# Disentangling what Drives Global Urea Price: Short-Run and Long-Run Effects

Marie-Hélène Hubert, Marilyne Huchet, Emma Skalski

► To cite this version:

Marie-Hélène Hubert, Marilyne Huchet, Emma Skalski. Disentangling what Drives Global Urea Price: Short-Run and Long-Run Effects. 2026. halshs-05483215

HAL Id: halshs-05483215

https://shs.hal.science/halshs-05483215v1

Preprint submitted on 29 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC0 1.0 - Universal - International License
# Disentangling what Drives Global Urea Price: Short-Run and Long-Run Effects

Marie-Hélène Hubert*

Marilyne Huchet†

Emma Skalski‡

## ABSTRACT

The concurrent surge in fuel, food, and fertilizer prices underscores the broad vulnerability of agrifood system in an interconnected global economy, where supply chain disruptions can propagate rapidly. It also highlights the urgent need for a deeper understanding of the short- and long-term drivers of the price of the world's most widely used nitrogen fertilizer: urea. To address this knowledge gap, our study employs a Vector Error Correction Model (VECM) using global monthly data from 1985 to 2023. In the long run, the energy price index emerges as a key driver of urea price, underscoring the sector's strong dependence on fossil fuels. In the short run, urea price exhibits a rapid and positive response to energy price shocks. Crop price demonstrates a positive and statistically significant long-term effect, though impulse response functions indicate no short-term significance, suggesting farmers gradually adjust urea usage. The real effective exchange rate exerts a negative impact on urea price, with U.S. dollar appreciation leading to lower global urea price—a relationship that remains statistically significant in the short run. Exogenous factors further influence pricing: temperature anomalies significantly increase urea price (+11.9% per 1°C rise), while the COVID-19 pandemic contributed to an 8.8% price surge and the Ukraine war pushed urea prices up by 9.75%, highlighting the vulnerability of fertilizer markets to global crises. Identifying the main drivers of urea price offers critical insights for forecasting price fluctuations and better guiding policy decisions.

Keywords: Urea price, fertilizers, VECM

JEL codes: C32, Q11, Q41

<footnote>*Univ Rennes, CNRS, CREM - UMR6211, 35000 Rennes France ; E-mail: marie-helene.hubert@univ-rennes.fr</footnote>

<footnote>†Institut Agro, SMART - UMR1302, 35000 Rennes France ; E-mail : marilyne.huchet@institut-agro.fr</footnote>

<footnote>‡Corresponding Author: Emma Skalski, Univ Rennes, CNRS, CREM - UMR6211, 35000 Rennes France ; E-mail: emma.skalski@univ-rennes.fr</footnote>
# 1 Introduction

The global market of urea —the world's most widely used nitrogen fertilizer— has experienced significant turbulence in recent years.<fnref n="1" /> A sharp increase in demand following the post-COVID-19 recovery, combined with supply disruptions caused by surges in natural gas price after the Ukraine war, led to a steep rise in the global urea price. Between March 2020 and April 2022, the urea price increased more than fourfold (World Bank, 2024). As of mid-2025, price remains well above pre-pandemic levels. Notably, the urea price also surged during the 2008 financial crisis before decreasing back to pre-crisis levels. Despite these episodes of extreme short-term fluctuations, real urea price has more than doubled over the past four decades, revealing a persistent long-term upward trajectory. This long-term price trend may reflect growing global demand to sustain increased agricultural production and enhance food security (Gollin et al., 2021).

However, international shocks in fertilizer markets can undermine the benefits of using inorganic fertilizers, as demonstrated during the recent fuel–food–fertilizer crisis of 2020 (Abay et al., 2025). Urea price plays a pivotal role in shaping farmers’ input decisions (Calvia, 2025). In response to rising fertilizer costs, farmers often reduce their fertilizer use, which can lead to lower crop yields—particularly in developing regions where access to inputs is already constrained (Brunelle et al., 2015; Ruzzante et al., 2021; Harou et al., 2022; Schaub and Benni, 2024). Such yield reductions may, in turn, depress agricultural productivity and output, exacerbate food insecurity, and even heighten the risk of civil conflict and social unrest in fragile states (Bellemare, 2015; Berman et al., 2021; Amankwah et al., 2025). Moreover, high fertilizer prices can constrain farmers’ ability to adapt to climatic shocks, thereby limiting their capacity to stabilize yields under adverse weather conditions (Di Falco and Vieider, 2022; Mulungu et al., 2025). Given urea’s essential role in agricultural production, understanding the factors driving urea price is crucial for designing effective policies to mitigate sharp variations, stabilize access and prices, and safeguard food security (Spielman et al., 2025). To deepen this understanding, this paper addresses the central question: Which determinants shape the dynamics of global urea price, differentiating between short-run and long-run drivers?

To address this research question, we employ a Vector Error Correction Model (VECM), following the approach established in prior studies (Von Cramon-Taubadel and Goodwin, 2021). Using global monthly data, we capture short-term dynamics while the extended time frame from 1985 to 2023 enables analysis of long-term effects. Our findings reveal distinct short-term and long-term effects. In the long run, energy prices exert a significant and positive impact on urea price, with an estimated price elasticity of 0.68. In the short run, urea price reacts positively to shocks in energy prices, increasing by 2.8% three months after the shock. These results confirm the strong dependence of urea costs on energy prices. The crop price, however, exhibits a positive but substantially lower elasticity of 0.07, indicating a weaker transmission of agricultural commodity prices to urea price. This relationship is not significant in the short run, suggesting that farmers adjust their urea usage over time. Conversely, the real effective exchange rate (REER) shows a negative elasticity of -0.97, indicating that U.S. dollar currency appreciation is associated with lower global urea price. This effect is negative and significant in the short run, with urea price decreasing by 2.5% three months after the shock. Additionally, our analysis isolates exogenous factors. We find that global temperature anomalies significantly impact urea price, with a

<footnote n="1">¹Urea accounts for about 50% of global nitrogen fertilizer consumption in 2023 (FAO, 2024).</footnote>
1°C rise leading to a 11.9% increase. Moreover, our analysis shows that the COVID-19 pandemic led to an increase of approximately 8.8% in urea prices during the considered period (January 2020 - January 2022), while the war in Ukraine caused a 9.75% rise over the period February 2022 - January 2023. Our analysis further decomposes the energy price index into its key components—natural gas, crude oil, and coal—and confirms a causal link between energy prices and urea price, with each component contributing distinct effects. This decomposition underscores energy's critical role in urea pricing and its broader economic implications.

Although the recent COVID-19 pandemic and Ukraine war have spurred significant research on fertilizer price determinants, existing literature exhibits three key limitations that this paper addresses. First, most existing studies focus on regional or country-specific dynamics and largely overlook global factors. For instance, Hu et al. (2025) analyze price spikes in China's domestic urea market, one of the world's leading producers of nitrogen fertilizers. Second, few studies systematically disentangle short-run and long-run effects at the global level with most regional analyses, failing to provide global perspectives (Vatsa et al., 2023; Wongpiyabovorn and Hart, 2024). Third, our analysis contributes to the literature by providing new insights into the dynamic linkages between energy and fertilizer markets. By focusing specifically on urea, which is the most widely used nitrogen fertilizer, we offer a targeted and policy-relevant analysis.

