Effect of mineral fertilizer on rain water and radiation use efficiencies for maize yield and stover biomass productivity in Ethiopia

Agricultural Systems 168 (2019) 88–100

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Agricultural Systems journal homepage: www.elsevier.com/locate/agsy

Effect of mineral fertilizer on rain water and radiation use efficiencies for maize yield and stover biomass productivity in Ethiopia

T

⁎

Amit Kumar Srivastavaa, , Cho Miltin Mboha, Thomas Gaisera, Arnim Kuhnb, Engida Ermiasb, Frank Ewerta a b

Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, D-53115 Bonn, Germany Institute for Food and Resource Economics, University of Bonn, Nussallee 21, Bonn, Germany

A R T I C LE I N FO

A B S T R A C T

Keywords: Maize Radiation use efficiency Rain water use efficiency Crop model Sub-Saharan Africa

The impact of increasing rates of typically used mineral fertilizer on Rain water use efficiency (WUE) and Radiation use efficiency (RUE) of maize grain yield and stover biomass productivity was estimated across the Agro-Ecological Zones (AEZs) of Ethiopia using the crop model LINTUL5 embedded into a general modeling framework, SIMPLACE (Scientific Impact Assessment and Modeling Platform for Advanced Crop and Ecosystem Management) with the hypothesis that WUE and RUE would increase with higher application rates of mineral fertilizer and vary for maize grain yield and stover biomass across the AEZs. The simulations were run using a long maturing cycle maize variety (BH660) and a medium maturing cycle maize variety (BH540) with historical weather data (2004–2010).There were strong effects of the application rate of mineral fertilizer on WUE and RUE of maize yield and stover biomass across the AEZs. The highest WUE of 11.5 kg mm−1 and 9.4 kg mm−1 in maize grain yield and stover biomass respectively was estimated with the application of 315 kg N ha−1 + 105 kg P ha−1 in AEZ 3 having the lowest amount of rainfall during the crop growth period as compared with AEZ 1 and 2.The findings of the current study indicate that WUE in grain and stover production can be increased to by 172% to 363%, and 230% to 352% respectively depending upon the AEZs, based on management intervention in terms of increased fertilizer application rates as compared with the WUE under unfertilized conditions. On the other hand, the highest RUE of 3.0 kg MJ−1 and 2.1 kg MJ−1 in maize grain yield and stover biomass respectively was estimated in AEZ 2 with the application of 315 kg N ha−1 + 105 kg P ha−1. RUE in grain yield and stover biomass can be increased to the tune of 177% to 362%, and 216% to 351% respectively depending upon the AEZs with the increased application of N and P compared with the RUE under unfertilized conditions. The economic analysis indicates optimal fertilizer application levels of 225 N + 75P kg ha−1 for maize production under average national conditions and prices and a slightly lower rate of 180 N + 60P kg ha−1 in regions where water availability tends to constrain grain yields in addition to the nutrient deficit.

1. Introduction In Ethiopia maize has a vital role in food security, social development, and the national economy. It is a principal food crop and provides the cheapest source of calorie intake, providing 20.6% of per capita calorie intake nationally (IFPRI; Food Policy Research Institute, 2010). In the highlands of the country including the west and central part, there is no possibility for farmers to harvest more than once a year due to the lack of irrigation systems and large spatial and temporal variability in rainfall. This vulnerability is aggravated by a predominance of low-input, rain-fed production systems, and depleted soils (Getnet

et al., 2016; Tittonell et al., 2010). Furthermore, interactions between these limiting resources strongly influence the efficiency with which the resources are used, crop productivity, and the sustainability of production systems. Therefore, understanding the resource use and resource use efficiencies of current production systems could help to identify possibilities of producing more with the available resources and to address the variability in yield and biomass production across the Agro-ecological zones in Ethiopia. The impacts of specific factors on crop growth and crop productivity have been studied for a long time and therefore, in general, are well known (Siebert, 2014). However, research gaps still exist for example in

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Corresponding author. E-mail addresses: [email protected] (A.K. Srivastava), [email protected] (C.M. Mboh), [email protected] (T. Gaiser), [email protected] (A. Kuhn), [email protected] (E. Ermias), [email protected] (F. Ewert). https://doi.org/10.1016/j.agsy.2018.10.010 Received 14 May 2017; Received in revised form 26 October 2018; Accepted 31 October 2018 0308-521X/ © 2018 Elsevier Ltd. All rights reserved.

