal herb which used as a vegetable on soup and also enhance the flavor and organoleptic properties. There are different highly valuable compounds in the essential oil of Polyscias fruticosa such as , alkaloids, saponins, vitamins B1, B2, B6, C, 20 amino acids [10]. Polyscias fruticosa essential oil is powerful antibacterial, analgesic, wound healing, and anti-inflammatory [11]. Human diseases may results in oxidative stress due to the imbalance between the neutralization and formation of pro-oxidants [12] [13] . Moreover, antioxidants with free radical scavenging activities have the function in the prevention and therapeutics of diseases. The previous study demonstrated that the antioxidant efficiency of the extracts against lipid peroxidation initiated by Fe2+/ ascorbate. [14]. Polyphenolic compounds including flavonoids and phenolic acids are usually located in plants which have antioxidants activity [15] [16].
In recent years, cultivating Polyscias fruticosa have been receiving a great deal of public attention due to the essential oil extracted from its flowers and leaves [17]. The present study was carried out to evaluate chemical components and determine the effects of different extraction conditions on the total polyphenol, flavonoids content, and antioxidant activity of the roots of P. fruticosa.

Materials and chemicals
The roots of Polyscias fruticosa was collected from Ho Chi Minh Biotech, Viet Nam in March 2019. The Polyscias fruticosa roots were dried at 40˚C, following blending to remove the water content and powdering.

Determination of polyphenol, flavonoids components of P.fruticosa roots in different factorst.
A set of experiments was carried out for evaluating the effects of different organic solvents on polyphenol, flavonoids content extraction including organic solvents (ethanol, methanol and acetone), concentrations of solution (50, 70 and 90 %), ratio between material and extracting (1:4, 1:10 and 1:20), the extraction time (2, 3 and 4 hours) and the temperature (30, 50 and 70°C). The parameters of solvent concentration, ratio of material to solvent, time and temperature were initially fixed with the respective values of 90%, 1:20 (g/ml), 2 hours and 30°C. Suitable extraction solvent is selected based on the highest total polyphenol and flavonoid content. Total polyphenol and flavonoid contents of the extracts were determined using modified method of Vuong et al. with some modifications [18]. Then use this solvent to evaluate other parameters to find the best extraction conditions.
Extracts exhibited the highest polyphenol content and the highest flavonoids content was evaluated the ability to capture free radicals DPPH and ABTS.

Total polyphenol content determination (TPC)
The level of total polyphenol in the crude extracts was determined by using Folin-Ciocalteu reagent and external calibration with gallic acid [18]. First, the 0.5 mL extract was pipetted into a test tube containing 2.5 mL Folin-Ciocalteu reagent 10% (v/v). After 5 minutes, 2 mL Na2CO3 20% (w/v) was added to the sample. Next, the mixture was vigorously shaken and incubated for 60 minutes in the dark. Finally, the absorbance was spectrophotometrically measured at 765 nm and the results were shown in µg of gallic acid equivalents per dry weight of sample (µgGAE/mg).

Total flavonoids content (TFC)
Base on the aluminum chloride colorimetric method, the total flavonoid content was determined [18]. Mixing 0.5 mL the extract with 0.3 mL 5% NaNO2. After 5 minutes, mixing with 0.3 mL 10% AlCl3. Then, 2mL 1M NaOH and 1.9 mL distilled water was added and vigorously shaken. The absorbance was spectrophotometrically measured at 510 nm.

DPPH Scavenging Activity
The antioxidant activity of the individual essential oil was tested by means of 1,1-diphenyl-2picrylhydrazyl (DPPH) assay [18]. 1.5ml DPPH (OD 517 nm = 1.1 ± 0.02) into 500 µL solution sample. The sample solution with pre-concentration, and the mixed the stable at room temperature in the dark within 37 min. The optical measurement of mixture by UV/VIS -1800 Shimadzu Spectrometer at 517 nm. Blank sample, but 500 µL solution replaced EtOH 99.7%. Standard sample: Vitamin C (0.1g ÷ 0.01) was dissolved ethanol 99.7% into volume flask 100mL, in the dark (C = 100 µL/mL). [19] The percent DPPH scavenging effect was calculated by using following equation: DPPH scavening effect (%) or percent inhibition (%I) [20]. The IC50 value was the concentration of the sample which inhibited percentage reaches 50% [21]

Statistical analysis
All determinations were carried out in triplicate and the results were expressed as mean values and standard deviation. One-way analysis of variance (ANOVA) was performed using SPSS statistics software (version 20, IBM, USA) and differences between samples were compared using Tukey's test with a significance of 0.05 (P < 0.05).

