Recent Trends in the Yield-Nutrient-Water Nexus in Morocco

Abstract

Climate change is impacting environmental systems including agriculture. In Morocco, declining precipitation and increasing temperatures are negatively impacting crop yields. Consequently, crop yields in Morocco are now dependent on nutrient and water management. Most studies have focused on experimentation through fertilizer application and irrigation without any attention to the intrinsic linear relationships that exist between crop yields, fertilizers, and agricultural water withdrawal. The time series agricultural water withdrawal data were collected from AQUASTAT for the period 1990-2022 while data on nitrogen, phosphorous, and potash fertilizers were collected from FAOSTAT. Yield data for maize, barley, sorghum, and wheat were also collected from FAOSTAT. The data were analyzed using two machine learning models fitted through multiple linear regression. The key results show that for the three fertilizers, phosphates tend to have the strongest impacts and cause changes in crop yield as seen in the context of wheat. When both fertilizers and agricultural water withdrawal are fitted against yield, agricultural water withdrawals tend to have a strong relationship with yields. This work has helped us to identify which crops and management options need to be valorized in terms of increased access to nutrients and water.

1. Introduction

Across Africa, agriculture is essentially rainfed; however, due to climate change and variability, precipitation is not often sufficient for crop production. Consequently, alternative sources of water need to be harnessed for crop production. The recent Intergovernmental Panel for Climate Change Report (IPCC) [1] has noted that across Africa, temperatures will continue to increase while the pattern of precipitation will continue to be variable regionally. For example, the various Representative Concentration Pathway (RCP) scenarios show that in terms of precipitation, north Africa including Morocco and southern Africa will continue to witness declines in precipitation, especially during the growing season of crops while west, central and east Africa will witness high intensity and poorly distributed precipitation. To ensure food security for over nine billion people by 2050, agricultural production at a global scale must increase by between 70 and 100% [2]. If care is not taken, global production as per recent projections for the period 2006–2050 will decline from 2.2 to 1.1% [3]. In Africa, during the same period, the annual growth rate of food production is projected to decline from 3 to 2.1% [3]. These downward trends in crop production are likely to have devastating effects; some of these constraints are crop yield related [4, 5, 6, 7].

Amidst these constraints imposed by climate on agriculture, across Africa and around the world, crop production has become increasingly based on water withdrawals/irrigation and fertilisation [8]. Currently, only about 15% of Moroccan agriculture is irrigated while about 85% is dependent on rainfall. Within this context of insufficient rainfall and an arid environment, crop yields are thus subjected to enormous stress [8]. Food insecurity and lagging food production in Africa are shifting attention to irrigation. Irrigation and fertilisation are among key investments and technical inputs that are needed to revamp crop production in Africa [8]. Therefore, there is an increased emphasis on the valorisation of water withdrawal and fertilisation in a bid to adapt to the limits imposed by climate change. Water withdrawal represents the total amount of water extracted from river, soil moisture, ground water and precipitation and used to enhance crop productivity. To respond to these stressors, communities, governments and other organisations across Africa are making efforts to make African agriculture resilient through sustainable withdrawal of water and fertilisation [8, 9]. Across Africa, organisations, such as the African Development Bank (AfDB), United Nations Reduction of Emissions from Deforestation and Forest Degradation (UN-REDD+) and Office Chérifien des Phosphates (OCP) Africa/Foundation, are now having programmes that aim at enhancing agriculture by the valorisation of water withdrawal and fertilisation [10, 11, 12].

Morocco is located on the North-West edge of the African continent, between latitudes 21°N and 36°N and longitudes 1°W and 17°W. The country has a total area of nearly 711,000 km². This includes 2934 km of coast on the Atlantic Ocean to the West and 512 km of coast on the Mediterranean Sea to the North. It borders Algeria to the East and South-East and Mauritania to the South-West. According to the latest 2014 census, its population is estimated at nearly 34 million people. Morocco is characterised by a wide variety of topographies ranging from mountains and plateaus to plains, oases and Saharan dunes. For this reason, the country experiences diverse climatic conditions with large spatial intra- and inter-annual variability of precipitation. Morocco faces irregular rain patterns, cold spells and heat waves increasingly resulting in droughts, which significantly affects agriculture [13, 14].

Morocco’s policy to modernise its agriculture and make it profitable for small- and medium-sized farmers and for the Moroccan economy in general is outlined in the “Green Morocco Plan” (GMP) that was established in 2008–2018 [13, 14], now replaced by the “Generation Green Plan” (GGP) to cover the next decade [15]. These plans have established goals to assist farmers to access water for agriculture as well as other agricultural inputs such as fertilisers (Agence Pour le Développement Agricole (i.e. the Agricultural Development Agency (ADA)) [16, 17]. Invariably, these policies have had positive effects on the Moroccan agricultural economy envisioned through increased agricultural production epitomised through greater access to fertilisers, water for irrigation and increased mastery of the irrigation process.