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Fig. 1. Map of Ethiopia showing simulation units and the Agro-ecological zones.

security problems by providing better access and buying capacity of the food (Srivastava et al., 2017; Mauser et al., 2015). Currently, a great deal of attention is paid to water and nutrient use efficiencies. However, how these efficiencies in the current production system could be increased, and their variability across regions, has received less attention as they may vary during plant growing seasons and depending on climatic conditions (Yi et al., 2010). Recently, Getnet et al. (2016) analyzed water use efficiency (WUE) of maize grain yield and the spatial variation in different farming zones of Central Rift valley of Ethiopia. The results indicated that the current WUE could be improved three to six times higher by improving nutrients and crop

the understanding of interactions between factors affecting resource use efficiency, in the importance of the factors and their interaction at the large scale (for instance to explain yield gaps at national scale), in the potential of different management measures to adapt crop productivity and resource use to climate change. Besides grain yield, currently, the increasing global demand for biomass, as primary agricultural products and feedstock for various forms of usage, has started to change the global agricultural production and price structure. This rise in global biomass demand is an opportunity for many agricultural-based, lowincome economies, like sub-Saharan countries, to diversify their economies and generate income which in turn could address the food 89

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absence of pests, diseases, and weeds (Wolf, 2012). Biomass production is based on intercepted radiation according to Lambert-Beer's law and light use efficiency. The produced biomass is partitioned among various crop organs (leaves, stems, storage organs and roots) according to partitioning coefficients defined as a function of the development stage of the crop. The phenology is simulated by the accumulation of thermal time above a defined base temperature. Photosynthesis and total crop growth rate are calculated by multiplying the intercepted light and radiation use efficiency (RUE). Total crop growth, root-shoot partitioning, and leaf area expansion are further influenced by water stress. To simulate a continuous cropping system, the model was embedded into a general modeling framework, SIMPLACE (Scientific Impact Assessment and Modeling Platform for Advanced Crop and Ecosystem Management) (Gaiser et al., 2013). The SIMPLACE < LINTUL5-SLIMSoilCN > solution of the modeling platform was used in this study. SLIM is a conceptual soil water balance model subdividing the soil in a variable number of layers, substituting the two-layer approach in Lintul5 (Addiscott and Whitmore, 1991). To estimate nitrogen uptake by crop, turnover, leaching of soil mineral nitrogen (Nitrate-N and Ammonium-N) in layered soils, the sub-model ‘SlimNitrogen’ derived from the SLIM model (Addiscott and Whitmore, 1991) and the submodel ‘SoilCN’ (Corbeels et al., 2005) was used. ‘SlimNitrogen’ calculates the daily changes of three pools of soil mineral N in each soil layer by considering i) Application of nitrate or ammonium fertilizer; ii) Leaching of nitrate and ammonium; iii) supply of ammonium-N from soil organic matter mineralization; iv) nitrification of ammonium-N and v) Crop N uptake (http://simplace.ipf.uni-bonn.de/doc/3.3/lap/class_ net.simplace.client.simulation.lap.slim.SlimNitrogen.html).‘SoilCN’ calculates turnover processes of soil organic carbon and nitrogen in multiple storage pools in multi-layered soil profiles. It incorporates a mechanistic representation of the role of soil organisms in the N mineralization-immobilisation turnover process during decomposition. (For details refer to Corbeels et al., 2005). Water stress occurs when the available soil water is between a defined critical point and wilting point or higher than the field capacity (water-logging).The critical point is a crop specific value which is calculated according to Allen et al. (1998) and depends on crop development, soil water tension, and potential transpiration. Water, nutrients (NPK), temperature, and radiation stresses restrict the daily accumulation of biomass, root growth, and yield. Stress indices are calculated daily for water (WSI) and nutrient limitations (NNI) and range from 0.0 to 1.0. The estimation of the daily increase in crop biomass, considers, on a given day, the maximum stress index among water, nitrogen, phosphorus and potassium stress. Water stress occurs when available water in the soil is below crop-water-demand. The same holds for nitrogen stress, that is, when crop available nitrogen in the rooted soil profile is lower than crop nitrogen demand.

management practices (e.g., timely operations and input use etc.). However, in addition to WUE, the variability in yield and total biomass (i.e., Stover) production in different production environment may also be attributed to changes in Radiation use efficiency (RUE) values (Shah et al., 2004) and the estimation of WUE and RUE will allow farmers to adapt their production practices to climate variability (Enciso et al., 2015) under rain-fed and low-input agriculture production systems. Moreover, quantifying the relationships between RUE, WUE, biomass accumulation and grain yields would be of great practical value to facilitate the elucidation of crops' physiological responses, breeding and improvement in the area (Awal et al., 2006). In the current study, we used a model-based approach for estimating the water and radiation use efficiencies in grain and stover production in Ethiopia and hypothesized that the i) Rain water Use Efficiency (WUE) and Radiation Use Efficiency (RUE) would increase with higher applications of mineral fertilizer ii) the increase in WUE and RUE would be different for maize grain and stover production and vary across the Agro-Ecological Zones (AEZs) in Ethiopia, and iii) maximum WUE and RUE would be reached at different mineral fertilizer application rates depending on the AEZs. As per the hypotheses, the following objectives have been formulated: i) to determine how WUE and RUE in grain and stover production are affected by N application rates, ii) to determine the variations in grain yield and Stover production across the AgroEcological Zones (AEZs) in Ethiopia, and iii) to identify the maximum N and P application rates that are profitable in Ethiopia based on N + P fertilizer price/ maize grain price ratio. The focus on spatial patterns in resource use and their impact on crop productivity imply that the impact of yield-limiting factors like weeds, pests, and diseases is not investigated in this study. 2. Materials and methods 2.1. Study area and simulation units Ethiopia lies within the tropics between 3°24′ and 14°53′ N; and 32°42′ and 48°12′ E with an estimated arable land area of 15.1 million hectares (FAO, 2014). The climate of the country is diverse ranging from semi-arid desert in the lowlands to humid and warm (temperate) in the southwest. Mean annual rainfall distribution ranges from a maximum of more than 2000 mm over the Southwestern highlands to a minimum of less than 300 mm over the South-eastern and North-western lowlands. The mean annual temperature also varies widely, from lower than 15 °C over the highlands to above 25 °C in the lowlands (Regassa et al., 2010). The simulations were done at the 1 km grid cell level, where cropland (Fig. 1) and soil data are available (details about soil data is under section 2.4.1). A long maturing cycle maize variety (BH660) (see details in section 2.3) was used in the simulations in AgroEcological zones (AEZs) 1 and 2 (Fig. 2) where the length of crop growing season is more than 160 days, elsewhere (AEZ 3) a medium maturing cycle variety (BH540) (see details in section 2.3) was used in the simulations. The simulated yield from all the simulation units within each administrative zone was averaged to obtain a representative value for a specific year for comparing them with the observed yield.

