Wheat yield and tillage–straw management system × year interaction explained by climatic co-variables for an irrigated bed planting system in northwestern Mexico

Field Crops Research 124 (2011) 347–356

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Wheat yield and tillage–straw management system × year interaction explained by climatic co-variables for an irrigated bed planting system in northwestern Mexico Nele Verhulst a,b , Ken D. Sayre a , Mateo Vargas a,c , Jose Crossa a , Jozef Deckers b , Dirk Raes b , Bram Govaerts a,∗ a

International Maize and Wheat Improvement Centre (CIMMYT), Apdo Postal 6-641, 06600 Mexico, D.F., Mexico Katholieke Universiteit Leuven, Department of Earth and Environmental Sciences, Celestijnenlaan 200 E, 3001 Leuven, Belgium c Universidad Autónoma Chapingo, Chapingo, México 56230, Mexico b

a r t i c l e

i n f o

Article history: Received 8 March 2011 Received in revised form 4 July 2011 Accepted 4 July 2011 Keywords: Conservation agriculture Permanent raised beds Factorial regression Partial least square regression

a b s t r a c t Wheat is an important food and income source and estimated demand for wheat in the developing world is projected to increase substantially. The objectives of this study were to gain insight into (i) the effect of tillage–straw system on yield and yield components (number of grains per m2 and thousand kernel weight), (ii) the relation between climatic conditions and yield and yield components, (iii) the explanation of tillage–straw system × year interaction for yield and yield components by climatic co-variables. Wheat grain yield and yield components were measured in a long-term trial established in 1992 under irrigated, arid conditions in northwestern Mexico. Five tillage–straw management systems (conventionally tilled raised beds [CTB] with straw incorporated and permanent raised beds [PB] with straw burned, removed, partly retained or fully retained) were compared for a wheat–maize rotation. Daily climatic data were averaged over six periods corresponding approximately to advancing wheat growth stages. The PB-straw retained and PB-straw removed had the highest yields (average yield of 7.31 and 7.24 t ha−1 , respectively) and grains per m2 . The PB-straw burned had the lowest yield (average yield of 6.65 t ha−1 ) and grains per m2 , but the highest thousand kernel weight. Maximum temperature was positively correlated to final grain yield during tillering and head differentiation, but was negatively correlated to thousand kernel weight during grain-filling. For the tillage–straw system year interaction, three groups of management systems were distinguished for yield and grains per m2 : PB-straw burned, CTB-straw incorporated and PB where straw is not burned. The CTB-straw incorporated had a positive interaction with year in favorable years with high radiation and evapotranspiration. The PB-straw burned was relatively more affected by excess water conditions and showed positive interactions in years with high relative humidity. The PB-straw retained was the most stable in different climatic conditions, indicating that this management system could contribute to maintaining wheat yield in a changing climate scenario. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Despite being considered a temperate species, wheat is grown on approximately 220 million ha annually in a wide variety of environments ranging from very favorable in Western Europe to

Abbreviations: CTB, conventionally tilled raised beds; PB, permanent raised beds; FR, factorial regression; PLS, partial least square regression; Tmx, maximum temperature; Tmn, minimum temperature; H, average air relative humidity; P, precipitation; E, reference evapotranspiration; R, solar radiation; S × E interaction, agronomic management system by environment interaction. ∗ Corresponding author. Tel.: +52 55 5804 2004; fax: +52 55 5804 7558. E-mail address: [email protected] (B. Govaerts). 0378-4290/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2011.07.002

severely moisture and temperature stressed in parts of Asia, Africa, and Australia (Braun et al., 2010). Wheat is an important food and income source and demands in the developing world are projected to increase by 60% by 2050 (Rosegrant et al., 2009). Meeting this growing demand is challenged by poor productivity growth or stagnation in the green revolution areas of South Asia and low yields in Africa. Climate change could affect wheat production and further complicate meeting the demand. The potential effects of climate change on wheat production depend greatly on local conditions. While future climate scenarios may be beneficial for wheat in high latitudes, global warming will reduce productivity in regions where favorable temperatures already exist, for example in the Indo-Gangetic Plains of South Asia (Ortiz et al., 2008). Asseng et al. (2011) state that the effect of temperature on wheat production has