Due to this emphasis on water withdrawal and fertilisation, research interest has also been tilted towards this direction. However, most of the studies have focused on aspects of agricultural management in the context of experimental agriculture. The goal has so far been to investigate through process-based models of how various scenarios of irrigation and fertilisation impact crop response [18, 19, 20, 21, 22, 23, 24]. To the best of our knowledge, this is the first study that uses historical data to assess recent trends in the yield of maize, barley, sorghum and wheat at a national scale in Morocco. The national scale approach adopted is essentially aspatial and seeks to provide insights into how national historical yields of the concerned crops respond to nutrient and water management. Therefore, this work assesses recent trends in the relationship between yield (dependent variable) and fertilisers (nitrogen, phosphate and potash-independent variables) on the one hand and between crop yield (dependent variable) and water withdrawals and fertilisers (independent variable) on the other. This approach is used to determine the effect that fertilisers and agricultural water withdrawal have on the selected crops to determine where emphasis can be made to improve food production. Therefore, this work seeks to assess at a national scale the recent trends in the relationships between the yields of the various crops and how they interact with aspects of nutrient and water management. This approach will improve understanding of these recent trends at the national scale. It will also go a long way in providing vital information necessary for closing yield gaps.

2. Materials and methods

2.1 Data collection

To be able to assess the recent trends in the crop yield, fertiliser and water withdrawal nexus, historical data on the key variables (crop yield, agricultural water withdrawal, nitrogen, phosphate and potash) were collected. The methodological steps employed in this study are presented in Figure 1. The collected time series data were selected for the period 1990–2020 for which data were available. For the years 2012–2022, the missing data were obtained by linear interpolation (see Section 2.3 below). Less than an insignificant 10% of the data were missing. Yield data were collected on maize, barley, sorghum and wheat (hectograms per hectare per year (hg/ha/year)) and were collected from FAOSTAT (FAO’s agricultural database) [25]. The historical data on fertilisers for nitrogen, phosphate and potash (tons/year) were also collected from FAOSTAT (FAO’s fertiliser statistics) [25]. The fertiliser data presented here represent fertiliser data used in agriculture in Morocco and reported by the government of Morocco to the FAO). Agricultural water withdrawal data were downloaded from AQUASTAT (FAO’s irrigation and water withdrawal database) [26]. The data from FAOSTAT and AQUASTAT are observed data that are collected from individual countries and reported by the FAO. In cases of missing data, the FAO estimates the missing data but, in most cases, the data are observed records. Agricultural water withdrawal is used here as a proxy for irrigation. It includes surface water, ground water and non-conventional water that has been used for agriculture. The agricultural water withdrawal data are often reported by governments to the FAO.

Figure 1.

Methodological steps employed.

2.2 Data analysis

The time series historical data were collected for the period 1990–2020. However, except for agricultural water withdrawal that had complete data from 1961 to 2020, all the other variables (fertilisers and yields) covered the data from 1990 to 2020. Therefore, the analysis was performed with the data that spanned the period 1990–2020. The data were analysed using the Statistical Package for the Social Sciences (SPSS) software while the graphs were produced using excel software. The inferential statistics used included simple linear regression, multiple linear regression, coefficient of determination (R²), p-values (significant at (p ≤ 0.05), t-values, linear interpolation and the related standard errors.

2.3 Linear interpolation

For homogeneity, the linear interpolation approach was used to estimate the missing data for yield and fertilisers for the period 2021–2022. The linear interpolation procedure used is defined below (Eq. (1)). The linear interpolation method is often used to estimate the unknown values or missing data through the known values or available data.

$$\text{Yyrnr}=\text{y1}+{\left(\left(\mathrm{x}–\text{x1}\right)/\left(\text{x2}-\text{x1}\right)\right)}^{\ast }\left(\text{y2}-\text{y1}\right)$$E1

where Yyrnr represents the unknown values or missing data for maize, barley, sorghum and wheat yield as well as missing values for nitrogen, phosphate and potash fertilisers, x represents the known values for maize, barley, sorghum and wheat yields as well as missing values for nitrogen, phosphate and potash fertilisers, x1 and y1 are the coordinates that are below the known x value and x2 and y2 are the coordinates that are above the known x value.

Once linear interpolation was finalised, a complete dataset for maize, barley, sorghum and wheat yields as well as missing values for nitrogen, phosphate and potash fertilisers were obtained covering the period 1990–2022.

2.4 Multiple linear regression

To assess the relationship between yield, agricultural water withdrawal and fertilisers, multiple linear regression was used to identify which independent variable impacts yield the most. Two regressions were fitted for each crop (Eqs. (2)–(9)), one based on the relationship between yield and nitrogen, phosphate and potash and one based on the relationship between yield, fertilisers and agricultural water withdrawal. This procedure was repeated for each crop as follows:

2.4.1 Maize

$$\text{Ymaize}\_\mathrm{yf}=\mathrm{a}+\text{bX1}+\text{bX2}+\text{bX3}+\in$$E2

where X1, X2, X3 (for nitrogen, phosphate and potash, respectively) are the explanatory variables and Ymaize_yf is the dependent variable (maize yield). The slope of the line is b, and a is the intercept (the value of y when x = 0) and ∈ is the error term.

$$\text{Ymaize}\_\mathrm{ywf}=\mathrm{a}+\text{bX1}+\text{bX2}+\in$$E3

where X1, X2 (agricultural water withdrawal and fertilisers, respectively) are the explanatory variables and Ymaize_ywf is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0) and ∈ is the error term.