2.3. Dataset for model calibration In this study, two sets of hybrid maize cultivar namely BH660 (a long maturing cycle variety) and BH540 (a medium maturing cycle variety) related parameters (Table 1) were calibrated against the experimental data (yield and phenology) under rain-fed conditions collected from the Melko (Jimma Agricultural Centre) for the year 2008 to 2012. Fertilizer application rate used in the experiments was 23 kg ha−1 of urea and 217 kg ha−1 DAP (Di-Ammonium Phosphate) at planting and 150 kg ha−1 urea after 35 days of planting. According to Jaleta et al. (2013) both BH660 and BH540 are the most popular and widely grown maize varieties in the country covering major maize producing areas. Default maize (Z. mays L) crop parameter dataset for Lintul5 was used and some parameters, which are similar or identical to the maize crop parameters in the WOFOST model (Boogard et al., 1998; Boons Prins et al., 1993) were adjusted. This parameter set was used as a starting point to establish a new parameter set for the maize varieties BH660 and BH540. TSUM1 (thermal time requirement from emergence

2.2. Model description LINTUL5, a bio-physical model that simulates plant growth, biomass, and yield as a function of climate, soil properties, and crop management using experimentally derived algorithms. LINTUL5 have been widely used in various studies at the field, country, and continental scale (Srivastava et al., 2017; Srivastava et al., 2016; Trawally et al., 2015; Eyshi Rezaei et al., 2015; Zhao et al., 2015; Webber et al., 2015; Gaiser et al., 2013; Franke et al., 2013). The applied version LINTUL5 simulates potential crop growth (limited by solar radiation only) under well-watered conditions, ample nutrient supply and the 90

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Fig. 2. Soil physical properties (Sand, Clay, Bulk density, Soil available water (SAW) and Soil organic carbon (OC) at three soil depths (i.e., 0.15, 0.3 and 1.0 m) of Ethiopia covering the crop land.

the entire parameter space of all variables and the parameter set with the smallest mean residual error were chosen. As a measure of accuracy to compare statistical data and simulated values the following objective functions were used (Papula, 1982):

to anthesis) and TSUM2 (thermal time requirement from anthesis to maturity) were fixed to values obtained from phenology and daily temperature observations. For the other parameters, a plausible range over which they vary was obtained from the literature (Srivastava et al., 2017; Ceglar et al., 2011; Boons Prins et al., 1993). By systematically sampling a value from the range of each parameter, a set containing all the parameters of the model was obtained and evaluated by comparing the simulated grain yield and the phenology to the corresponding observations. The systematic sampling and evaluation were performed for

a. The mean relative error MR as:

MR =

91

1 n

n

∑ i=1

(yi − x i ) xi

(1)

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Table 1 Crop parameters of LINTUL5 used in the study for maize varieties BH660 and BH540. Name

Description

Unit

Value BH660/ BH540

Crop parameters TSUM1 TSUM2 TBASEM TEFFMX TSUMEM RUE-0.0 RUE-1.25 RUE-1.50 RUE-1.75 RUE-2.0 SLATB-0.0 SLATB-1.0 SLATB-2.0 LAI critical

Temperature sum from emergence to anthesis Temperature sum from anthesis to maturity Lower threshold temperature for emergence Maximum effective temperature for emergence Temperature sum from sowing to emergence Radiation use efficiency at development stage 0 Radiation use efficiency at development stage 1.25 Radiation use efficiency at development stage 1.50 Radiation use efficiency at development stage 1.75 Radiation use efficiency at development stage 2.0 Specific leaf area at development stage 0 Specific leaf area at development stage 1.0 Specific leaf area at development stage 2.0 Critical leaf area beyond which leaves die due to self shading Maximum relative increase in LAI Initial rooting depth Maximum rooting depth Maximum rate of increase in rooting depth Initial total crop dry weight

RGRLAI ROOTDI ROOTDM RRDMAX TDWI

b.