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been underestimated. Using a modeling approach, they found that the observed variations of ±2 ◦ C in average growing-season temperatures in the main wheat growing regions of Australia could cause yield reductions of up to 50%. In this respect, it is important to gain insight into the effects of climatic variables on grain yield and yield components and determine whether cropping system management could help to adapt to climate change. The differential response of agronomic management systems to environmental changes is related to agronomic system × environment (S × E) interaction. Studying the interaction between agronomic management systems and environments or years with different climatic conditions could yield insights into how management systems react to climatic conditions. Genotype × environment interactions in multi-environment trials have been studied intensively. Statistical tools have been developed that incorporate a large number of external variables (environmental and genotypic variables) into the analysis of these trials in order to explain genotype × environment interaction and predict the performance of new cultivars in future years and new locations (Crossa et al., 2010). These models are also useful for the interpretation of S × E interaction. Two of these models are the factorial regression (FR) model (Denis, 1988; van Eeuwijk et al., 1996) and the partial least squares (PLS) regression method (Aastveit and Martens, 1986). The results of the multiplicative decomposition obtained from PLS can be presented graphically in the form of a biplot with treatments, environments and co-variables represented as vectors in a two-dimensional space. This paper includes data from a furrow-irrigated, long-term trial (initiated in 1992) in the Yaqui Valley in north-western Mexico. The Yaqui Valley is agro-climatically representative of areas where 40% of the wheat is produced in the developing world, such as the Indian and Pakistani Punjab and the Nile Valley in Egypt. In the Yaqui Valley, more than 95% of the region’s farmers have switched from using flood irrigation on the flat to planting on raised beds over the past 25 years (Aquino, 1998). One to four rows are planted on top of the bed, depending on the bed width and crop, with gravity irrigation applied in the furrow. Farmers growing wheat on beds obtain 8% higher yields and save nearly 25% in production costs, compared with the flood irrigation systems (Aquino, 1998). Wheat grain yields in the area exceed 6 t ha−1 and input levels are high, e.g. the average N rate on-farm for wheat is 275 kg N ha−1 . Another change in farmer practices in the Yaqui Valley has been crop residue management. In the 1992/93 cycle, residues were burned by 95% of the farmers. By 2001, however, 96% of the farmers were no longer burning but incorporating the residue after a campaign to enforce the prohibition of burning and raise awareness with farmers about the importance of organic matter. The next logical step to increase the sustainability of beds is to make them permanent, avoiding tillage (only reshaping the beds as needed) and retaining and distributing crop residues on the surface. In the long-term trial, five agronomic management systems are compared in a maize–wheat rotation including conventionally tilled raised beds (CTB) with all crop residues incorporated and permanent raised beds (PB) with crop residue management varying from full to partial retention and burning. Three groups of tillage–straw systems with different characteristics in relation to the soil environment were distinguished: PB-straw burned, CTBstraw incorporated, and PB-straw not burned. The PB-straw burned had high electrical conductivity, Na concentration and penetration resistance and low soil resilience and aggregation. The CTB-straw incorporated was distinguished from the PB practices by the soil physical variables, especially the low direct infiltration and aggregate stability, indicating degradation of physical soil quality in this system (Verhulst et al., 2011). Sayre et al. (2005) reported that in the first 5 years of the experiment no significant differences in

Precipitaon and Evapotranspiraon (mm)

348

300

40 35

250

30 200

25

150

20 15

100

10 50

5 0

0 1

2

3

4

5

6

7

8

9

10

11

12

Month Precipitaon Minimum temperature

Reference evapotranspiraon Maximum temperature

Fig. 1. Climate in Ciudad Obregón, Sonora, Mexico.