2.4.2 Barley

$$\text{Ybarley}\_\mathrm{yf}=\mathrm{a}+\text{bX1}+\text{bX2}+\text{bX3}+\in$$E4

where X1, X2, X3 (nitrogen, phosphate and potash, respectively) are the explanatory variables and Ybarley_yf is the dependent variable (barley yield). The slope of the line is b, and a is the intercept (the value of y when x = 0) and ∈ is the error term.

$$\text{Ybarley}\_\mathrm{ywf}=\mathrm{a}+\text{bX1}+\text{bX1}+\in$$E5

where X1, X2 (agricultural water withdrawal and fertilisers, respectively) are the explanatory variables and Ybarley_ywf is the dependent variable (barley yield). The slope of the line is b, and a is the intercept (the value of y when x = 0) and ∈ is the error term.

2.4.3 Sorghum

$$\text{Ysorghum}\_\mathrm{yf}=\mathrm{a}+\text{bX1}+\text{bX2}+\text{bX3}+\in$$E6

where X1, X2, X3 (nitrogen, phosphate and potash, respectively) are the explanatory variables and Ysorghum_yf is the dependent variable (sorghum yield). The slope of the line is b, and a is the intercept (the value of y when x = 0) and ∈ is the error term.

$$\text{Ysorghum}\_\mathrm{ywf}=\mathrm{a}+\text{bX1}+\text{bX2}+\in$$E7

where X1, X2 (agricultural water withdrawal and fertilisers, respectively) are the explanatory variables and Ysorghum_ywf is the dependent variable (sorghum yield). The slope of the line is b, and a is the intercept (the value of y when x = 0) and ∈ is the error term.

2.4.4 Wheat

$$\text{Ywheat}\_\mathrm{yf}=\mathrm{a}+\text{bX1}+\text{bX2}+\text{bX3}+\in$$E8

where X1, X2, X2 (nitrogen, phosphate and potash, respectively) are the explanatory variables and Ywheat_yf is the dependent variable (wheat yield). The slope of the line is b, and a is the intercept (the value of y when x = 0) and ∈ is the error term.

$$\text{Ywheat}\_\mathrm{ywf}=\mathrm{a}+\text{bX1}+\text{bX2}+\in$$E9

where X1, X2 (agricultural water withdrawal and fertilisers, respectively) are the explanatory variables and Ywheat_ywf is the dependent variable (wheat yield). The slope of the line is b, and a is the intercept (the value of y when x = 0) and ∈ is the error term.

3. Results

3.1 Fertiliser use in Moroccan agriculture

In terms of the use of nitrogen, phosphate and potash fertilisers in African agriculture, Morocco is among the highest users as can be seen in Figure 2. For example, in the context of nitrogen fertilisers used in agriculture, Morocco records about 1,155332.34 tons together with other Africa countries like Egypt, Nigeria, Ethiopia and South Africa (Figure 2a). In terms of phosphate fertiliser usage in agriculture, Morocco still ranks among the top users in Africa. The country records about 410,513.54 tons together with countries like Algeria, Egypt, Nigeria, Ethiopia, Kenya and South Africa (Figure 2b). In the context of potash, Morocco still ranks among the highest users in Africa, using about 355,225.81 tons in its agriculture together with other leading countries such as Egypt, Nigeria and South Africa (Figure 2c).

Figure 2.

Agricultural fertilisers (tons) used in Africa (1990–2020). (a) Nitrogen, (b) phosphate and (c) potash. Source: Developed by authors from FAOSTAT.

3.2 The evolution of crop yields, fertilisers (nutrients) and agricultural water withdrawals in Morocco

In the context of the four crops under consideration in this study (maize, barley, sorghum and wheat), the evolution over time is one that has been highly variable and fluctuating between 1990 and 2022 (Figure 3a). Despite this observation, wheat records the highest yield during this period followed by barley, maize and sorghum (Figure 3a). In terms of fertilisers/nutrients used in agriculture in Morocco, the results are consistent with those reported in Figure 2a, b and c. For example, in Morocco, though also slightly variable as yields, fertilisers/nutrients are generally dominated by nitrogen fertilisers that record the highest values throughout the series; this is followed by phosphate and potash, respectively (Figure 3b). In terms of agricultural water withdrawal, the historical data show a slight decline between 1990 and 2022 (Figure 3c).

Figure 3.

Evolution of (a) yields, (b) fertilisers/nutrients and (c) water withdrawal for irrigation in Morocco.