1 n

n

∑ yi − xi

1 n

ha ha-1 day-1 m m m kg ha-1

0.02 0.1 2.0 0.012 5.0

2.4.1. Climate and soil data The climate data at the national scale, was made available from the National Aeronautics and Space Administration (NASA), Goddard Institute of Space Studies (https://data.giss.nasa.gov/impacts/agmipcf/ agmerra/), AgMERRA (Ruane et al., 2015) and consists of daily time series over the 1980–2010 period with global coverage of climate variables required for agricultural models (i.e., minimum and maximum temperature, solar radiation, precipitation, and Windspeed). These datasets are produced by combining state-of-the-art reanalyses (NASA's Modern-Era Retrospective analysis for Research and Applications, MERRA (Rienecker et al., 2011) and NCEP's Climate Forecast System Reanalysis, CFSR (Saha et al., 2010) with observational datasets from in situ observational networks and satellites. The dataset is stored at 0.25° × 0.25° horizontal resolution (~25 km). Values for relevant soil parameters for each soil layer down to maximum soil depth (sand, silt, clay, gravel content, cation exchange capacity, pH, organic carbon and bulk density) were extracted from the soil property maps of Africa at 1 km × 1 km resolution (http://www.isric.org/data/soil-propertymaps-africa-1-km) (Fig. 3). Other parameters such as soil water at field capacity, wilting point and saturation point and Van-Genuchten parameters were computed (Rawls et al., 1993).

(2)

i=1

Where n is the sample number, x is the observed and y is the simulated value. A value of 0 of mean residual error (ME) indicates no systematic bias between simulated and measured values. The mean relative error (MR) gives an indication of the mean magnitude of the error in relation to the observed value. Small values indicate little difference between simulated and measured values.

RMSE =

1000/ 680 990/760 8.0 30.0 56.0 3.2 2.5 2.2 2.0 1.4 0.03 0.02 0.02 4.0

2.4. Datasets used at national level

The mean residual error ME as:

ME =

°C day-1 °C day-1 °C °C °C g MJ-1 g MJ-1 g MJ-1 g MJ-1 g MJ-1 m2 g-1 m2 g-1 m2 g-1 m2 m-2

n

∑ (si − oi )2 i=1

(3) where, RMSE = Root mean square error. Si = simulated yield. Oi = Observed yield. n = Total number of observations. The daily weather data were recorded at the meteorological stations of Jimma situated at 1718 m above sea level. and provided by National Meteorological Agency (NMA) of Ethiopia. The required soil data for crop model were provided by National Soil Testing Center of Ethiopia (Table 2).

2.4.2. Crop yield and fertilizer application data Maize yields (Mg ha−1) and fertilizer application (Nitrogen and Phosphorus) rates over seven years (2004–2010) at administrative zone level were collected from the Central Statistical Agency, Ethiopia. These values were used for the model calibration at the national scale. 2.4.3. Approach for crop sowing date estimation The sowing period (i.e., from onset to the end of sowing) of maize which stretches out to about 90 days were extracted from crop calendar provided by FAO (2010) database and Global Yield Gap Atlas report (available at http://www.yieldgap.org/ethiopia). A rule, based on rainfall (The first day of spell of 7 days in which at least 20 mm of rain falls, on condition that no dry period of more than 7 days occurs in the following 30 days) was used to estimate sowing date within the sowing window because rainfall based approaches are currently in use in SubSaharan Africa (Srivastava et al., 2016; Frimpong and Kerr, 2015; Laux et al., 2008; Dodd and Jolliffe, 2001; Diallo, 2001).

Table 2 Sand, Silt, Clay and organic carbon (OC) content used as an input for model calibration at Jimma. Depth (cm)

Sand (%)

Silt (%)

Clay (%)

OC (%)

0–5 5–40 40–70 70–110 110–200

30.0 15.5 15.0 15.0 17.5

31.0 14.5 9.0 6.5 6.5

39.0 71.0 76.0 78.0 76.0

2.0 1.5 1.0 0.8 0.6

92

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Fig. 3. Simulated (water and nutrient-limited) versus observed (farmer's yield) maize yield averaged over 7 years (2004–2010) across 38 administrative zones in Ethiopia.

hectare (C). The optimal application level is the one with the highest difference between R and C, which implies the lowest positive marginal rate of return (MRR). MRRn is calculated as (RneCn) - (Rn-1-Cn-1). Finally, the value-cost ratio (VCR) is the ratio between a) the value of additional production due to fertilizer application and b) the total cost of fertilizer applied. VCR should be above 2 in low-income countries to accommodate risk aversion among farmers (Rashid et al., 2013).

2.5. Data analysis methods Rain water use efficiency (WUE, in kg mm−1) of maize yield was calculated as the ratio of grain yield (Y, in kg) to precipitation in crop growth period (P, in mm), and of maize total biomass was calculated as the ratio of aboveground biomass (AG, in kg) to P. Whereas, radiation use efficiency (RUE, in kg MJ−1) of maize yield was calculated as the ratio of grain yield (Y, in kg) to cumulated global solar radiation in the crop growth period (GR, in MJ) and in total biomass as the ratio of aboveground biomass to cumulated GR in the crop growth period.