The affects of different organic solvents on the extraction of total polyphenol, flavonoids
Extraction solvent is one of the significant factors which affect extraction efficiency. Figure 1 shows the effects of extraction solvent on polyphenol and flavonoid content. The parameters of between solvent concentration, ratio material and solvent, time, and temperature were fixed with the respective values of 90%, 1:20 (g/ml), 2 hours and 30°C. The results showed that the ethanol extraction solvent gave higher polyphenol (28.56 µgGAE/mg) and flavonoid (26.26 µgGAE/mg) content than the methanol and acetone extraction solvent. As most phenolic compounds are polar permitting, so they have been efficiently extracted in high yield, in higher polarity solvent like ethanol [19]. Beside, ethanol is a common solvent extraction solvent in industry due to its non-toxic, relatively inexpensive and available.   Figure 2 indicates the total polyphenol and flavonoids content in different concentration solvents. Effect of concentration of solvent on the total polyphenol and flavonoids content was studied at 50%, 70% and 90%. Other parameters were fixed include ethanol, ratio between material and solvent (1:20 g/ml), extraction time (2 hours) and temperature (30ºC). Suitable extraction solvent concentrations are selected based on polyphenol and flavonoids content. The results showed that the extract of ethanol 90% gave higher total polyphenol (28.56 µgGAE/mg) and flavonoid content (26.26 µgQE/mg) than ethanol 70% and 50% with significantly difference (p<0.05).

Figure 2. The total polyphenol and flavonoids content in different concentration solvents
Ethanol 90% is the most suitable solvents for extract from P.fruticosa root due to its ability to dissolve not only many polar but also nonpolar compounds. Moreover, ethanol has been known as a suitable solvent for polyphenol extraction and is safe for human consumption. From the results obtained, we selected ethanol 90% extraction solvent for further experiments.  Figure 3 illustrates the total polyphenol and flavonoids content in the different ratio (1:2, 1:1, 1:4) between sample and solvent. The solvent extraction procedure was carried out according to the extraction procedures described by Tan et al. (2011) with slight modifications [25]. The total polyphenol and flavonoids content in the ratio between material and ethanol (1:20 g/mL) was significantly higher than ratio 1:4 and 1:10 (g/ml). Further increase in the ratio between sample and solvent (1: 20 to 1: 4) was not significant in increasing of the concentration of both total polyphenol and flavonoids content (p>0,05). According to Tan et al. (2011), a high solvent ratio is considered favorable in extracting polyphenol and flavonoid compounds. This result is in line with a previous study which proved that Pegaga (Centella Asiatica) compounds yields were increased gradually with the increase of the solid-to-solvent ratio and achieved the highest polyphenol and flavonoid content at 1:15 [25].

The effects of extraction temperature on the total polyphenol, flavonoids content extraction
The total polyphenol and flavonoids content in 30°C were significantly higher than the extraction temperature 50°C and 70°C. Extraction temperature affects solubility, mass transfer rate and stability of polyphenol compounds. Within a certain limit, high temperatures enhance the extraction efficiency by enhancing the degree of diffusion and solubility of analytes in solvents [6]. Beyond that limit, high extraction temperatures will reduce total polyphenol and flavonoids content.

Antioxidant activity of Polyscias fruticosa roots in the most suitable extraction solvent
There are different techniques for estimating the antioxidant activity of both synthetic compounds and natural. The DPPH assay and ABTS were a rapid and low-cost method, which usually used for evaluation of the antioxidative potential of different natural stocks. The results showed that ethanol (90%); ratio between material and ethanol (1:20 g/mL); time (3 hours); temperature (30°C) to extract total polyphenol and flavonoids content in Polyscias fruticosa roots is highest. This extract was evaluated antioxidant activity by method DPPH radical scavenging activity and ABTS inhibition activity. Antioxidants can remove the radical by hydrogen donation, which results in a decrease of DPPH absorbance at 515 nm. The IC50 value was the concentration of the sample which inhibited percentage reaches 50% [25]. Therefore, IC50 values are negatively correlated to antioxidant activity, the lower IC50 value means the highest antioxidant activity of the tested sample. Figure 6,7 shows the IC50 values in the DPPH and ABTS radical scavenging activity assay of the extracts IC50 = 96.14 µg/mL and 38,76 µg/mL, respectively.

Conclusions
Polyscias fruticosa essential oil has been widely used in folk medicine for treatment of anxiety, memory deficit, and cancer thanks to its high antioxidant activity and antibacterial properties. The parameters between different organic solvents, solvent concentration, ratio material and solvent, extraction time and extraction temperature effect significantly on the extraction process of Polyscias fruticosa roots. The results showed that ethanol (90%); the ratio between material and ethanol (1:20 g/mL); time (3 hours); temperature (30°C) to extract total polyphenol and flavonoids content in Polyscias fruticosa roots is highest. Total phenolics result was calculated as (96,09 µg gallic acid equivalent/mg), total flavonoids as (58,30 µg quercetin equivalent/mg of dry extract) and the ability to capture free radicals DPPH of this extract (IC50 = 96,14µg/ml) and anti-oxidants activity ABTS (IC50 = 38,76 µg/ml). According to the results of the present investigation, the plant showed significant antioxidant activity that can be used for the medical purpose for the treatment of various diseases.