The rest of the paper proceeds as follows. In the following section, we provide background on the world fertilizer market and the existing literature on the drivers of fertilizer prices by focusing on urea. Section 3 presents the data and the empirical strategy. Section 4 discusses the results and the final section concludes.

# 2 Setting the scene: the fuel-food-fertilizers nexus

## 2.1 The central role of fertilizers as key feedstock in global agricultural markets

Fertilizers are substances applied to soil or plant tissues to provide essential nutrients required for plant growth, particularly nitrogen (N), phosphorus (P), and potassium (K). Fertilizers may be derived from natural sources (organic fertilizers), chemically manufactured or processed from minerals (inorganic fertilizers), or formulated as blends of mineral and organic components (organo-mineral fertilizers) to optimize nutrient availability and crop performance (EUROSTAT, 2024). Specifically, inorganic fertilizers are widely used due to their ability to enhance agricultural productivity at a relatively low cost. The global average use of inorganic fertilizers per hectare of arable land has risen sharply over the past five decades, increasing from 42 kg/ha in 1966 to 134 kg/ha in 2022 (World Bank, 2023).

The widespread adoption of inorganic fertilizers worldwide—particularly in developing countries—has played a key role in boosting food production and supporting structural transformation, thereby contributing to improved food security (Duflo et al., 2008; McArthur and McCord, 2017). In particular, during the early period of the Asian Green Revolution, inorganic fertilizers are estimated to have contributed to approximately 25 percent of total rice output growth (Barker et al., 2014). Combined with the adoption of high-yielding crop varieties, it has led to improvement in Gross Domestic Product (GDP) worldwide. Gollin et al. (2021) have estimated that a 10-year delay of the Green Revolution would have cost 17% of GDP per capita and added 223 million people to the developing-world population in 2010. Fertilizers have become an increasingly important component of agricultural production costs, heightening
farmers' exposure to input price fluctuations. Recent evidence from developing countries shows that the 2020-2022 global fertilizer price spikes were transmitted unevenly across markets, with asymmetric price transmission and spatial heterogeneity causing farmers to be more severely affected (Vos et al., 2025; Willwerth et al., 2025).

Nitrogen (N) stands as the most critical nutrient for plant growth, often serving as the primary limiting factor in agricultural productivity. This essential role has made nitrogen fertilizers indispensable for modern agriculture (FAO, 2024). Among these, urea is particularly prominent due to its properties: a high nitrogen content and its availability in two primary forms —granular and prilled— each with distinct advantages. Granular urea offers slow-release properties, reduced caking/clumping, and suitability for crops with longer growth cycles. In contrast, prilled urea provides rapid solubility, ease of handling, and immediate nitrogen availability for crops with urgent nutrient requirements. These unique properties have solidified urea's dominance in global nitrogen fertilizer markets, accounting for about 50% of global nitrogen fertilizer consumption in 2023 (FAO, 2024).

Given its central role in agricultural productivity, understanding the key drivers of urea price is of importance. While the economic literature extensively examines fertilizer use impacts and general price determinants, research specifically focused on urea remains limited. This study addresses this critical gap by providing the first comprehensive analysis of urea price dynamics, distinguishing between short-term and long-term factors.

## 2.2 *The key transmission mechanisms that may drive urea price*

**Fuel-food-fertilizer markets.** Natural gas plays a central role in urea production, functioning as both a primary feedstock and an energy source. This role is primarily attributable to the industrial synthesis of ammonia (NH₃)—the principal nitrogen component of urea—through the Haber-Bosch process, which combines atmospheric nitrogen (N₂) with hydrogen (H₂) predominantly sourced from natural gas. Urea is subsequently formed through the reaction of ammonia with carbon dioxide (CO₂). As a result, natural gas, which represents approximately 72–85% of total ammonia production costs, exerts a significant influence on ammonia and urea price dynamics (Huang, 2007).

The economic literature extensively documents the relationship between energy prices and fertilizer markets, consistently identifying natural gas as one of the most critical cost determinant (Kapsalyamova and Paltsev, 2020). This energy-cost nexus extends to agricultural markets, as fertilizers represent essential inputs for sustaining crop production (Wongpiyabovorn and Hart, 2024). The interdependence between energy, fertilizer, and food markets has been broadly examined in the literature. For instance, Hu et al. (2025) examine the factors that influence urea price in China from 2018 to 2023. Using a Structural Vector Autoregression (SVAR) model, the authors decompose urea price into four main shocks: supply shocks due to changes in the energy price, demand shocks from crop price fluctuations, export demand shocks and idiosyncratic market shocks. The results reveal that increasing energy costs and idiosyncratic factors were the primary drivers of the fluctuations in the urea price in China during this period, while the impact of corn price and export demand was relatively small. In particular, coal-driven supply shocks had a significant effect on recent urea price spikes in China. Other studies analyze how shocks on fertilizer prices affect crop price. Specifically, Vatsa et al. (2023) analyze monthly data from 1990 to 2021 using a SVAR model, identifying that shocks to natural gas and nitrogen fertilizer prices have rapid but transitory effects on key agricultural commodities such as maize, rice, and wheat.
**Macroeconomic and geopolitical drivers.** Beyond the well-documented interconnections between fuel, fertilizer, and food markets, several additional global factors contribute to fertilizer price fluctuations, particularly given the market's high degree of integration. The fertilizer industry's concentrated structure, dominated by a small number of key supplier countries, renders it particularly vulnerable to external shocks and supply chain disruptions (Hernandez and Torero, 2013). Furthermore, major producers may impose export restrictions to secure domestic supplies and maintain strategic reserves, thereby significantly constraining global fertilizer availability - as demonstrated by China's actions during the COVID-19 pandemic.

*Macroeconomic shocks.* Over the past decade, the global economy has faced multiple interconnected crises. They have disrupted global markets—particularly energy and agricultural markets—which, in turn, have had significant repercussions on fertilizer markets. Financial crisis such as the subprime crisis or the European sovereign debt crisis had profound macroeconomic impacts, particularly on global fossil fuel markets (Andriosopoulos et al., 2017). A decade later, the COVID-19 pandemic disrupted global economies, affecting unemployment rates, GDP, and supply chains (Beckman and Countryman, 2021). Several studies have examined the impact of the pandemic on various commodity markets, including fertilizers. The rise in the price of natural gas, the reduction in fertilizer production capacity, and export restrictions, particularly from China, further contributed to a sharp increase in fertilizer costs (Hebebrand and Laborde, 2023).

*Trade policies.* Mineral fertilizers are traded internationally in U.S. dollars, and exchange rate fluctuations directly affect the cost of imported inputs as well as the competitiveness of exports. Movements in the value of the dollar—driven by U.S. monetary policy or trade balances—can therefore influence fertilizer prices, ceteris paribus. Similarly to internationally traded agricultural commodities, the exchange rate emerges as a fundamental determinant in price formation processes (Gohin and Chantret, 2010; Nazlioglu and Soytas, 2012; Baffes and Haniotis, 2016).