3.3 Scatter plots and linear regression outputs of the relationship between fertilisers/nutrients and maize yields in Morocco

The scatter plots depict the linear relationship between maize yields as the dependent variable and fertilisers as the independent variable. In the context of nitrogen fertilisers used in maize cultivation, it can be observed that an R² of 0.0014 (0.14%) is obtained. This implies that only 0.14% of changes in maize yield can be explained by changes in nitrogen fertiliser application (Figure 4a). In terms of phosphate fertilisers used in maize cultivation, it can be observed that an R² of 0.07 (7%) is obtained. This implies that only 7% of changes in maize yield can be explained by changes in phosphate fertiliser application (Figure 4b). Considering potash fertilisers used in maize cultivation, it can be observed that an R² of 0.041 (4.1%) is obtained. This implies that only 4% of changes in maize yield can be explained by changes in potash fertiliser application (Figure 4c). From these linear trends, it can be observed that the changes in maize yields cannot be explained by changes in fertiliser application. However, phosphate fertilisers seem to outbid the other nutrients as they record relatively higher R² of 7%. Phosphate fertilisers tend to explain more of the changes in maize yield as depicted by the R² of 7%, a statistic that is relatively higher than those recorded for nitrogen and potash.

Figure 4.

Scatter plots of maize yield against (a) nutrient nitrogen, (b) nutrient phosphate and (c) nutrient potash.

The results from the scatter plots (Figure 4a, b and c) are consistent with multiple linear regression outputs (Table 1). This is observed, as phosphate fertilisers tend to record the lowest p-value of 0.15 and the highest t-value of 1.44. This is followed by potash (p-value = 0.34, t-value = −0.95) and lastly nitrogen (p-value = 0.72, t-value = −0.35). Even though nitrogen represents higher levels of application in Moroccan agriculture, when it comes to maize, phosphates tend to explain more of the changes in maize yield than the other fertilisers. When the linear relationship between maize yield as the dependent variable and agricultural water withdrawal and fertilisers as the independent variables is considered, it is observed that agricultural water withdrawal records the lower p-value (0.11) and the highest t-value (−1.63) (Table 2). In relative terms, agricultural water withdrawal tends to explain more of the changes in maize yield when compared to fertilisers.

	Coefficients	Standard error	t Stat	P-value	Lower95%	Upper95%	Lower95.0%	Upper 95.0%
Intercept	6591.479609	2684.042776	2.455803	0.020287	1101.99576	12080.963	1101.995763	12080.9635
X1: Nitrogen	−0.003664534	0.010421889	−0.35162	0.727666	−0.0249797	0.0176506	−0.02497969	0.01765062
X2: Phosphate	0.015876546	0.01095039	1.449861	0.157827	−0.0065195	0.0382726	−0.00651952	0.03827261
X3: Potash	−0.01932795	0.020283127	−0.95291	0.348512	−0.0608116	0.0221557	−0.0608116	0.0221557

Table 1.

Linear regression outputs of the relationship between maize yield and fertilisers.

	Coefficients	Standard error	t Stat	P-value	Lower95%	Upper95%	Lower95.0%	Upper95.0%
Intercept	21890.29571	10197.37344	2.14666	0.040026	1064.4808	42716.111	1064.48081	42716.11061
X1: Water withdrawal	−1410.92082	864.2597603	−1.63252	0.113024	−3175.9747	354.13308	−3175.97472	354.1330848
X:2 Fertilisation	−0.00292644	0.007058972	−0.41457	0.681406	−0.0173428	0.0114899	−0.01734278	0.011489907

Table 2.

Linear regression outputs of the relationship between maize yield, water withdrawal and fertilisers.

3.4 Scatter plots and linear regression outputs of the relationship between fertilisers/nutrients and barley yields in Morocco

The scatter plots depict the linear relationship between barley yield as the dependent variable and fertiliser as the independent variable. In the context of nitrogen fertilisers used in barley cultivation, it can be observed that an R² of 0.0005 (0.05%) is obtained. This implies that only 0.05% of changes in barley yield can be explained by changes in nitrogen fertiliser application (Figure 5a). In terms of phosphate fertilisers used in barley cultivation, it can be observed that an R² of 0.076 (7.6%) is obtained. This implies that only 7.6% of changes in barley yields can be explained by changes in phosphate fertiliser application (Figure 5b). Considering potash fertilisers used in barley cultivation, it can be observed that an R² of 0.0002 (0.02%) is obtained. This implies that only 0.02% of changes in barley yield can be explained by changes in potash fertiliser application (Figure 5c). Phosphate fertilisers outbid the other nutrients as they record the highest R² of 7.6% and the lowest p-values of 0.11. Phosphate fertilisers tend to explain more of the changes in barley yield as depicted by the R² of 7.6%, a statistic that is much higher than those recorded for nitrogen and potash.

Figure 5.

Scatter plots of barley yield against (a) nutrient nitrogen, (b) nutrient phosphate and (c) nutrient potash.

The results from the scatter plots (Figure 5a, b and c) are consistent with linear regression outputs (Table 3). This is observed as phosphate fertilisers tend to record the lowest p-value of 0.11 and the highest t-value of 1.64. This is followed by nitrogen (p-value = 0.63, t-value = −0.48) and lastly potash (p-value = 0.77, t-value = 0.29). Even though nitrogen represents higher levels of application in Moroccan agriculture, when it comes to barley, phosphates tend to explain more of the changes in barley yields. Still, none of these nutrients correlates significantly with barley yields. When the linear relationship between barley yields as the dependent variable and agricultural water withdrawal and fertilisers as the independent variables is considered, it is observed that agricultural water withdrawal records the lower p-value (0.15) and the highest t-value (−1.44) (Table 4).