wheat yield were found between the different management systems. However, after 5 years, yield started to decrease in PB-straw burned compared to the other management systems. Lobell and Ortiz-Monasterio (2007) reported a roughly 10% decrease in wheat yields in the Yaqui Valley for each degree increase in January–April average minimum temperature. Maximum temperature had an apparently negligible effect on yield, but this was attributed to the positive correlation between maximum temperature and solar radiation, so that the negative effects of higher maximum temperature could have been canceled by the positive effects of increased radiation. Number of grains per m2 was positively correlated to solar radiation in the 30 days prior to flowering when artificial shading was applied to the irrigated wheat crop and negatively correlated to mean temperature (Fischer, 1985). The objectives of this study were (i) to evaluate the effect of tillage–straw management, year and their interaction on wheat grain yield and yield components (grains per m2 and thousand kernel weight) in an irrigated bed planting system; (ii) analyze the relationships of grain yield and yield components with climatic variables in different wheat growth stages; (iii) determine the most relevant climatic variables that influence management system × year interaction of grain yield and yield components. 2. Materials and methods 2.1. Characterization of the experimental site The long-term trial was conducted at the experiment station CENEB (Campo Experimental Norman E. Borlaug), near Ciudad Obregón, Sonora, Mexico (lat. 27.33◦ N, long. 109.09◦ W, 38 m a.s.l.). The climate is arid, with an annual rainfall of approximately 320 mm, and an annual reference evapotranspiration of approximately 1800–2000 mm. Between 1986 and 2009, annual rainfall ranged from 90 to 590 mm demonstrating the high level of rainfall variability at the site (coefficient of variation = 40%). Rainfall is summer dominant and only 20% of the average annual rainfall occurs during the wheat growing season (November–May). The mean annual temperature is 24 ◦ C. Mean monthly temperatures range from 16.3 ◦ C in January to 30.8 ◦ C in July/August (Fig. 1). The soil is a Hyposodic Vertisol (Calcaric, Chromic) according to World Reference Base (Verhulst et al., 2009; IUSS Working Group, 2007) and a fine, smectitic Chromic Haplotorrert in Soil Taxonomy (Verhulst et al., 2009; Soil Survey Staff, 2010). It is low in organic matter (< 1%) and slightly alkaline (pH around 8).

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2.2. Description of the long-term trial The experiment was initiated in 1992 and consisted of a randomized complete block design with a split plot treatment arrangement and three replicates of each treatment. The cropping system was an annual rotation of maize and wheat with furrow irrigation (one pre-planting irrigation of approximately 120 mm and four auxiliary irrigations of 80–100 mm): the winter crop wheat was planted in late November to early December and harvested in May, followed by a summer crop maize planted in June and harvested in October. The whole experiment was planted on the same day or in two consecutive days. Both crops were planted on 0.75 m raised beds with wheat in two rows seeded 24 cm apart and maize seeded in one row in the center of the bed. Traffic was confined to the furrows and narrow tractor tires were used for all operations. Main treatments consisted of tillage–straw management systems as follows: (1) CTB-straw incorporated: Conventionally tilled raised beds (CTB; tilled after each crop with a disk harrow to 20 cm after which new beds were formed); wheat and maize residues were incorporated by the tillage operations; (2) PB-straw burned: Permanent raised beds (PB; zero tillage with continual reuse of existing beds, which were reformed in the furrows without disturbance of the tops of the beds as needed); residues of both wheat and maize were burned; (3) PB-straw removed: PB; residues of wheat and maize were removed by baling, leaving about 30% of the total residue in the field; (4) PB-straw partly retained: PB; maize residues were removed by baling and wheat straw was retained on the soil surface; (5) PB-straw retained: PB; maize and wheat residues were kept on the soil surface. Split plots during the winter measured 6 m × 13 m and the split plot treatments comprised seven N fertilizer treatments differing in dose and timing of application. A treatment without N shortage (basal application of 300 kg N ha−1 ) was chosen for this study in order to exclude N fertilization as a factor in the analyses. Maize received a uniform application of 150 kg N ha−1 . The N fertilizer was applied as urea in the bottom of the furrow and incorporated through irrigation. Each year wheat and maize received 45 kg P2 O5 ha−1 banded in the furrow and incorporated through cultivation when reshaping beds. 2.3. Yield and yield components A random sample of 50 entire tillers cut at soil level was taken after physiological maturity in each split-plot. This sample was oven-dried for 48 h at 65 ◦ C and threshed. Thousand kernel weight was determined by counting a subsample of 200 grains and determining dry weight. Wheat was combine-harvested from the two center beds in each plot. The number of grains per m2 was calculated as yield/(thousand kernel weight/1000). 2.4. Climate data The climatic data were obtained from a standard weather station at the experimental site. The daily data included: maximum and minimum temperature (Tmx and Tmn), average air relative humidity (H), precipitation (P), reference evapotranspiration (E, derived from pan evaporation) and solar radiation (R). Since planting dates varied from November 16 to December 16, depending on the climatic conditions of the season, we chose to average the climatic data over periods with reference to plant stage rather than calendar weeks or months. The growing season was divided into six