*Climate patterns.* Many studies have quantified the adverse effects of climate change and weather shocks on agricultural yields, with some regions more affected than others. For example, yields in Sub-Saharan Africa are projected to decline by 10–20% by 2050 (Lobell and Field, 2007). A substantial body of work has also examined how farmers adapt to these shocks, including adjustments in input use (Schlenker and Roberts, 2009; Bareille and Chakir, 2024). Among these strategies, changes in nitrogen fertilizer application may play a key role and could have implications for fertilizer markets. A nascent literature investigates whether temperature anomalies affect fertilizer use. Celikkol Erbas and Guven So-lakoglu (2017) show that such anomalies increase nitrogen fertilizer emissions, possibly because farmers increase their use of inputs to mitigate their negative effects. While this paper relies on country-level data, Mulungu et al. (2025) and Hazrana et al. (2025) use household-level data to provide a more detailed un-derstanding of farmers’ responses. Mulungu et al. (2025) find that weather shocks reduce fertilizer use by separating their direct (behavioral) and indirect (income) effects. Finally, Hazrana et al. (2025) show that although input subsidies generally increase fertilizer use, this effect is weaker for households more vulnerable to climate shocks.

*Geopolitical disturbances.* Given the uneven geographic distribution of fossil fuel resources world-wide, global fuel markets exhibit heightened sensitivity to geopolitical disruptions. The conflict in Ukraine has exacerbated the volatility in the interconnected food, fuel and fertilizer markets (Arndt et al., 2023). Empirical analysis using a competitive storage model reveals that supply shocks directly
contributed to higher global average prices for wheat and corn during the crop year 2021/22 (Gilbert, 2023).

# 3 Data and empirical strategy

## 3.1 Data

We use monthly data spanning from January 1985 to December 2023. This period is chosen for two reasons. OPEC countries adopted a change in strategy in 1985: they declared that they would abandon all production discipline and official oil price support, which very quickly led to a fall in oil price (Srinivasan, 1986). Besides, 2023 serves as an appropriate endpoint to capture the impacts of both the COVID-19 pandemic and the war in Ukraine. Data source and units are summarized in **Table 1**.

<table><caption>**Table 1: Data information**</caption><thead><tr><td>Variable</td><td>Source</td><td>Unit</td></tr></thead><tbody><tr><td>Urea price</td><td>World Bank</td><td>USD/t in real value, Base 2010</td></tr><tr><td>Energy price index</td><td>World Bank</td><td>Index, 2010 = 100</td></tr><tr><td>Crop price</td><td>World Bank</td><td>USD/t in real value, Base 2010</td></tr><tr><td>Real effective exchange rate</td><td>FRED</td><td>Index, 2010 = 100</td></tr><tr><td>Temperature anomalies</td><td>NOAA</td><td>Degrees Celsius</td></tr></tbody></table>

Note: FRED: Federal Reserve Economic Data, Federal Reserve Bank of St Louis. NOAA: National Oceanic and Atmospheric Administration.

As already mentioned, we seek to identify the drivers of global urea price fluctuations. For the main variable of interest, the global urea price, the Black Sea prill FOB spot market serves as the reference until March 2022. However, due to disruptions caused by the Ukraine war, this reference is replaced by the Middle East market from March 2022, as indicated in the Pink Sheet commodity database of the World Bank (2024). According to the literature, a number of variables may explain the variation of the urea price. First, we consider the energy price index, as reported in the World Bank's monthly Pink Sheet (World Bank, 2024). This index is computed at a monthly frequency, expressed in nominal terms, and normalized to a base year of 2010 (=100). It is constructed as a weighted average of natural gas, crude oil, and coal prices, with respective weights of 10.7% for natural gas, 84.6% for crude oil, and 4.7% for coal.<fnref n="2" /> Second, we introduce the prices of key nitrogen-dependent crops—namely, wheat, corn, sorghum, and rice—and compute their weighted average prices using the weights applied by the World Bank in its grain price index.<fnref n="3" /> All monthly price data in the World Bank database are reported in nominal terms. We convert these nominal values into real terms using the annual World Consumer Price Index (CPI), applying monthly adjustments under the assumption of uniform inflation throughout

<footnote n="2">²The weights are based on the export values of developing countries during the period 2002–2004. Developing countries are defined as low and middle-income countries (LMICs) according to the World Bank Development Data Group income classification as of June 20, 2006.</footnote>

<footnote n="3">³In the World Bank's crop price index, the original weights are 30.1% for rice, 25.2% for wheat, 40.7% for maize and sorghum, and 4.1% for barley. However, since the price data for barley are available only until August 2020, barley was excluded from the index for the analysis. The remaining weights were rescaled to sum to 100%, resulting in new weights of 31.35% for rice, 26.25% for wheat, and 42.40% for maize and sorghum.</footnote>
the year (FRED, 2024). We set 2010 as the base year. Given that fertilizers are priced in U.S. dollars on international markets, it is pertinent to examine the role of the Real Effective Exchange Rate (REER) in this context. The REER measures the value of the U.S. dollar relative to a basket of foreign currencies, weighted by the trade importance of each major trading partner. This index is calculated by the FRED (FRED, 2024). To capture the impact of weather shocks on fertilizer markets, we introduce temperature anomalies as an explanatory variable. Temperature anomalies are defined as the absolute deviation of monthly temperatures from their long-term monthly averages during the 1971–2000 reference period, as calculated by the National Oceanic and Atmospheric Administration (NOAA) (National Oceanic and Atmospheric Administration, 2024). For example, a temperature anomaly of 0.4 °C in January 2020 indicates that the temperatures in January 2020 were 0.4 °C higher than their long-term average for that month.

The model also incorporates dummy variables to account for the effects of the major economic crises as shown in **Figure 1**. A control dummy is included for the subprime crisis, covering the period from August 2007 to October 2008. The COVID-19 dummy covers the period from January 2020 to January 2022, reflecting the global disruptions caused by the pandemic, including lockdowns and supply chain issues. The Ukraine war dummy spans from February 2022 to January 2023, capturing the initial phase of the conflict and its impact on global markets. These periods were chosen carefully to avoid overlap, allowing for a clear distinction between the effects of each event.

**Table 2** presents descriptive statistics. We apply the natural logarithm to the variables: urea price, energy price index, crop price and REER to manage proportional changes and scale the data. As temperature anomalies represent deviations from the 1971-2000 baseline (ranging from 0.04 to 1.43), we do not use the logarithmic form.