3. Results

WUEY = Y P−1

(4)

3.1. Model calibration and evaluation

WUEAG = AG P−

(5)

RUEY = Y GR−

(6)

RUEAG = AG GR−1

(7)

The observed and simulated day of maturity under fertilized production treatment agreed well. In case of variety BH660, the model overestimated the DOM by one day compared with the observed value. Whereas, for variety BH540, observed and simulated DOM matches exactly (Table 3). Model overestimates the day of anthesis by 4 days for both the maize varieties (Table 3). The simulated grain yield of both the maize varieties is comparable to the corresponding observations where model overestimated the grain yield of variety BH660 by 4% and underestimated the grain yield of variety BH540 by −1.6% (Table 3). When applied at the 38 administrative zones of in Ethiopia, average simulated yields of the districts were in the range of observed yields averaged over 7 years (Fig. 3) with a root mean square error (RMSE) of 0.5 Mg ha−1. The discrepancy observed in the simulated yield could have resulted due to soil parameters used from ISRIC-WISE database, which refer to soil samples that are representative of large areas. Available soil data does not likely represent long-term cultivated, nutrient-depleted soils.

From the fitted yield and biomass response curve, we determined the marginal maize grain yield and stover increment for increasing applications of N and P to determine economically optimal levels of N and P application. Calculations are based on most recent price levels for maize grain (4.3 Birr1 kg−1 maize grains as the average for 2012–2014) as well as the commonly used fertilizer DAP and urea (14 Birr kg−1 N as the average for 2013–2014). Criteria are ‘revenues minus costs’, the marginal rate of return (MRR), and the value-cost ratio (VCR). 2.6. Economically optimal fertilizer application rates The results of profitability calculations to obtain optimal fertilizer application rates are summarized in Table 5. Revenues from maize sales per hectare (R) were calculated by grain yield levels multiplied by 4.28 thousand birr per metric ton, the average national farm-gate price for 2012–2014 (CSA, 2014). Fertilizer application with a constant 75:25 N/ P ratio was converted from nutrient equivalents to a composite of 52% DAP and 48% urea that was applied per hectare. For this hypothetical ‘mixture’ a composite price of 14 birr kg-1 was calculated, based on recent prices for DAP and urea reported by World Bank (2014, 2016). Multiplied with ‘fertilizer applied’ then results in fertilizer costs per

3.2. Simulated rain water use efficiencies in Maize grain yield and Stover The WUE in maize grain yield in AEZ 1 and 2, ranged from 2.1 kg mm−1 and 2.4 kg mm−1 under the unfertilized condition to a maximum of 8.9 kg mm−1 and 10.9 kg mm−1 respectively under application of 315 kg N ha−1 in combination with 105 kg P ha−1. In AEZ 3, WUE (4.0 kg mm−1) was higher under the unfertilized condition and also higher (11.5 kg mm−1) under 315 kg N ha−1 in combination with 105 kg P ha−1, when compared with AEZ 1 and 2 (Fig. 4). On the other hand, WUE in maize stover ranged from 1.5 kg mm−1 and 1.7 kg mm−1 under unfertilized conditions to 6.8 kg mm−1 and 7.7 kg mm−1 under

1 Exchange rate in year 2014/15: 1 USD = 20.1 Birr (source: National Bank of Ethiopia)

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Table 3 Observed and simulated Grain yield, Day of Anthesis (DOA), Day of Maturity (DOM), mean residual error (ME), and mean absolute error (MR), for the maize variety BH660 and BH540 at Jimma in Ethiopia. Variety

BH660 BH540

Site

Jimma Jimma

Treatment

Fertilized Fertilized

DOA

DOA

DOM

DOM

Grain Yield

Grain Yield

ME

MR

Observed

Simulated

Observed

Simulated

Observed

Simulated

Grain Yield

Grain Yield

(Mg ha-1)

(Mg ha-1)

(Mg ha-1)

(%)

10.0 6.2

10.4 6.1

0.4 −0.1

4.0 −1.6

70 71

74 75

160 150

161 150

Fig. 4. Rain water Use Efficiency (WUE) in Maize grain yield (a) and Stover (b) under different rates of Nitrogen (N) and Phosphorus (P) application in three AgroEcological Zones of Ethiopia.

315 kg N ha−1 combined with 105 kg P ha−1 application rates in AEZ 1and 2 respectively. In AEZ 3, the WUE in stover production ranged from 2.8 kg mm−1 under the unfertilized condition to 9.4 kg mm−1 under 315 kg N ha−1 combined with 105 kg P ha−1 application rate (Fig. 4). The increase of WUE in maize yield and stover across the three AEZs stagnated at 315 kg N ha−1 in combination with 105 kg P ha−1 application regardless of the WUE values under unfertilized condition.

unfertilized condition and was lowest (2.2 kg MJ−1) compared with AEZ 1 and 2, under 315 kg N ha−1 in combination with 105 kg P ha−1 (Fig. 5). On the other hand, RUE in maize stover ranged from 0.5 kg MJ−1 under unfertilized conditions to 2.0 kg MJ−1and 2.1 kg MJ−1 under 315 kg N ha−1 combined with 105 kg P ha−1 application in AEZ 1, 2 respectively. In AEZ 3, RUE was 0.6 kg MJ−1 under unfertilized conditions and was 1.8 kg MJ−1, hence, lower compared with AEZ 1 and 2, under 315 kg N ha−1 in combination with 105 kg P ha−1 application The increase of RUE in maize grain yield and stover across the three AEZs reached plateau at 315 kg N ha−1 combined with 105 kg P ha−1 application (Fig. 5).

