EFFECT


1 Agricultural Economics và Policy Group, ETH Zürich, Zürich, Switzerland

2 Grassland Sciences Group, ETH Zürich, Zürich, Switzerland


Sergei Schaub https://orcid.org/0000-0001-8477-3737

Robert Finger https://orcid.org/0000-0002-0634-5742


Received 17 October 2019 Revised 18 December 2019 Accepted 7 January 20đôi mươi Published 19 February 20đôi mươi


và are summarized as follows (see, e.g., Alam and Gilbert 2017):




represents prices, for example wholesale prices, of the agricultural product, i.e. ,$" align="top"> is the transport costs và the transaction costs. Whether buyers or sellers bear the transport và transaction costs depends on the market (power) of the different parties (e.g. Graubner et al 2011), therefore we state them explicitly in equations (1) and (2). và are vectors of variables: $" align="top"> & ,$" align="top"> where và are the respective sầu demand và supply shifting variables. cảnh báo that we denote droughts separately from the other demand & supply shifting variables as We consider droughts at the regional level (i.e. in South Germany) or at the national level (i.e. in the whole of Germany)5, i.e. & respectively. và are random shoông chồng variables.

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Using equations (1) & (2), the change in storage, can be expressed as



Note that while we assume intra-annual adjustments of these storage levels, we expect no changes in storage levels across periods. Moreover, storage can be seen as part of the market characteristics & the presence of storage tends to lớn buffer price shocks (Serra and Gil 2012).

We focus here on the impact of drought on prices. Thus, using equation (3) we can obtain the inverse dem& function, i.e. the price function (sensu Alam và Gilbert 2017),


where $" align="top"> và .$" align="top">

How prices in one region react lớn (drought) shocks, depends amongst other things on costs for transport and transactions, as these costs affect market integration (Goodwin & Piggott 2001, Balcombe et al 2007), và thus how production and price shocks in one region can be balanced by other regions. Costs for transport and transaction depend on the distance between buyer và seller, (for transaction costs, because closer markets are usually better known), and are affected by drought since droughts are systemic to a region. Additionally, transaction costs depover on the transparency of the market, Furthermore, prices might not respond immediately but temporally delayed to shocks. The response time of a market lớn a shoông xã, is assumed to depkết thúc on as well as on change in storage, Hence, we can express the price function as


To analyze the effect of drought on feed prices we use a structural vector autoregressive sầu mã sản phẩm (SVAR; see, e.g., Lütkepohl 2005). SVAR models can be used lớn Mã Sản Phẩm the effect of an exogenous drought shochồng on endogenous feed prices using time series data.6 Using a SVAR Mã Sản Phẩm allows us to identify immediate & lagged effects of drought on feed prices; therefore, we allow that market participants can adjust their price expectations based on expected yields, và thus also expected drought-induced yield losses.7 The SVAR is defined as


Here is the vector of variables in period including a drought variable & feed prices, i.e. ,$" align="top"> và is the number of lags. for are the coefficient matrices is an identity matrix and is the structural error, which is assumed lớn be white noise. Multiplying equation (6) by the inverse of results in


where is the vector of reduced form residuals and its variance–covariance matrix. We restrict the mã sản phẩm by using the "canonical form" (see Appendix 1 for is available online at stacks.cheap-kenya-vacation-tips.com.org/ERL/15/034014/mtruyền thông details).

To identify the optimal length, we employ the Akaike information criterion (AIC). Furthermore, we used an augmented Dickey–Fuller (ADF) unit root kiểm tra with a constant khổng lồ chạy thử for stationarity of the different price time series and without a constant lớn kiểm tra for stationarity of the different drought time series (see, e.g., Pfaff et al 2016). Based on the estimated coefficients, we use impulse response functions to analyze the effect of drought shocks, i.e. "drought effects", on prices. The impulse response functions show the effect over time of an exogenous impulse, here drought shock, on endogenous variables, here feed prices. They are useful because estimated SVAR coefficients alone are difficult lớn interpret. The shock to lớn the impulse response function equals one standard deviation of the drought variable.8 This empirical framework allows us lớn identify the different responses proposed in the theoretical framework, i.e. with respect lớn magnitude and timing of the response. Furthermore, the theoretical framework provides reasons why prices react differently lớn drought. Our analysis is conducted in R (R Vi xử lý Core Team 2018) using the R-packages "vars" and "urca" (Pfaff 2008, Pfaff et al 2016).9