<table><caption>**Table 2:** Descriptive statistics</caption><thead><tr><td>Variable</td><td>Mean</td><td>Median</td><td>Stand. dev</td><td>Max</td><td>Min</td><td>Nb of obs</td></tr></thead><tbody><tr><td>Urea price</td><td>0.30</td><td>0.42</td><td>0.90</td><td>2.15</td><td>-1.28</td><td>468</td></tr><tr><td>Energy price index</td><td>-0.95</td><td>-0.74</td><td>0.99</td><td>0.64</td><td>-2.71</td><td>468</td></tr><tr><td>Crop price</td><td>0.42</td><td>0.39</td><td>0.69</td><td>1.76</td><td>-0.82</td><td>468</td></tr><tr><td>REER</td><td>4.71</td><td>4.68</td><td>0.12</td><td>5.00</td><td>4.54</td><td>468</td></tr><tr><td>Temperature anomalies</td><td>0.60</td><td>0.60</td><td>0.27</td><td>1.43</td><td>0.04</td><td>468</td></tr></tbody></table>

Note: Urea price, energy price index, crop price, and REER are ln-transformed.
<figure><img src="image_2.png" /><figcaption>Figure 1: Evolution of urea price since 1985 ($/metric ton)</figcaption></figure>

Source: World Bank (2024)

Note: This figure illustrates the evolution of urea price in nominal terms from January 1985 to December 2023. In April 2022, urea price reached to a historic $925/ton on the Middle East spot market, marking a record that had not been reached in years.

## 3.2 Empirical strategy

To examine the differential short- and long-term effects of identified key determinants on urea price fluctuations, we apply a Vector Error Correction Model (VECM) which enables to filter long run co-movement in the prices (Engle and Granger, 1987; Awokuse, 2005; Panagiotidis and Rutledge, 2007; Vatsa et al., 2023; Cabrera and Schulz, 2016). This model combines a Vector Autoregressive (VAR) model with a long-term equilibrium relationship incorporating an Error Correction Term (ECT), which measures the speed at which urea price adjusts its value to return to long-term equilibrium after a short-term disturbance. The latter is emphasized by the concept of cointegration, which consists of identifying a linear combination of price series that is stationary (or integrated of order 0, I(0)) even if the price series are non-stationary (integrated of order 1, I(1)) (Granger, 1981).

The evolution of prices $Y_t$ is determined by both deviations from the long-run equilibrium relationship and short-term dynamic adjustments. The general form of the VECM model is:

$$ \Delta Y_t = \nu + \Pi Y_{t-1} + \sum_{i=1}^{p-1} \Gamma_i \Delta Y_{t-i} + \gamma X_t + \varepsilon_t \quad (1) $$

In Equation (1), the variables are defined as follows: $\Delta Y_t$ represents the first difference vector of the endogenous variables. The long-term relationship is given by $\Pi = \alpha\beta'$, where $\alpha$ represents the adjustment coefficient matrix and $\beta'$ the cointegration matrix (long-run parameter). The number of lags in the model is denoted by $p$, while $\Gamma_i$ captures the short-term dynamics. $\Delta Y_{t-i}$ represents the
lagged first differences of the endogenous variables. The impacts of exogenous variables are captured by the exogenous variable matrix $X_t$, with the corresponding coefficient vector denoted as $\gamma$. Finally, $\nu$ represents the intercept and $\varepsilon_t$ is the random error term.

Before estimating Equation (1), we run stationarity tests. Two unit root tests are available: the Augmented Dickey-Fuller test and the test proposed by Zivot-Andrews (Dickey and Fuller, 1979; Zivot and Andrews, 2002). Given the documented structural disruptions in these markets, we employ the Zivot-Andrews test, which permits stationarity analysis while accounting for the most significant structural break in the time series. The results of the structural break test indicate that nearly all series exhibit I(1) behavior, implying non-stationarity in levels but stationarity following first-differencing (Table A.1 in Appendix). The only exception is found for temperature anomalies, which do not require differencing to achieve stationarity. The test also identifies a major break occurring in May 2011, in a context of sovereign debt crisis in the Eurozone. The results indicate that the vector of endogenous variables, $Y_t$, includes urea price, the energy price index, crop price, and the REER, while temperature anomalies, and the dummy variables representing the 2011 structural break, the subprime crisis, COVID-19 and, Ukraine war are exogenous ($X_t$).

Having assessed stationarity, we proceed to examine whether the variables exhibit a long-term equilibrium relationship, a process formally known as cointegration analysis. The Johansen test represents the standard methodology for detecting such relationships among variables integrated of the same order (Johansen, 1988). However, as this test does not account for structural breaks, we supplement our analysis with the Gregory-Hansen test, which accounts the presence of one major structural break (Gregory and Hansen, 1996). The results of the Johansen test provides strong evidence of the existence of cointegrating relationships among urea price, energy price index, crop price and the real effective exchange rate, a finding that is confirmed by the Gregory-Hansen test. Detailed results of both cointegration tests are reported respectively in Appendix A.2 and A.3. In this analysis, we focus on the first cointegrating vector, which is associated with the largest eigenvalue and represents the dominant long-run equilibrium in the system. By normalizing this vector with respect to urea prices, we can directly interpret how energy price index, crop price and the real effective exchange rate influence urea price in the long run.

# 4 Results

## 4.1 Results

Based on the previous analysis, we estimate the VECM, with detailed results presented in Table A.4 in Appendix. We first analyze the coefficients representing the long-term relationships among endogenous variables. We specifically examine the cointegration between urea price and other endogenous variables, with the estimated coefficients reported in Table 3.

<table><caption>**Table 3:** Cointegration relationship between endogenous variables</caption><thead><tr><td>Variable</td><td>Urea price (UP)</td></tr></thead><tbody><tr><td>Energy price index (EP)</td><td>0.68</td></tr><tr><td>Crop price (CP)</td><td>0.07</td></tr><tr><td>REER</td><td>-0.97</td></tr></tbody></table>

Using the information from Table 3, the long-term relationship can be written as follows:
$$ \widehat{lnUP} = 5.09 + 0.68lnEP + 0.07lnCP - 0.97lnREER \quad (2) $$

The estimated coefficients quantify the price responsiveness of urea to key determinants. The energy price elasticity of 0.68 indicates that a 1% increase in energy price translates to a 0.68% increase in urea price. This positive elasticity underscores the direct transmission of energy cost shocks to urea markets. In contrast, the crop price elasticity of 0.07 demonstrates minimal pass-through, with only a 0.07% urea price response to each 1% change in crop price. This elasticity suggests a weak relationship between crop price and urea price, indicating that farmers' demand for urea is relatively inelastic. The real effective exchange rate (REER) elasticity of -0.97 shows that a 1% U.S. dollar appreciation reduces the urea price by 0.97%. This negative elasticity reflects how exchange rate movements influence global urea price. It is in line with empirical studies from the literature which show that fluctuations in the exchange rate can lead to significant agricultural price variations (Awokuse, 2005; Baffes and Haniotis, 2016).

Impulse Response Functions (IRF) capture the short-term reactions of urea price to shocks from other endogenous variables, while also considering the long-term equilibrium constraints imposed by cointegration (Figure 2). Figure 2 graph (a) illustrates that urea price reacts positively and quickly to a shock in the energy price index, showing a 2.8% increase three months after the shock. This strong and rapid price transmission reveals a high sensitivity of the urea price to the energy price index. The impact of crop price on urea price appears to be non-significant, as indicated by the confidence intervals, which are represented by the shaded areas and bounded by the dotted lines of the upper and lower intervals, including zero (Figure 2 graph (b)). Farmers are likely to adjust their production plans and, consequently, their fertilizer purchases in anticipation of the next season, based on the observed prices from the current year. The increase in urea supply can take time, particularly if farmers are dependent on specific supply channels or on the availability of credit to purchase inputs. Figure 2 graph (c) illustrates a significant negative effect of the REER on urea price, with a 2.5% decrease three months after the shock, which highlights the rapid and significant impact of exchange rate fluctuations on the urea price.
<figure><img src="image_3.png" /><figcaption>**Figure 2:** Urea price responses to endogenous variable shocks</figcaption></figure>

Note: This analysis is carried out at a confidence level of 95%.