349

periods between 50% plant emergence and physiological maturity to correspond approximately to wheat growth stages. The beginning of flowering marked the start of period 5. Periods 4 (head emergence) and 5 (flowering and grain-setting) were set to consist of 14 days. The remaining days before period 4 were divided in three periods: period 1 of approximately 2 weeks (emergence), period 2 of approximately 4 weeks (tillering and head differentiation) and period 3 of approximately 2 weeks (stem and head growth). Period 6 (grain-filling) consisted of the remaining days after period 5 (approximately 4 weeks). 2.5. Statistical analysis All years of the experiment were included in analyses where the effect of tillage–straw system was not considered, i.e. for the calculation of the correlations between grain yield and yield components and climatic variables. When treatment effects were taken into account, only data from 1999 to 2009 were included (year 7–18 of the experiment) to ensure that effects of tillage–straw systems were well established in the experiment. Analysis of variance of grain yield, grains per m2 and thousand kernel weight data was conducted with SAS PROC GLM (SAS Institute, 2010). Principal component analysis (PCA) was performed with R prcomp (cran.r-project.org) to construct a lower-dimension summary of the climatic variables. With PCA, the correlated climatic variables are described in terms of a new set of uncorrelated variables (principle components), each of which is a linear combination of the original variables. The PCA analysis was interpreted graphically by constructing a biplot (Everitt, 2005). Correlations between grain yield and yield components and climatic variables in different wheat growth stages were determined with SAS PROC CORR (SAS Institute, 2010). Significant correlations were determined at P < 0.05. Two statistical methods, i.e. factorial regression (FR) and partial least squares (PLS) analysis, which have been developed to explain genotype × environment interaction were used in this paper to study the agronomic system × environment (S × E) interaction as described in Vargas et al. (2001). The five tillage–straw management systems were the agronomic systems and the different years were the environments. The analysis were performed on the S × E interaction matrix with GENSTAT and SAS to interpret the interactions between tillage–straw management and years and to attempt to explain the environmental causes of this interaction for response variables grain yield, thousand kernel weight and number of grains per m2 using the climatic variables. 2.5.1. Factorial regression The FR model is a useful linear model for incorporating external environmental variables (Denis, 1988; van Eeuwijk et al., 1996). The aim of FR is to replace, in the S × E subspace, agronomic system and environmental factors with a small number of agronomic system and environmental co-variables. In FR, agronomic system co-variables, xa (a = 1. . .A) with values xia , can be introduced for the agronomic system main effect, Gi : Gi = xia a + residual

(1)

where a is the regression coefficient for the regression of Gi on xa . For more than one agronomic system co-variable, this becomes Gi =

A 

xia a + residual

(2)

a=1

Analogous to the agronomic system main effect, in FR, the environmental main effect, Ej , can also be regressed on environmental co-variables, zb with values zjb . The corresponding partitioning is Ej = zjb ˇb + residual

(3)

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for one environmental co-variable, or Ej =

B 

zjb ˇb + residual

(4)

b=1

for multiple environmental co-variables. The parameters ˇb represent the regression coefficients of the regression of the environmental main effect on zb . 2.5.2. Partial least squares When environmental (or agronomic system) co-variables show high collinearity, interpretation of the least squares regression coefficients from the FR is complicated because they are estimated very imprecisely. Noise on the response variable also complicates the interpretation of FR parameters. Furthermore, least squares estimation of parameters in FR models is not unique when the number of co-variables is larger than the number of observations; therefore, an alternative estimation method is needed. Partial least squares (PLS) regression can be used. The PLS algorithm performs separate (but simultaneous) principle component analysis of the environmental co-variables (i.e. the climate data) and the response variable matrix (i.e. the S × E interaction matrix for yield, grains per m2 or thousand kernel weight) that allows reducing the number of variables in each system to a smaller number of hopefully more interpretable latent variables (Aastveit and Martens, 1986; Crossa et al., 2010). The results can be summarized graphically in a biplot that contains agronomic systems, environments and co-variables. In that biplot, a perpendicular projection of the agronomic systems on one environment vector, extended in either direction, gives the relative values of the systems for the S × E interaction. Multivariate partial least squares regression models (Aastveit and Martens, 1986; Helland, 1988) are a special class of bilinear models. When agronomic system responses over environments (Y) are modeled using environmental co-variables, the J × H matrix Z of H (h = 1.2,. . .,H) environmental co-variables can be written in bilinear form as Z = t1 p1 + t2 p2 + · · · + tM pM + EM = TP  + E

(5)

where the matrix T contains the t1. . . tJ J × 1 vectors called latent environmental co-variables or Z-scores (indexed by environments) and the matrix P has the p1 . . .pH H × 1 vectors called Z-loadings (indexed by environmental variables), and E has the residuals. Similarly, the response variable matrix Y in bilinear form is Y = t1 q1 + t2 q2 + · · · + tM qM + FM = TQ  + F