3.3. Simulated Radiation use efficiencies in Maize grain yield and Stover The RUE in maize grain yield in AEZ 1 and 2, ranged from 0.6 kg MJ−1 and 0.7 kg MJ−1 under unfertilized conditions to a maximum of 2.7 kg MJ−1 and 3.0 kg MJ−1 respectively under 315 kg N ha−1 in combination with 105 kg P ha−1 application. Whereas, in AEZ 3, the estimated RUE was 0.8 kg MJ−1 under the

Fig. 5. Radiation Use Efficiency (RUE) in Maize grain yield (a) and Stover (b) under different rates of Nitrogen (N) and Phosphorus (P) application in three AgroEcological Zones of Ethiopia. 94

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Table 4 Simulated Maize grain yield and stover biomass (in Mg ha−1), Accumulated Rainfall (in mm), Global Radiation (in MJ) in the crop growth period, Rain water use efficiency (WUE, in kg mm−1), and Radiation use efficiency (RUE, in kg MJ−1) for maize grain yield and Stover under current Nitrogen (N, in kg ha−1), Phosphorus (P, in kg ha−1) application rates, Water stress Indices (WSI) and Nitrogen stress Indices (NNI) in three Agro-Ecological regions (AEZs) in Ethiopia. AEZs

1 2 3

Simulated

Simulated

Grain Yield

Grain Yield

Stover

Stover

Grain Yield

Stover Biomass

WUE

RUE

WUE

RUE

2.3 2.3 2.1

1.6 1.7 1.5

2.55 2.80 4.21

0.77 0.77 0.83

1.85 2.02 3.00

0.55 0.56 0.58

Rainfall

Radiation

N

P

WSI

NNI

962.7 836.1 506.8

3031.2 2981.4 2652.0

15.1 8.7 5.0

4.7 2.7 1.6

0.96 0.99 0.81

0.83 0.82 0.87

4. Discussion

3.4. Water and Radiation use efficiencies under current maize production systems

The WUE in maize grain and stover production under current N and P application rates are varying across the AEZs with the highest values in AEZ 3 amounting to 4.21 kg mm−1 in maize grain and 3.0 kg mm−1 in the maize stover compared with the AEZ 1 and 2 (Table 4) under current NeP fertilizer application rates. The higher WUE in AEZ 3 was associated with the lowest rainfall accumulated in the crop growing period. Our finding goes in line with the findings of Getnet et al. (2016), where, WUE for maize yield varied from 2.7 to 4.3 kg mm−1 in Central Rift Valley (CRV) of Ethiopia and the higher WUE was estimated where seasonal rainfall was lower. The AEZ 3 is characterized by lower maize yield levels but higher associated WUE compared with the AEZ 1 and 2, where water was not limiting, implying that in AEZ 3, any additional water could result in a yield increase compared with the higher rainfall areas AEZ1 and 2. Secondly, the estimated WUE was lowest in AEZ 1, despite having highest rainfall amount (Table 4) in the crop growth period. This demonstrates that rainfall distribution and sufficient amount of nutrients is of great importance in obtaining good crop water use. Due to the strong growth limitation by nutrients in AEZ1, the crop transpired only a small amount of available water and a significant amount of water in AEZ1 was unproductively lost as soil evaporation, runoff or deep percolation which all lead to reduced rain water productivity. The same phenomenon has been reported by Miriti et al. (2012). The highest WUE of 11.5 kg mm−1 and 9.4 kg mm−1 in maize grain yield and stover respectively was estimated with the application of 315 kg N ha−1 + 105 kg P ha−1 in AEZ 3 having lowest rainfall amount in the maize growth period compared with the AEZ 1 and 2 (Fig. 4; Table 4). Higher WUE in crops well supplied with N and P was the result of large grain yield and stover increments. The ecophysiological processes underlying the interaction between grain and stover biomass

The estimated rain water use efficiency in maize grain yield under current fertilizer application rates were 2.55, 2.80, and 4.21 kg mm−1 in AEZ 1, 2 and 3 respectively. Whereas, in maize stover, the efficiency was 1.85, 2.02, and 3.0 kg mm−1 in AEZ 1, 2 and 3 respectively (Table 4). The estimated Radiation use efficiency in maize grain yield was 0.77, 0.77, and 0.83 kg MJ−1 in AEZ 1, 2, and 3 respectively under current fertilizer application rates (Table 4). Whereas for maize stover, it was 0.55, 0.56, and 0.58 kg MJ−1 in AEZ 1, 2, and 3 respectively. Regression of WUE against grain yield in all the AEZ 2 and 3 shows good relation having R2 values of 0.53 and 0.33 respectively. Whereas, in AEZ 1, grain yield was poorly correlated with the WUE (Fig. 6). Similar results were estimated for maize Stover (refer Fig. 7). A positive correlation between grain yield, Stover and RUE were estimated across the AEZs analyzed under current fertilizer application rates (Fig. 7). Results shown in Table 5 indicate optimal fertilizer application levels of 225 N + 75P under average national conditions and prices, at two-thirds of the simulated application range. In regions where water availability tends to constrain grain yields in addition to the nutrient deficit, optimal application rates are slightly lower at 180 N + 60P. A graphical sensitivity analysis of these average results is offered in Fig. 8 where maize grain revenues minus fertilizer costs are on display for alternative price constellations. While 20% higher maize prices do not imply higher application rates, 20% lower prices suggest a reduction of fertilizer application rates in water-constrained regions of about 25%. Low grain prices combined with doubled fertilizer prices further reduce optimal application rates in both average and water constrained regions.