The Error Correction Term (ECT) captures how the dependent variable adjusts to its long-term equi-
librium after a short-term shock. The negative coefficient of -0.2657 (Table A.4 in Appendix) indicates
that 26.57% of the deviation is corrected in each period, reflecting a gradual return of urea price to its
long-term equilibrium value. This statistically significant estimated coefficient at 1% level confirms the
robustness of this process.

We now turn to the analysis of the short-term impacts of the exogenous variables on the global urea
price. The estimated coefficients are reported in Table 4. The temperature anomalies have a significant
positive effect on the urea price, with a coefficient of 0.119, indicating that an increase of 1°C leads to
an 11.9% increase in the price of urea. Our results reveal that the subprime crisis has no statistically
significant effect while the COVID-19 pandemic and Ukraine war are associated with higher urea price.
The estimated coefficient of 0.084 for COVID-19 suggests that, on average, the global urea price was
around 8.8% higher during the COVID-19 period than outside this period, all else equal.<fnref n="4" /> The Ukraine war shows a more pronounced effect, with a 9.75% increase in urea price during the considered period.

<table><caption>**Table 4: Impact of exogenous variables on urea price**</caption><thead><tr><th></th><th>Temperature anomalies</th><th>Subprime crisis</th><th>COVID-19</th><th>Ukraine war</th></tr></thead><tbody><tr><th>Urea Price</th><td>0.119**(0.039)</td><td>0.036 (0.039)</td><td>0.084*(0.034)</td><td>0.093*(0.046)</td></tr></tbody></table>

Note: The values in parentheses correspond to the standard errors.

*** statistically significant at the 1% level; ** statistically significant at the 5% level; * statistically significant at the 10% level.

## 4.2 Robustness check

To ensure the robustness of the VECM estimates and the stability of the cointegrating relationship, we conduct a lag length robustness check using various lag specifications. Although the VECM optimal lag (p=1) was determined by the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), we also test the model with 12 lags, which are commonly used in monthly data to capture annual seasonal patterns and delayed effects. This specification allows us to assess the robustness of the results to a longer lag structure. The results confirm that the long-run relationship remains stable across alternative lag specifications. Coefficient estimates retain their sign and exhibit similar magnitudes under longer lag lengths, thereby supporting the robustness and reliability of the model. Detailed results are available in Table A.5.

Additionally, we break down the energy price index into its key components—natural gas, crude oil, and coal—to assess their individual effects on urea price. The results show a positive and statistically significant response of urea price to shocks in each energy source, with varying dynamics.

First, urea price exhibits a pronounced and persistent response to natural gas price shocks. Empirical analysis indicates that a one-standard-deviation shock to natural gas price induces a 4.3% increase in urea price within six months (Figure 3, graph (a)), highlighting the central role of natural gas as the primary feedstock in ammonia synthesis. In contrast, shocks to crude oil price induce a more complex dynamic. Initially, urea price experiences a modest decline, likely reflecting short-term inventory adjustments or hedging behavior within the supply chain. However, two months post-shock, prices begin to rise, reaching a peak increase of approximately 3.3% by the sixth month (Figure 3, graph (b)). This sustained response reflects crude oil's dual role in urea production: both as a direct energy input in some production processes and as an indirect cost driver through transportation and substitute feedstock dynamics. Beyond the sixth month, the urea price remains elevated following the shock, indicating the persistent structural impact of oil price shocks. Finally, the urea price responds more moderately to coal price shocks, with an increase of 2.9% after six months (Figure 3, graph (c)). Coal, which is less widely used as a feedstock in global urea production and whose utilization is predominantly concentrated in China, exerts a comparatively limited impact on urea price (Bicer et al., 2017).

<footnote n="4">⁴In a ln-linear model, the coefficient on a dummy variable corresponds to a percentage difference in the value dependent variable between two periods —the period during which the dummy equals one and the period in which it equals zero— and is given to ($e^{\beta_{dummy}} - 1$) × 100, where $\beta_{dummy}$ is the coefficient associated with the dummy variable.</footnote>
<figure><img src="image_4.png" /><figcaption>**Figure 3:** Urea price response to shocks in energy price components</figcaption></figure>

Note: This analysis is carried out at a confidence level of 95%.

# 5 Conclusion

This study examines the impacts of key factors—including the energy price index, the crop price, the real effective exchange rate, temperature anomalies, and major economic crises (subprime crisis, COVID-19 and Ukraine war)—on urea price dynamics using a Vector Error Correction Model with global monthly data from 1985 to 2023. In the long run, we find that the energy price index appears as a key driver of urea price (elasticity of 0.68), highlighting the sector's strong dependence on fossil fuels, while crop price exhibits a weak transmission effect (elasticity of 0.07) and U.S. dollar appreciation has a negative and statistically significant impact (elasticity of -0.97). In the short run, urea price reacts quickly and positively to energy price shocks and negatively to U.S. dollar appreciation, though crop price shows no statistically significant impact, suggesting farmers adjust urea usage gradually rather than immediately.
Our analysis also isolates exogenous shocks, revealing that each 1°C rise in global temperature anomalies corresponds to an 11.9% increase in urea price. We also find that the COVID-19 pandemic contributed to an 8.8% price increase between January 2020 and January 2022, while the Ukraine war had a stronger impact, raising global urea prices by 9.75% during the conflict period (February 2022 - January 2023). After decomposing the energy price index into its key components, the IRFs indicate that, six months after the shock, urea price is most sensitive to shocks in natural gas (+4.3%) and crude oil (+3.3%), while responding more moderately to coal price shocks (+2.9%). These results reflect the dominant role of natural gas and crude oil as inputs in urea production, in contrast to the limited use of coal, which results in a weaker transmission mechanism to urea price.

These findings underscore the complex interplay between energy markets, macroeconomic factors, and environmental conditions in determining urea price dynamics. It offers a comprehensive framework that integrates both macroeconomic and climatic variables, bridging critical gaps in the literature on fertilizer price fluctuations. This study opens several avenues for future research. First, the analysis is based on a global scale, which limits the consideration of country-specific factors, such as agricultural and economic policies that may influence the price of urea. Second, by focusing on reference markets, the study may not fully capture the heterogeneity of market conditions across different regions. Incorporating regional dynamics would provide valuable insights into how local market conditions shape global fertilizer price trends.

## References

Abay, K. A., Chamberlin, J., Chivenge, P., and Spielman, D. J. (2025). Fertilizer, soil health, and economic shocks: A synthesis of recent evidence. *Food Policy*, 133:102892.