	Coefficients	Standard error	t Stat	P-value	Lower95%	Upper95%	Lower95.0%	Jpper95.0%
Intercept	6389.667811	4031.559477	1.584912	0.123832	−1855.797	14635.133	−1855.7971	14635.133
X1: Nitrogen	−0.00760109	0.015654171	−0.48556	0.630925	−0.039617	0.0244153	−0.0396175	0.0244153
X2: Phosphate	0.027003673	0.016448005	1.64176	0.111442	−0.006636	0.0606436	−0.0066363	0.0606436
X3: Potash	0.008948938	0.030466218	0.293733	0.771054	−0.053361	0.0712593	−0.0533615	0.0712593

Table 3.

Linear regression outputs of the relationship between barley yield and fertilisers.

	Coefficients	Standard error	t Stat	P-value	Lower 95%	Upper 95%	Lower95.0%	Upper 95.0%
Intercept	26914.105	15137.24273	1.778006	0.085541	−4000.2689	57828.4789	−4000.2689	57828.47889
X1: Water Withdrawal	−1849.11365	1282.929359	−1.44132	0.159851	−4469.2049	770.97764	−4469.2049	770.9776401
X2: Fertilisation	0.00106867	0.01047852	0.101986	0.919446	−0.0203313	0.02246866	−0.0203313	0.022468657

Table 4.

Linear regression outputs of the relationship between barley yield, water withdrawal and fertilisers.

3.5 Scatter plots and linear regression outputs of the relationship between fertilisers/nutrients and sorghum yields in Morocco

In the context of nitrogen fertilisers used in sorghum cultivation, it can be observed that an R² of 0.075 (7.5%) is obtained. This implies that only 7.5% of changes in sorghum yield can be explained by changes in nitrogen fertiliser application (Figure 6a). In terms of phosphate fertilisers used in sorghum cultivation, it can be observed that an R² of 0.23(23%) is obtained. This implies that only 23% of changes in sorghum yields can be explained by changes in phosphate fertiliser application (Figure 6b). Considering potash fertilisers used in sorghum cultivation, it can be observed that an R² of 0.26 (26%) is obtained. This implies that only 26% of changes in sorghum yield can be explained by changes in potash fertiliser application (Figure 6c). Phosphate fertilisers outbid the other nutrients as they record a relatively higher R² of 23% and the lowest p-values of 0.009. Phosphate fertilisers tend to explain more of the changes in sorghum yield as depicted by the R² of 23%, a statistic that is much higher than those recorded for nitrogen and potash.

Figure 6.

Scatter plots of sorghum yield against (a) nutrient nitrogen, (b) nutrient phosphate and (c) nutrient potash.

The results from the scatter plots (Figure 6a, b and c) are consistent with linear regression outputs (Table 5). This is observed as phosphate fertilisers tend to record the lowest relative p-value of 0.009 and the highest t-value of 2.78. This is followed by nitrogen (p-value = 0.29, t-value = −1.06) and lastly potash (p-value = 0.68, t-value = −0.40). Even though nitrogen represents higher levels of application in Moroccan agriculture, when it comes to sorghum, phosphates tend to impact maize yields more than the other fertilisers. When the linear relationship between sorghum yields as the dependent variable and agricultural water withdrawal and fertilisers as independent variables is considered, it is observed that agricultural water withdrawal records the lower p-value (0.025) and the highest t-value (−2.35) (Table 6). This depicts the fact that agricultural water withdrawal has a more significant relationship with sorghum yield when compared to fertiliser application.

	Coefficients	Standard error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	2526.199866	1697.373588	1.488299	0.147467	−945.3189	5997.71864	−945.31891	5997.718642
X1: Nitrogen	0.007045388	0.006590744	1.068982	0.293895	−0.006434	0.02052497	−0.0064342	0.020524973
X2: Phosphate	0.019310401	0.006924965	2.78852	0.009252	0.0051473	0.03347354	0.005147257	0.033473544
X3: Potash	0.005243352	0.012826935	0.408777	0.685707	−0.020991	0.03147738	−0.02099068	0.031477381

Table 5.

Linear regression outputs of the relationship between sorghum yield and fertilisers.

	Coefficients	Standard error	t Stat	P-value	Lower 95%	Upper95%	Lower95.0%	Upper95.0%
Intercept	15953.27167	6053.100472	2.635554	0.013167	3591.191298	28315.35204	3591.1913	28315.352
X1: Water withdrawal	−1207.806837	513.0194746	−2.35431	0.025299	−2255.53238	−160.081294	−2255.5324	−160.081294
X2: Fertilisation	0.007163757	0.004190164	1.70966	0.097655	−0.0013937	0.015721214	−0.0013937	0.01572121

Table 6.

Linear regression outputs of the relationship between sorghum yield, water withdrawal and fertilisers.