The price data include prices of xuất xắc, feed wheat và barley from August 2002 to lớn April 2019 from the German states of Bavaria & Baden-Württemberg, together referred to lớn as "South Germany" & were provided by the Bavarian Association of Farmers. South Germany represents about 30% of Germany"s xuất xắc production & 20% of its wheat và barley production10 (Destatis 2019). Hay prices (Euro 100 kg−1) were reported as a bi-weekly average wholesale price ex-farm including value added tax for high-pressure pressed tốt.11 Feed wheat và barley prices (triệu Euro 100 kg−1) were reported as weekly average wholesale purchase prices from producers excluding value added tax. We converted prices into monthly natural long transformed real prices using the harmonized12 index of consumer prices for Germany with the base year 2015 (Eurostat 2019; figure 1, see table A1 for summary statistics). These prices are henceforth indicated as tuyệt, feed wheat & feed barley prices. The optimal lag length, of the price time series is 3 months based on the AIC và the ADF unit root chạy thử indicates that all price time series are stationary (table A2).


To identify droughts we used the Standardized Precipitation Evapotranspiration Index (SPEI). The SPEI incorporates information about precipitation and potential evapotranspiration (Vicente-Serrano et al 2010). Thus, the SPEI also accounts for the impact of high temperature on drought intensity as temperature strongly affects evapotranspiration (Vicente-Serrano et al 2010, Beguería et al 2014). We used different SPEI lengths that comprise information about the last X months (SPEI-X). The drought variables were defined as drought, i.e. as when SPEI-X was below a specific threshold and otherwise as

We focus on the occurrence of drought during the entire main vegetation period13 (April—October). In the robustness checks, we also separately considered droughts in spring (April–May) and summer (June—August).14

We used monthly potential evapotranspiration và precipitation data from January 1991 khổng lồ April 2019 provided by the German Meteorological Office as 1 km × 1 km gridded data (DWD 2019). SPEI-X15 was calculated for every 1 km × 1 km grid of the agricultural area in (i) South Germany và (ii) the whole of Germany. To identify the agricultural area16 we used the 2012 "CORINE Land Cover 10 ha" data (BKG 2019). For both regions, South Germany & the whole of Germany, we then calculated the monthly average SPEI-X over all grid cells and the drought variable. The spatial aggregation of droughts is in line with their systemic nature, i.e. droughts usually affect larger areas (Mirandomain authority & Glauber 1997), and market prices are an expression of the aggregated market supply và demvà. All drought time series are stationary (table A2).

The main drought specification used here reflects a "severe drought", i.e. threshold = −1.5 (Yu et al 2014), based on SPEI-3. Figure 2 shows severe droughts for South Germany và the whole of Germany for the different drought periods using SPEI-3. For this specification, the correlation between South Germany and the whole of Germany of the SPEI and severe droughts were 0.90 & 0.84, respectively (see figure A1 available online at stacks.cheap-kenya-vacation-tips.com.org/ERL/15/034014/mtruyền thông media for more details). Additional specifications are given in table 1.


We found that a drought shoông chồng, i.e. "drought effects", in South Germany led to a substantial increase in tuyệt prices, up lớn +13% in month five after the shoông chồng (figure 3 and table 2).17 The hay price increase lasted from month 3 khổng lồ month 16 after the drought shoông chồng (see figure 3 & tables 2, A4 and A5 for details on other than the 5% significance level). Germany-wide drought shocks resulted in similar effects on xuất xắc prices, which peaked at +15% and lasted from month 3 to month 14 after the drought shock However, we found no significant effects of drought on feed grain prices, independent of whether the drought occurred in South Germany or the whole of Germany.

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Table 2. Drought effects (peak & duration) for different drought specifications.

   SPEI-3SPEI-2SPEI-4   −1−1.5−1−1.5−1−1.5
Droughts in South GermanyHay priceMain vegetation period11% (4–14)13%% (3–16)8% (3–12)13% (3–14)12% (4–11)16% (3–14)
  Spring
  Summer10% (4–12)14% (3–14)10% (3–12)12% (3–13)9% (4–8)15% (4–14)
 Feed wheat priceMain vegetation period
  Spring8% (1–6)
  Summer
 Feed barley priceMain vegetation period
  Spring4% (1–2)2% (1–1)
  Summer
Droughts in whole of GermanyHay priceMain vegetation period12% (3–13)15% (3–14)8% (4–13)12% (3–12)12% (3–12)17% (3–13)
  SpringNA
  Summer10% (4–11)15% (3–14)10% (3–13)12% (3–12)10% (4–10)16% (3–13)
 Feed wheat priceMain vegetation period6% (9–9)
  SpringNA
  Summer
 Feed barley priceMain vegetation period6% (8–10)
  Spring2% (1–1)NA
  Summer