Amankwah, A., Ambel, A., Gourlay, S., Kilic, T., Markhof, Y., and Wollburg, P. (2025). Smallholder farming, fertilizer use, and the polycrisis period: Cross-country evidence from longitudinal surveys in Sub-Saharan Africa. *Food Policy*, 133:102885.

Andriosopoulos, K., Galariotis, E., and Spyrou, S. (2017). Contagion, volatility persistence and volatility spill-overs: The case of energy markets during the European financial crisis. *Energy Economics*, 66:217–227.

Arndt, C., Diao, X., Dorosh, P., Pauw, K., and Thurlow, J. (2023). The Ukraine war and rising commodity prices: Implications for developing countries. *Global Food Security*, 36:100680.

Awokuse, T. O. (2005). Impact of macroeconomic policies on agricultural prices. *Agricultural and Resource Economics Review*, 34(2):226–237.

Baffes, J. and Haniotis, T. (2016). What explains agricultural price movements? *Journal of Agricultural Economics*, 67(3):706–721.

Bareille, F. and Chakir, R. (2024). Structural identification of weather impacts on crop yields: disentangling agronomic from adaptation effects. *American Journal of Agricultural Economics*, 106(3):989–1019.
Barker, R., Herdt, R. W., and Rose, B. (2014). *The Rice Economy of Asia*. Routledge. 1st edition in 1985. E-book published in 2014.

Beckman, J. and Countryman, A. M. (2021). The importance of agriculture in the economy: impacts from COVID-19. *American Journal of Agricultural Economics*, 103(5):1595–1611.

Bellemare, M. F. (2015). Rising food prices, food price volatility, and social unrest. *American Journal of Agricultural Economics*, 97(1):1–21.

Berman, N., Couttenier, M., and Soubeyran, R. (2021). Fertile ground for conflict. *Journal of the European Economic Association*, 19(1):82–127.

Bicer, Y., Dincer, I., Vezina, G., and Raso, F. (2017). Impact assessment and environmental evaluation of various ammonia production processes. *Environmental management*, 59:842–855.

Brunelle, T., Dumas, P., Souty, F., Dorin, B., and Nadaud, F. (2015). Evaluating the impact of rising fertilizer prices on crop yields. *Agricultural Economics*, 46(5):653–666.

Cabrera, B. L. and Schulz, F. (2016). Volatility linkages between energy and agricultural commodity prices. *Energy Economics*, 54:190–203.

Calvia, M. (2025). Dissecting Extreme Price Fluctuations in Mineral Fertilizers: Regularities and Co-Movements in Light of Global Food Security. *Applied Economic Perspectives and Policy*.

Celikkol Erbas, B. and Guven Solakoglu, E. (2017). In the presence of climate change, the use of fertilizers and the effect of income on agricultural emissions. *Sustainability*, 9(11):1989.

Di Falco, S. and Vieider, F. M. (2022). Environmental adaptation of risk preferences. *The Economic Journal*, 132(648):2737–2766.

Dickey, D. A. and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. *Journal of the American Statistical Association*, 74(366):427–431.

Duflo, E., Kremer, M., and Robinson, J. (2008). How high are rates of return to fertilizer? Evidence from field experiments in Kenya. *American economic review*, 98(2):482–488.

Engle, R. F. and Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. *Econometrica*, 55(2):251–276.

EUROSTAT (2024). Glossary: Fertiliser. EUROSTAT database. Available at: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Fertiliser.

FAO (2024). Fertilizers by product. FAOSTAT database. Available at: https://www.fao.org/faostat/en/#data/RFB.

FRED (2024). Federal reserve bank of st. louis. economic data. Available at: https://fred.stlouisfed.org.

Gilbert, C. L. (2023). The impact of the Ukraine conflict on world grains prices. *Agricultural Economic Society*, 97th Annual Conference.
Gohin, A. and Chantret, F. (2010). The long-run impact of energy prices on world agricultural markets: The role of macro-economic linkages. *Energy Policy*, 38(1):333–339.

Gollin, D., Hansen, C. W., and Wingender, A. M. (2021). Two blades of grass: The impact of the green revolution. *Journal of Political Economy*, 129(8):2344–2384.

Granger, C. W. (1981). Some properties of time series data and their use in econometric model specification. *Journal of Econometrics*, 16(1):121–130.

Gregory, A. W. and Hansen, B. E. (1996). Residual-based tests for cointegration in models with regime shifts. *Journal of Econometrics*, 70(1):99–126.

Harou, A. P., Madajewicz, M., Michelson, H., Palm, C. A., Amuri, N., Magomba, C., Semoka, J. M., Tschirhart, K., and Weil, R. (2022). The joint effects of information and financing constraints on technology adoption: Evidence from a field experiment in rural Tanzania. *Journal of Development Economics*, 155:102707.

Hazrana, J., Nazrana, A., and Mishra, A. K. (2025). Input subsidies, fertilizer intensity and imbalances amidst climate change: Evidence from Bangladesh. *Food Policy*, 133:102825.

Hebebrand, C. and Laborde, D. (2023). High fertilizer prices contribute to rising global food security concerns. IFPRI Blog. Available at: https://www.ifpri.org/blog/high-fertilizer-price-s-contribute-rising-global-food-security-concerns/.

Hernandez, M. A. and Torero, M. (2013). Market concentration and pricing behavior in the fertilizer industry: a global approach. *Agricultural Economics*, 44(6):723–734.

Hu, Z., Yan, L., Yuan, J., and Etienne, X. (2025). Deconstructing fertilizer price spikes: Evidence from Chinese urea fertilizer market. *Food Policy*, 133:102829.

Huang, W.-Y. (2007). Impact of rising natural gas prices on U.S. ammonia supply. Economic research service, United States Department of Agriculture (USDA).

Johansen, S. (1988). Statistical analysis of cointegration vectors. *Journal of Economic Dynamics and Control*, 12(2-3):231–254.

Kapsalyamova, Z. and Paltsev, S. (2020). Use of natural gas and oil as a source of feedstocks. *Energy Economics*, 92:104984.

Lobell, D. B. and Field, C. B. (2007). Global scale climate–crop yield relationships and the impacts of recent warming. *Environmental research letters*, 2(1):014002.

McArthur, J. W. and McCord, G. C. (2017). Fertilizing growth: Agricultural inputs and their effects in economic development. *Journal of Development Economics*, 127:133–152.

Mulungu, K., Manning, D. T., and Bozzola, M. (2025). Once bitten, twice shy? Direct and indirect effects of weather shocks on fertilizer and improved seeds use. *Food Policy*, 133:102852.
National Oceanic and Atmospheric Administration (2024). Global Temperature Surface Anomalies. *National Centers for Environmental Information*. Available at: https://www.ncei.noaa.gov/ac cess/monitoring/global-temperature-anomalies/anomalies.

Nazlioglu, S. and Soytas, U. (2012). Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis. *Energy Economics*, 34(4):1098–1104.

Panagiotidis, T. and Rutledge, E. (2007). Oil and gas markets in the UK: Evidence from a cointegrating approach. *Energy Economics*, 29(2):329–347.