3.6 Scatter plots and linear regression outputs of the relationship between fertilisers/nutrients and wheat yields in Morocco

In the context of nitrogen fertilisers used in wheat cultivation, it can be observed that an R² of 0.015 (1.5%) is obtained. This implies that only 1.5% of changes in wheat yield can be explained by changes in nitrogen fertiliser application (Figure 7a). In terms of phosphate fertilisers used in wheat cultivation, it can be observed that an R² of 0.03 (3%) is obtained. This implies that only 3% of changes in wheat yields can be explained by changes in phosphate fertiliser application (Figure 7b). Considering potash fertilisers used in wheat cultivation, it can be observed that an R² of 0.0002 (0.02%) is obtained. This implies that only 0.02% of changes in wheat yields can be explained by changes in potash fertiliser application (Figure 7c). Also, though most of the relationships are weak, phosphate fertilisers seem to outbid the other nutrients as they record a relatively higher R² of 3% and the lowest p-values of 0.05. Phosphate fertilisers tend to explain more of the changes in wheat yield as depicted by the R² of 3%, a statistic that is much higher than those recorded for nitrogen and potash.

Figure 7.

Scatter plots of wheat yield against (a) nutrient nitrogen, (b) nutrient phosphate and (c) nutrient potash.

The results from the scatter plots (Figure 7a, b and c) are consistent with linear regression outputs (Table 7). This is observed as phosphate fertilisers tend to record the lowest p-value of 0.05 and the highest t-value of 2.04. This is followed by nitrogen (p-value = 0.78, t-value = −0.26) and lastly potash (p-value = 0.81, t-value = −0.23). Even though nitrogen represents higher levels of application in Moroccan agriculture, when it comes to wheat, phosphates tend to impact wheat yields more than the other fertilisers. When the linear relationship between wheat yields as the dependent variable and agricultural water withdrawal and fertilisers as independent variables are considered, it is observed that agricultural water withdrawal records the lower p-value (0.17) and the highest t-value (−1.39) (Table 8). This depicts the fact that agricultural water withdrawal though generally weak has a relatively more significant relationship with wheat yield when compared to fertiliser application.

	Coefficients	Standard error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper95.0%
Intercept	6032.704941	5254.512938	1.1481	0.26031	−4713.98067	16779.3906	−4713.9807	16779.391
X1: Nitrogen	0.005490829	0.020402786	0.269122	0.789742	−0.03623755	0.04721921	−0.0362376	0.0472192
X2: Phosphate	0.043772796	0.021437425	2.041887	0.050351	−7.166E-05	0.08761725	−7.166E-05	0.0876173
X3: Potash	0.009229943	0.039707993	0.232445	0.817825	−0.07198202	0.09044191	−0.071982	0.0904419

Table 7.

Linear regression outputs of the relationship between wheat yield and fertilisers.

	Coefficients	Standard error	t Stat	P-value	Lower 95%	Upper 95%	Lower95.0%	Upper95.0%
Intercept	31606.3683	19681.80113	1.605868	0.118779	−8589.232	71801.9686	−8589.232	71801.96865
X1: Water Withdrawal	−2321.3313	1668.095106	−1.39161	0.174275	−5728.036	1085.37339	−5728.036	1085.37339
X2: Fertilisation	0.01242275	0.013624419	0.9118	0.369143	−0.015402	0.04024752	−0.015402	0.040247522

Table 8.

Linear regression outputs of the relationship between wheat yield, water withdrawal and fertilisers.

4. Discussion

This work has found that of the three fertilisers under study and for all the crops included, phosphate fertilisers tend to have a relatively stronger linear relationship with yields. Wheat records the most significant relationship with fertilisers and more specifically in the case of phosphates (p-value = 0.05). Phosphate fertilisers are obtained from phosphorus rocks that are essential in crop growth and food cultivation. Phosphorus fertilisers constitute a strong element in Moroccan agriculture. Phosphorus is essential found in just a handful of countries around the world, with Morocco being the number one producer of phosphate fertilisers followed by China [27, 28]. About 70% of the global distribution of phosphate rocks will be depleted within the next 100 years. Morocco, with >77% of global phosphorus rock reserves, will need to increase production by 700% to be able to meet its deficits by 2075 [27, 28]. Invariably, Morocco’s reserves accounted for almost all the increases in the global reserves of phosphate rocks [28, 29]. Morocco will have to increase its production to be able to meet the local and international demand for phosphate fertilisers. This work has assessed the recent trends in the linear relationships between the yields of the concerned crops and fertilisers and water withdrawal. This approach is important because as in the case of Morocco, as an arid country in which agriculture is mainly rainfed, water and nutrient management are key drivers of crop yields. However, there is need for more analysis with several variables on the climatic and non-climatic spectra to be able to better understand these trends. Perhaps therefore, the key contribution here is that yield prediction based on a few variables as in the case of this work does not often yield good outputs as yields are complex and generally impacted by a complex combination of several factors. In the current scientific literature, it remains unclear how these components of nutrient and water management play out in determining crop yields. For example, there are no national scale studies that have integrated components of nutrient and water management in yield prediction. Existing studies have either focused on nutrients or water management mostly in the context of irrigation [30]. A better understanding of this relationship will play a crucial role in determining how well yield gaps can be closed through nutrient and water management. In other words, this work sets the pace for a better understanding of the combinations and the need for more research for improved insights into such relationships on nutrient and water or irrigation that can be used to close future yield gaps. By understanding the linear trends between yield and nutrients or water, it becomes possible to identify where the linear trends are weak and which combinations of irrigation and nutrients can be used to close such gaps. The only other study that comes close to this is the work published in 2012 in Nature by Muller et al. [30] as it examines the use of nutrient and water management strategies to determine yield gaps using essential non-linear regression models at a global scale. Our work builds on the same strategy but focuses essentially on Morocco for which the global study did not dwell in detail.