Fig. 6. Relationship between observed Maize grain yield to Rain water use efficiency (WUE) and Radiation use efficiency (RUE) in three Agro-Ecological Zones (AEZs) in Ethiopia. 95

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Fig. 7. Relationship between Maize stover to Rain water use efficiency (WUE) and Radiation use efficiency (RUE) in three Agro-Ecological Zones (AEZs) in Ethiopia.

Maize seasonal ET increments in response to N supply were closely associated with seasonal Intercepted Photosynthetic Active Radiation (iPAR) increments (Fig. 10). In the water-limited environment (i.e., AEZ 3), lower ET increments were evident in response to iPAR increments due to N supply; indicating further limitations possibly due to stomatal closure in response to water deficiencies (Bennett et al., 1989). The close association between seasonal ET and iPAR responses to N supply provides a possible reason for the discrepancies among published work accounting for ET response to N supply (Hernandez et al., 2015; Teixeira et al., 2014; Barbieri et al., 2012; Adamtey et al., 2010). In water-limited environments, low ET increments due to increasing N supply can be expected. The results for economically optimal application rates suggest a robust case for a substantial increase in fertilizer use in maize in most Ethiopian regions (Table 5). Across all regions, the optimal application rates are almost constant irrespective of the relationship between fertilizer and maize grain prices except for the scenario with an increase of fertilizer price by 50% and a reduction of maize grain process by 19%. In the water constraint regions (AEZ3), the economically optimal application rate is more sensitive to changes in price ratios and hence these regions are more vulnerable to changes in markets. Even though the maize prices used for the analysis are relatively high in historical perspective, the same is true for fertilizer prices, the level of which was correlated to crop price levels in the past. So a possible future decrease in world price levels for maize would most likely cause a corresponding drop in fertilizer prices, both of which would heavily influence price trends on Ethiopia's domestic markets. From that perspective, the constellation of lowered maize prices combined with doubled fertilizer prices as presented above is indeed a worst case scenario which is unlikely to happen (Fig. 8).

to N supply were well documented in the literature (e.g., Hernandez et al., 2015; Boomsma et al., 2009). Briefly, the positive relationship between shoot biomass and N supply was associated with an increased leaf area (Bennett et al., 1989). Greater RUE (Fig. 5) (Sinclair and Muchow, 1999) and greater leaf photosynthesis (Echarte et al., 2008) could also be explaining the increased WUE at high N + P fertilizer rates in our study. This is illustrated by the strong linear relationship between WUE and RUE (Fig. 9.). Asare et al. (2011) reported WUE in grain yield and biomass production of 14.6 kg mm−1 and 10.0 kg mm−1 respectively under 275 kg NPK ha−1 on cumulative actual evapotranspiration in the crop growing season in Ghana. Similar WUE values in grain yield ranging from 11.0 kg mm−1 to 18.0 kg mm−1, 9.3 kg mm−1 to 13.8 kg mm−1 and 11.4 kg mm−1 to 14.4 kg mm−1 have been reported by Tijani et al. (2008), El-Tantawy et al. (2007), and Meena and Bhimavat (2009), respectively, for maize grown under rain-fed conditions. Teixeira et al. (2014) also reported a positive effect of N application on maize biomass RUE, on an average, under fully fertilized (250 kg ha−1) conditions were water was used by 20% more efficient than under unfertilized condition. The findings of the current study indicate that WUE in grain yield and stover can be increased by 172% to 363%, and 230% to 352% respectively depending upon the AEZ with increased fertilizer application rates compared with the WUE under non-fertilized conditions. Getnet et al. (2016) also reported that WUE can be increased by 3 to 6 times for maize with nutrient management in CRV, Ethiopia. The increased response to water in the presence of higher nutrients could arise mainly from increased capture efficiency as documented by Giller et al. (2006). Similarly, there was a positive effect of fertilizer application on RUE in maize grain yield and stover. The highest RUE of 3.0 kg MJ−1 and 2.1 kg MJ−1 in maize grain yield and stover respectively was estimated with the application of 315 kg N ha−1 + 105 kg P ha−1 in AEZ 2. Teixeira et al. (2014) also reported the positive implication on N fertilizer on the RUE of maize biomass ranged from 1.0 g DM MJ−1 under the unfertilized condition to up to 1.4 g DM MJ−1 at the full fertilizer use (i.e., 250 kg ha−1). RUE in grain yield and stover production can be increased to the tune of approximately 177% to 362%, and 216% to 351% respectively depending upon the AEZs with increasing application rates of N and P compared with the RUE values under unfertilized conditions. The increase in RUE in high nitrogen conditions could be explained by the close relationship between leaf nitrogen contents and photosynthesis. Van Keulen and Seligman (1987) found a linear relationship between the rate of CO2 assimilation and nitrogen concentration in the leaf.