Ruzzante, S., Labarta, R., and Bilton, A. (2021). Adoption of agricultural technology in the developing world: A meta-analysis of the empirical literature. *World development*, 146:105599.

Schaub, S. and Benni, N. E. (2024). How do price (risk) changes influence farmers’ preferences to reduce fertilizer application? *Agricultural Economics*, 55(2):365–383.

Schlenker, W. and Roberts, M. J. (2009). Nonlinear temperature effects indicate severe damages to US crop yields under climate change. *Proceedings of the National Academy of Sciences*, 106(37):15594–15598.

Spielman, D. J., Mugabo, S., Rosenbach, G., Ndikumana, S., Benimana, G., and Ingabire, C. (2025). Fertilizer policy reforms in the midst of crisis: Evidence from Rwanda. *Food Policy*, 133:102823.

Srinivasan, V. K. (1986). Oil Market in the Eighties: Capers or Calculations? *Economic and Political Weekly*, 21(16):690–692.

Vatsa, P., Miljkovic, D., and Baek, J. (2023). Linkages between natural gas, fertiliser and cereal prices: A note. *Journal of Agricultural Economics*, 74(3):935–940.

Von Cramon-Taubadel, S. and Goodwin, B. K. (2021). Price transmission in agricultural markets. *Annual Review of Resource Economics*, 13(1):65–84.

Vos, R., Glauber, J., Hebebrand, C., and Rice, B. (2025). Global shocks to fertilizer markets: Impacts on prices, demand and farm profitability. *Food Policy*, 133:102790.

Willwerth, H. S., Khaemba, C. N., Serra, T., Michelson, H. C., Rutsaert, P., and Donovan, J. (2025). Global shocks and local sellers: Kenyan fertilizer markets’ response to the fuel-fertilizer-food price crisis. *Food Policy*, 133:102889.

Wongpiyabovorn, O. and Hart, C. (2024). Examining the factors of fertilizer pricing. *Journal of the Agricultural and Applied Economics Association*, 3(3):572–583.

World Bank (2023). World Development Indicators. Available at: https://databank.worldbank.org/source/world-development-indicators.

World Bank (2024). Pink Sheet: World Bank Commodity Price Data. Available at: https://www.worldbank.org/en/research/commodity-markets.

Zivot, E. and Andrews, D. W. K. (2002). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. *Journal of Business & Economic Statistics*, 20(1):25–44.
# Appendix

<table><caption>**Table A.1:** Results of the Zivot-Andrews test</caption><thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td></td><td>Urea price</td><td></td><td></td><td>Energy price index</td><td></td><td></td><td>REER</td><td></td><td>1% crit.value</td></tr><tr><td><strong>Level</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intercept</td><td>-3.99</td><td>no</td><td>12/2021</td><td>-3.67</td><td>no</td><td>01/2020</td><td>-4.54</td><td>no</td><td>07/2014</td><td>-5.34</td></tr><tr><td>Trend</td><td>-3.89</td><td>no</td><td>10/2021</td><td>-3.48</td><td>no</td><td>02/2015</td><td>-3.87</td><td>no</td><td>10/1985</td><td>-4.93</td></tr><tr><td>Both</td><td>-4.76</td><td>no</td><td>05/2011</td><td>-5.45</td><td>no</td><td>12/2011</td><td>-4.38</td><td>no</td><td>08/2003</td><td>-5.57</td></tr><tr><td><strong>Log</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intercept</td><td>-3.77</td><td>no</td><td>12/2021</td><td>-3.71</td><td>no</td><td>12/1998</td><td>-4.36</td><td>no</td><td>07/2014</td><td>-5.34</td></tr><tr><td>Trend</td><td>-3.58</td><td>no</td><td>04/2015</td><td>-3.75</td><td>no</td><td>01/2014</td><td>-3.68</td><td>no</td><td>10/2020</td><td>-4.93</td></tr><tr><td>Both</td><td>-3.93</td><td>no</td><td>05/2011</td><td>-5.17</td><td>no</td><td>12/2008</td><td>-4.21</td><td>no</td><td>08/2003</td><td>-5.57</td></tr><tr><td><strong>Log 1st diff</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intercept</td><td>-11.12</td><td>yes</td><td>12/2021</td><td>-10.59</td><td>yes</td><td>09/1994</td><td>-12.23</td><td>yes</td><td>04/1988</td><td>-5.34</td></tr><tr><td>Trend</td><td>-10.77</td><td>yes</td><td>01/2012</td><td>-10.49</td><td>yes</td><td>07/1999</td><td>-12.11</td><td>yes</td><td>03/1989</td><td>-4.93</td></tr><tr><td>Both</td><td>-11.19</td><td>yes</td><td>12/2021</td><td>-10.62</td><td>yes</td><td>12/1998</td><td>-12.27</td><td>yes</td><td>08/1998</td><td>-5.57</td></tr><tr><td></td><td>Crop price</td><td></td><td></td><td>Temperature anomalies</td><td></td><td></td><td></td><td></td><td>1% crit.value</td></tr><tr><td><strong>Level</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intercept</td><td>-5.31</td><td>no</td><td>12/2020</td><td></td><td></td><td>-5.54</td><td>yes</td><td>11/2014</td><td>-5.34</td></tr><tr><td>Trend</td><td>-4.13</td><td>no</td><td>01/2020</td><td></td><td></td><td>-5.73</td><td>yes</td><td>04/2023</td><td>-4.93</td></tr><tr><td>Both</td><td>-5.02</td><td>no</td><td>12/2012</td><td></td><td></td><td>-6.90</td><td>yes</td><td>10/2022</td><td>-5.57</td></tr><tr><td><strong>Log</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intercept</td><td>-5.44</td><td>no</td><td>12/2020</td><td></td><td></td><td>-5.54</td><td>yes</td><td>11/2014</td><td>-5.34</td></tr><tr><td>Trend</td><td>-4.59</td><td>no</td><td>05/2019</td><td></td><td></td><td>-5.73</td><td>yes</td><td>04/2023</td><td>-4.93</td></tr><tr><td>Both</td><td>-5.22</td><td>no</td><td>12/2008</td><td></td><td></td><td>-6.90</td><td>yes</td><td>10/2022</td><td>-5.57</td></tr><tr><td><strong>Log 1st diff</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Intercept</td><td>-12.58</td><td>yes</td><td>12/2020</td><td></td><td></td><td>-5.49</td><td>yes</td><td>11/2014</td><td>-5.34</td></tr><tr><td>Trend</td><td>-12.43</td><td>yes</td><td>01/2013</td><td></td><td></td><td>-5.72</td><td>yes</td><td>04/2023</td><td>-4.93</td></tr><tr><td>Both</td><td>-13.08</td><td>yes</td><td>12/2020</td><td></td><td></td><td>-6.90</td><td>yes</td><td>10/2022</td><td>-5.57</td></tr></tbody></table>

Note: The first sub-column contains the Zivot-Andrews test statistic. If the statistic is more negative than the 1% critical value (shown in the last column), the null hypothesis of non-stationarity is rejected. In this table, 'yes' indicates that the series is stationary, while 'no' indicates that it is non-stationary. The third column reports the date of the most significant structural break identified by the Zivot-Andrews test.