In Morocco, it is important to however understand that there exists a spatial variation in crop yield as are climatic conditions. Normally, from south to north, crop yield increases for most crops. This has been explained by the south–north spatial variations in climate. This is seen as a straddle from south to north shows increased precipitation and lower temperatures due to the presence of the Atlantic Ocean and the Mediterranean Sea. Furthermore, sociodemographic data have also shown that due to its European and temperate influence, the north has higher literacy and lower poverty rates. This mix of climatic and non-climatic variables plays an important role in determining crop yields. The results have shown that in most cases, the changes in yield are explained by relatively small changes in fertilisation as depicted by the R². There is a need to identify how much, when and where nutrients and water are needed to improve crop yields. This is especially true as many of the farmers involved are essentially small-scale farmers who do not have access to nutrients and irrigation. Wheat, on the other hand, has a relatively more significant relationship with phosphate fertilisers when compared to the other crops because the crop has been more valorised over the years as it is not just a major staple crop, but it also plays an important economic role in the country. Therefore, this work helps us to understand which crops need more valorisation in the context of access and use of nutrients and water, insights that can help in the formulation of agricultural policy.

It is now evident from previous scholarship that the relatively stronger impact that phosphate fertilisers have on crop yield in Morocco in general and especially on wheat when compared to the other crops is highly tied to the country’s dominance in the production of phosphate fertilisers, which is also tied to its huge phosphate rock reserves [27, 29]. Morocco is often described as the Saudi Arabia of phosphorus because it is blessed with huge phosphate rock reserves. It has been argued that comparing Morocco with Saudi Arabia in terms of Morocco’s phosphate rock reserves is very simplistic as Morocco would be better compared with all OPEC (Organisation of the Petroleum Exporting countries) put together [31, 32, 33].

The current global reserves of phosphate rocks may last for an additional 300–400 years. By this time, most countries would have exhausted their stocks. This global figure is hugely impacted by Morocco with a rock-phosphate ratio of close to 2000 years. Globally, the countries that currently have rock-phosphate ratios of less than 100 years are responsible for about 70% of the global production and continuous depletion will result in huge deficits [28]. China and the United States account for over 50% of the global production and these might be depleted within the next 60 years at the current rates of extraction. The delicate global phosphorus intricate situation that surrounds phosphate extraction is going to intensify as Morocco has an ever-greater share of global phosphate production [29, 34]. This has implications for food and phosphorus security unless new phosphate reserves can be accessed. This will further mean that the world will be more reliant on Morocco [28, 35].

In terms of the relationship between yield as a dependent variable and agricultural water withdrawal and fertilisers as independent variables, agricultural water withdrawals are observed to have a stronger relationship. Here, it can be concluded that agricultural water withdrawal as a proxy for irrigation in agriculture plays a very important role. As a management option for agriculture, water withdrawal plays a keen role in filling the gaps created by unreliable precipitation. Farmers in most arid and semi-arid countries and regions often depend on precipitation as the main source of water for their crops. However, due to climate change and other anthropogenically induced stressors such as unsustainable systems of farming, there is often not enough precipitation for crop growth for systems that are basically rainfed. The recent IPCC Sixth Assessment Report confirms this assertion when it notes that most of north Africa including Morocco will continue to witness declining precipitation and rising temperature under various Representative Concentration Pathways (RCP) scenarios [36, 37]. The government of Morocco, through the Green Morocco Plan (GMP) and the Generation Green Strategy (GGS), have subsidised irrigation to make it accessible for farmers at various scales. Irrigation is therefore a significant driver of agriculture in Morocco.