Remark: The effects of drought in South Germany and the whole of Germany are derived from the impulse response function (figure 3). Percentages indicate the peak effects & numbers in parentheses the start và over month of the effects. We only report values when effects were significant at the 5% cấp độ (for other significance levels see tables A4 & A5). Gray shaded cells indicate the baseline drought specification và NA the specification without drought observation. We note that results are similar when droughts are computed for all areas of South Germany & Germany và not only for the agricultural areas.


In our robustness checks we varied the drought specification with respect to lớn the timing of drought, SPEI length & drought threshold (table 2). Considering only droughts in spring or summer, we found that summer droughts (at regional and national level) caused increases in giỏi prices. In contrast, we found no effects of spring droughts on tốt prices. The effects of drought on feed grain prices remained absent in South Germany for spring or summer droughts in almost all cases. On the national level, we also found generally no effect of spring or summer droughts on feed grain prices (table 2). When altering SPEI length from SPEI-3 khổng lồ SPEI-2 or SPEI-4, the effects of drought on xuất xắc prices remained similar. For feed grain prices, we discovered drought effects in some cases when drought specification was based on SPEI-2, whereas for the other SPEI lengths no effects of drought were present (table 2). Decreasing the threshold for drought severity from −1.5 (severe drought) to lớn −1.0 (moderate drought) decreased the magnitude & duration of the effects of drought on tuyệt prices. The choice of threshold did not influence the effects of drought on feed grain prices.18


We have shown that droughts at regional and national levels caused substantial increases in hay prices (up lớn +15%), while feed grain prices were, in our case study, not affected by droughts. This indicates that feed grain markets are—in contrast to lớn hay markets—organized at higher than regional or national levels và thus react less to lớn regional or national drought shocks. These responses confirm our theoretical and market assumptions, i.e. that prices of markets with relatively low market integration due khổng lồ high transport và transaction costs respond more strongly lớn drought shocks. Furthermore, giỏi prices did not react immediately to lớn droughts, but drought responses occurred with a delay (about 3 months), và drought-induced price shocks were long lasting (usually for over a year). These observations are in line with our theoretical model & the assumption of relatively low transparency of the tốt market. Therefore, our analysis highlights the importance of considering transport & transaction costs with respect lớn their value to lớn understand price sensitivity to lớn regional shocks such as droughts. In general, regional & national droughts were highly correlated, which is in line with the systemic nature of droughts và explains the similar reaction to lớn regional & national droughts. Climate change will increase the probability of occurrence & the magnitude of droughts. The price sensitivity of the hay market identified here represents an additional severe risk lớn the agricultural and livestochồng sector, next lớn the risk of yield loss. Farmers may suffer from low feed production và exceptionally high prices for the additional feed bought. Similar arguments about responses to lớn drought can also hold true for other markets with low-value-to-weight products, low market transparency, low trade quantities and/or with a lachồng of formal market exchanges, và particularly for agricultural markets in developing countries that often exhibit high national và international trade costs, i.e. transport and transaction costs, và thus low market integration (Porteous 2019). Knowledge about the responses of feed price lớn drought is important lớn include in farm management & policy actions, especially under future climatic scenarquả táo. Here, for example, online feed price exchanges might contribute lớn reduce price shocks as they increase market transparency.

Droughts based on the SPEI cover important events of low precipitation & high temperature, which together increase the intensity of droughts and often occur together (Trenberth & Shea 2005, Estrella và Menzel 2013). Next to these events other extreme weather events, for example extreme high/low temperature and precipitation on their own as well as interactions other than high temperature và low precipitation, might also be important (e.g. Rosenzweig et al 2002, Schlenker & Roberts 2009, Barlow et al 2015, Taông xã et al 2017) for feed và other agricultural prices; this remains an important area for future retìm kiếm.


The data that support the findings of this study are openly available at Schaub and Finger (2019; https://doi.org/10.3929/ethz-b-000385361) & https://doi.org/10.25412/cheap-kenya-vacation-tips.com.11371254.v1.