<table><caption>**Table A.2:** Results of the Johansen trace test</caption><thead><tr><th>Rank</th><th>Test</th><th>10%</th><th>5%</th><th>1%</th></tr></thead><tbody><tr><td>r ≤ 3</td><td>1.95</td><td>7.52</td><td>9.24</td><td>12.97</td></tr><tr><td>r ≤ 2</td><td>14.09</td><td>13.75</td><td>15.67</td><td>20.20</td></tr><tr><td>r ≤ 1</td><td>26.38</td><td>19.77</td><td>22.00</td><td>26.81</td></tr><tr><td>r = 0</td><td>84.94</td><td>25.56</td><td>28.14</td><td>33.24</td></tr></tbody></table>

Note: The Johansen trace test determines the number of cointegrating relationships in the system. The null hypothesis ($H_0$) states that there are at most r cointegrating vectors. If the trace statistic exceeds the critical value at a given significance level (e.g., 1%, 5%, or 10%), $H_0$ is rejected. The optimal lag length was set to 2, based on the Akaike (AIC) and Schwarz Bayesian (BIC) information criteria.
<table><caption>Table A.3: Results of the Gregory-Hansen test</caption><thead><tr><th>Model</th><th>Test</th><th>Statistic</th><th>Breakpoint</th><th>Date</th><th>1%</th><th>5%</th><th>10%</th></tr></thead><tbody><tr><td rowspan="3">Level</td><td>ADF</td><td>-7.28</td><td>361</td><td>01/2015</td><td>-5.77</td><td>-5.28</td><td>-5.02</td></tr><tr><td>Zt</td><td>-8.32</td><td>358</td><td>10/2014</td><td>-5.77</td><td>-5.28</td><td>-5.02</td></tr><tr><td>Za</td><td>-102.12</td><td>358</td><td>10/2014</td><td>-63.64</td><td>-53.58</td><td>-48.65</td></tr><tr><td rowspan="3">Trend</td><td>ADF</td><td>-7.28</td><td>221</td><td>05/2003</td><td>-6.05</td><td>-5.57</td><td>-5.33</td></tr><tr><td>Zt</td><td>-8.32</td><td>93</td><td>09/1992</td><td>-6.05</td><td>-5.57</td><td>-5.33</td></tr><tr><td>Za</td><td>-106.80</td><td>93</td><td>09/1992</td><td>-70.27</td><td>-59.76</td><td>-54.94</td></tr><tr><td rowspan="3">Regime</td><td>ADF</td><td>-7.56</td><td>221</td><td>05/2007</td><td>-6.51</td><td>-6.00</td><td>-5.75</td></tr><tr><td>Zt</td><td>-8.69</td><td>348</td><td>03/2007</td><td>-6.51</td><td>-6.00</td><td>-5.75</td></tr><tr><td>Za</td><td>-109.41</td><td>348</td><td>03/2007</td><td>-80.15</td><td>-68.94</td><td>-63.42</td></tr><tr><td rowspan="3">Regime and Trend</td><td>ADF</td><td>-7.56</td><td>221</td><td>05/2003</td><td>-6.89</td><td>-6.32</td><td>-6.16</td></tr><tr><td>Zt</td><td>-8.68</td><td>219</td><td>03/2003</td><td>-6.89</td><td>-6.32</td><td>-6.16</td></tr><tr><td>Za</td><td>-110.03</td><td>219</td><td>03/2003</td><td>-90.84</td><td>-78.87</td><td>-72.75</td></tr></tbody></table>

Note: All Gregory-Hansen test statistics are more negative than the corresponding critical values, leading to the rejection of the null hypothesis of no cointegration. This provides evidence of a cointegrating relationship among the endogenous variables allowing for a structural break.
<table><caption>Table A.4: Results of the VECM estimation</caption><thead><tr><th></th><th>ECT</th><th>Intercept</th><th>UP(-1)</th><th>EC(-1)</th><th>CP(-1)</th><th>REER(-1)</th><th>Temp. anomalies</th><th>Sub. crisis</th><th>2011 break</th><th>Ukraine war</th><th>COVID-19</th></tr></thead><tbody><tr><td>Urea price (UP)</td><td>-0.2657***<br>(0.0340)</td><td>1.3556***<br>(0.1750)</td><td>0.1726**<br>(0.0592)</td><td>-0.1012<br>(0.0821)</td><td>-0.0106<br>(0.0991)</td><td>-0.8635.<br>(0.4738)</td><td>0.1187**<br>(0.0390)</td><td>0.0356<br>(0.0387)</td><td>0.0525*<br>(0.0225)</td><td>0.0931*<br>(0.0466)</td><td>0.0845*<br>(0.0341)</td></tr><tr><td>Energy price index (EP)</td><td>-0.0093<br>(0.0286)</td><td>0.0566<br>(0.1473)</td><td>-0.0221<br>(0.0498)</td><td>0.1215.<br>(0.0691)</td><td>-0.1694*<br>(0.0834)</td><td>-0.9588*<br>(0.3986)</td><td>-0.0011<br>(0.0328)</td><td>-0.0448<br>(0.0325)</td><td>0.0003<br>(0.0189)</td><td>0.0245<br>(0.0392)</td><td>-0.0329<br>(0.0287)</td></tr><tr><td>Crop prices (CP)</td><td>-0.0638**<br>(0.0242)</td><td>0.3263**<br>(0.1243)</td><td>-0.0102<br>(0.0421)</td><td>-0.2126***<br>(0.0583)</td><td>0.1658*<br>(0.0704)</td><td>-0.6933*<br>(0.3365)</td><td>0.0364<br>(0.0277)</td><td>-0.0175<br>(0.0275)</td><td>0.0090<br>(0.0160)</td><td>0.0577.<br>(0.0331)</td><td>-0.0246<br>(0.0242)</td></tr><tr><td>Equation REER</td><td>-0.0051<br>(0.0032)</td><td>0.0251<br>(0.0167)</td><td>0.0094.<br>(0.0056)</td><td>-0.0026<br>(0.0078)</td><td>0.0059<br>(0.0095)</td><td>0.3308***<br>(0.0452)</td><td>0.0014<br>(0.0037)</td><td>0.0064.<br>(0.0037)</td><td>0.0041.<br>(0.0021)</td><td>0.0032<br>(0.0044)</td><td>0.0011<br>(0.0033)</td></tr></tbody></table>

Notes: All coefficients are estimated in the VECM. Standard errors are shown in parentheses. ***, ** and * denote significance at the 1%, 5% and 10% levels, respectively. All endogenous variables are in first difference.
<table><caption>**Table A.5:** Sensitivity of VECM cointegrating vector to lag specification</caption><thead><tr><th></th><th>Lag</th><th>Energy price index (EP)</th><th>Crop price (CP)</th><th>REER</th></tr></thead><tbody><tr><td>Urea price (UP)</td><td>1</td><td>0.68</td><td>0.07</td><td>-0.97</td></tr><tr><td>Urea price (UP)</td><td>12</td><td>0.79</td><td>0.14</td><td>-0.81</td></tr></tbody></table>