Within the pessimistic climate conundrum that necessitates irrigation and fertilisation, such water withdrawals are often based on surface water resources, groundwater resources and non-convectional water resources. The mean precipitation records about 140 billion cubic meters per year [38, 39]. Evapotranspiration triggers a leakage of about 118 billion cubic meters per year. Natural water potential is estimated at about 22 billion cubic meters per year. In Morocco, the amount of economically exploitable water is 80% of the available water resources, revealing the constraints in water resources and the difficulties associated with their utilisation for agriculture [39]. For example, the hydrological regime of all basins is characterised by rife interannual variability marked by wet and dry sequences, interspaced with years of intense drought stress. The Ouergha basin, for example, is among the most productive basins in Morocco by virtue of its surface water flow variations of 2.5 billion cubic meters per year. Thus, the large regional differences in rainfall also trigger large surface water flow variations that vary from a few million cubic meters (MCM) with most arid basins in Morocco such as the Saharan basins, to about one billion cubic meters per year for the most water-rich basins. The Sebou basin in the north of Morocco, for example, holds 30% of surface and groundwater resources in Morocco. Although it represents only 6% of the total area of Morocco, 18% of the country’s population lives within its frontiers [40, 41]. Internal water reallocations are applied to reduce the shortages in each river basin. About 0.3 million cubic meters per year is transferred from the Oum er-Rbia basin to dry areas in Tensift, essentially to sustain irrigation. Similarly, another 0.16 million metric cubic meters per year is transferred from the Sebou and Oum er-Rbia basins to support Bouregreg’s domestic water needs in other parts of Morocco. Ground water, on its part, accounts for 20% of all water resources in Morocco. Aquifers cover about 80,000 km², about 10% of the national territory. Total groundwater withdrawal is estimated at about 3170 million cubic meters per year. However, the huge water withdrawal stress here is exhibited as about 4.2 billion cubic meters per year are extracted (higher than the rate of recharge) [30, 42]. On a final note, non-conventional water withdrawal is governed by the National Water Plan, which aims to address issues of gaps between water demand and water supply through inter alia desalinisation of sea water. The establishments of plants to this effect have enabled the desalination of nearly 515 million cubic meters per year [30, 42, 43].

The generally weak relationship between yield and fertilisers on the one hand and water withdrawal on the other hand shows us that crop yield is not that simple. Crop yields are determined by a complex interplay of several variables including precipitation, temperature, soils, fertilisers, irrigation, crop pests and diseases and livestock. Isolating yield and a few independent variables may provide insights into how many of the changes in yield are explained by changes in fertilisers and irrigation, but it is good to caution that several variables often impact crop yields.

Phosphorus can also be released from compost, green manures and animal manures. These elements contain mineralised phosphorus and micronutrients that are easier for plants to use. These alternative ways of adding phosphorus to increase soil fertility are good but cannot independently match the phosphorus that is obtained from phosphorus rocks [30, 40, 41, 42, 43]. Also, combining phosphorus rock with green manure crop buckwheat might have significant benefits but phosphate rock deposits will remain the main source of phosphate fertilisation. Incubated phosphorus rocks have been observed to increase phosphorus uptake in buckwheat crop but the buckwheat residues did not however enhance the yield of the next crop. It has however been observed that using green manure crops on different soils might produce more positive results, but the scale does not match natural phosphorus. Green manure crop like legumes might also perform better [30, 40, 41, 42, 43].

In the case of inorganic fertilisers, the phosphorus is removed from the rock using acids, rendering the phosphorus more soluble and easier to absorb. The problem now is to employ the same technique in organic agriculture to see how organic farms can enhance the update of phosphorous—one of the three key micronutrients together with potash and nitrogen for plant growth. This is even more challenging as micronutrients have no substitutes and their absence might hamper plant growth [30, 40, 41, 42, 43]. In fact, inorganic phosphorus can be obtained through farm-level recycling of organic materials such as composts, green manure and animal manures. Adding green manure residues to soils can increase mineralisation rates of phosphorus but, at the same time, low concentrations of residues often do limit crop demand. Using green manure crop species that have high phosphorus uptake can overcome this limitation. Buckwheat, for example, absorbs concentrations of phosphorus beyond its own needs such that excess of it is left in the soil for future use [30, 40, 41, 42, 43]. This has evidently shown us that, the future of phosphorus fertilisation depends on natural phosphorus rocks and Morocco will continue to play an important role in the agricultural food-chain in Morocco.

5. Conclusions

This study has shown that though fertiliser application and water withdrawals have a weak relationship with crop yields (indicating the need for valorisation), these inputs nevertheless potentially constitute key variables that can trigger a veritable agricultural revolution in Moroccan agriculture that needs to be investigated further. More specifically, this work has shown that phosphate fertilisers have strong impact on the yields of all the crops, especially wheat, relative to nitrogen and potash. With the reality of the depletion of phosphorus rocks across the world in the frame of the huge dependence of agricultural systems on phosphate fertilisers, several questions come to mind. For example, how can land use policy be streamlined to ensure that global agricultural systems continue to have the phosphates they need to thrive? What are the potential alternative sources of phosphorus away from the huge phosphorus deposits in Morocco? If the Moroccan phosphorus monopoly is maintained, what are the likely actions needed to ensure access to phosphate fertiliser for the rest of Africa without extreme high prices? These among others are some of the questions that come to mind when the likely future trends in the use and stock of phosphorus resources are considered.

However, this study focuses mainly on fertiliser use and irrigation; in reality, crop production is more complex than simplified here, as it is often impacted by a complex interplay of several climatic and non-climatic variables. Potential areas of further research however would estimate yield gaps using the relationship between actual and attainable yield towards closing yield gaps as well as the relationship closing the latter through nutrient and water management. The use of machine learning-based approaches to determine the drivers of crop yields with several climatic and non-climatic drivers will go a long way in providing a balanced understanding of crop yields.