5. Conclusion Resource use efficiencies of maize production systems under different rates of mineral fertilizer (N + P) and their variability across the administrative zones of Ethiopia were analyzed. An estimation of water use efficiency (WUE) and radiation use efficiency (RUE) will allow farmers to adapt their production practices to climate variability under rain-fed and low-input production systems.The three hypotheses tested in this study cannot be rejected except the one where we hypothesized that WUE and RUE would reach its maximum (i.e., plateau) at different mineral fertilizer application rates depending on the AEZs. Conversely, the plateau for WUE and RUE were reached at approximately the same fertilizer application rate (i.e., 315 N + 105P kg ha−1) in all the three AEZs. There were positive impacts of nutrient application on maize rain 96

97

Water-constrained regions Maize grain yield in kg ha−1 Average revenue (R) at 4.3b/kg maize (1000 birr per ha) Fertil. appl. (composite of 52% DAP and 48% urea, in kg ha−1) Fertilizer cost (C), 1000 b/ha (at 14 b/ kg DAP-urea composite) Fertilizer costs in % of total revenue R-C in 1000 birr per ha MRR at 14 b/kg for fert. & 4.3 birr/kg for maize (1000 birr/ha) VCR for 4.3 birr/kg maize

National average Maize grain yield in kg ha−1 Average revenue (R) at 4.3b/kg maize (1000 birr per ha) Fertil. appl. (composite of 52% DAP and 48% urea, in kg ha−1) Fertilizer cost (C), 1000 b/ha (at 14 b/ kg DAP-urea composite) Fertilizer costs in % of total revenue R-C in 1000 birr per ha MRR at 14 b/kg for fert. & 4.3 birr/kg for maize (1000 birr/ha) VCR for 4.3 birr/kg maize

N + P in kg ha−1

8.1 0.0

1895 8.1

8.2 0.0

1914 8.2

0

0 N + 0P

2.0 15.6 10.9 1.4 2.4

0.9 8.4 9.5 1.4 2.6

3.5

3.8

143

13.2 13.3 2.6

7.6 10.6 2.4

62

2.0

0.9

3016 12.9

143

62

2424 10.4

3566 15.3

60

45 N + 15P (kg ha−1)

2687 11.5

26

20 N + 6.6P (kg ha−1)

2.2

23.6 13.0 2.1

4.0

287

3982 17.0

3.3

18.7 17.5 4.2

4.0

287

5024 21.5

120

90 N + 30P (kg ha−1)

35.2 14.8 0.1 1.8

2.1

8.0

573

5325 22.8

2.8

26.2 22.6 1.6

8.0

573

7154 30.6

240

180 N + 60P (kg ha−1)

29.2 14.6 1.6

6.0

430

4829 20.7

3.1

22.3 20.9 3.4

6.0

430

6299 27.0

180

135 N + 45P (kg ha−1)

1.6

42.0 13.8 −0.9

10.0

717

1.4

49.4 12.4 −1.5

12.1

860

5702 24.4

2.1

2.4 5581 23.9

35.9 21.5 −1.1

12.1

860

7849 33.6

360

270 N + 90P (kg ha−1)

30.7 22.6 0.0

10.0

717

7635 32.7

300

225 N + 75P (kg ha−1)

1.2

57.1 10.6 −1.8

14.1

1003

5754 24.6

1.8

41.3 20.0 −1.6

14.1

1003

7950 34.0

420

315 N + 105P (kg ha−1)

Table 5 Calculation of economically optimal levels of fertilizer application, distinguishing between the national average of regions and regions where water availability is constraining yields in addition to nutrient deficits. The primary criterion is the marginal rate of return (MRR); optimal application levels are those where MRR is still positive. The secondary criterion is the value-cost ratio (VCR) which should be above 2. Columns closest to the optimal application level are grey-shaded in the MRR and VCR rows.

A.K. Srivastava et al.

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Fig. 8. Revenues from maize grain sales minus fertilizer costs per hectare at different application rates and price constellations. Numbers indicate maxima, i.e. optimal application rates.

Fig. 9. Relationship between Radiation use efficiency (RUE) and Rain water use efficiency (WUE) in Maize grain yield (a) and Stover (b) in the three Agro-Ecological zones in Ethiopia.

increase of maize productivity per unit of water applied, leading to a reduced vulnerability of maize production to water deficit. The results indicate an economically optimal fertilizer application rate of 225 N + 75P kg ha−1 for maize production systems under current average price rations at the national scale, somewhat above the

water use efficiency and radiation use efficiency in the three AEZs of Ethiopia. However, the magnitude of increase in these efficiencies was higher in maize grain yield compared with the stover biomass. The result presented in this study also delineates the zones (i.e., AEZ 3) where the application of additional water would provide the highest

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Fig. 10. Relationship between mean crop Actual Evapotranspiration (AET) and iPAR increments due to N + P supply in AEZs 1, 2 and 3.

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