(6)

where the matrix T is as in Eq. (5), the matrix Q contains the q1 . . .qI I × 1 vectors called Y-loadings (indexed by agronomic systems), and F has the residuals. The relationship between Y and Z is transmitted through latent variable T. A singular value decomposition of the matrix product Z Y allows reducing the number of variables in each system to a smaller number of hopefully more interpretable latent variables. 3. Results 3.1. Characterization of climatic conditions in the different growing seasons The principal component analysis of the climatic variables separated four clusters of growing seasons in the biplot of the first two principal components (PCs) which accounted together for 50.3% of the variance in the original variables (Fig. 2). The seasons with harvest in 2003 and 2005 had high positive loadings on the first PC, associated with high minimum temperature before flowering (period 1, 2, 3, 4) and precipitation and humidity at head emergence

Fig. 2. Biplot of the first two components of the principal component analysis of climatic variables for 1999–2009 for CIMMYT’s long-term trial, Yaqui Valley, Mexico. Climatic variables: H, relative humidity, R, solar radiation; E, reference evapotranspiration; Tmn, minimum temperature; Tmx, maximum temperature; P, precipitation; periods of the growing season: 1, emergence; 2, tillering; 3, stem elongation and booting; 4, head emergence; 5, flowering and grain setting; 6, grain filling. Scale for observations indicated in black and for variables in grey.

(period 4) and low radiation and evapotranspiration throughout the season. The seasons with harvest in 1999, 2006, 2007 and 2008 were characterized by high radiation and evapotranspiration in most periods. The season with harvest in 2004 was separated from all other seasons by a high positive loading on the second PC, associated with high rainfall and humidity during tillering and flowering (period 2 and 5) and high temperature and humidity during grain-filling (period 6). On 14 January 2004 (middle of period 2), an exceptionally high rainfall of 133 mm was measured, resulting in very moist field conditions. At the beginning of flowering, on 23 February 2004, another rainfall event of 37 mm was recorded, making 2004 the wettest of the considered growing seasons. The seasons with harvest in 2000, 2001, 2002 and 2009 had low loadings on both PCs. 3.2. Grain yield, grains per m2 and thousand kernel weight Grain yield varied from 5.23 t ha−1 (PB-straw burned in 2004) to 8.39 t ha−1 (PB-straw retained in 2001) (Fig. 3, Table 1). The main effect of tillage–straw system explained 9.9% of the total sum of squares, the differences between year means contributed 70.3% and the interaction term 7.4% (Table 2). Grain yield was significantly higher in PB-straw retained and PB-straw removed (mean 7.31 and 7.24 t ha−1 , respectively) than in the other tillage–straw systems. The same tillage–straw systems also had the lowest coefficient of variance of grain yield, but the difference with other tillage–straw systems was not significant. PB-straw burned had lower grain yield (mean 6.65 t ha−1 ) than the other tillage–straw systems (Table 1). The lowest number of grains per m2 was 10.9 × 103 grains per m2 (PB-straw burned in 2004) and the highest 17.8 × 103 grains per m2 (PB-straw retained in 2002) (Fig. 3). The main effect of tillage–straw system explained 14.7% of the total sum of squares for grains per m2 , whereas the differences between year means contributed 61.4% and the interaction term contributed 12.4% (Table 2). Differences between tillage–straw systems showed the same trend

N. Verhulst et al. / Field Crops Research 124 (2011) 347–356

351

8.5

(a) Yield at 1 12% H2O (Mg ha-1)

80 8.0 7.5

CTB-straw incorporated

7.0

PB-straw burned 6.5

PB-straw removed

6.0

PB-straw partly retained PB-straw retained

5.5 5.0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Year of harvest (b) 19.0 18.0

10³ grains per m m²

17.0 16 0 16.0

CTB-straw incorporated

15.0

PB-straw burned 14.0

PB-straw removed

13.0

PB-straw partly retained

12.0

PB-straw PB straw retained

11.0 10.0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Year of harvest

Thousand kerrnel weight (g)

(c) 60 55

50

CTB-straw incorporated PB-straw burned PB-straw removed

45

PB-straw partly retained PB-straw retained

40 35 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Year of harvest

8.5

8.5

8.0

8.0

8.0

7.5 7.0 6.5 6.0

R² = 0.309 p = 0.031

5.5 2.0

2.5

3.0

ET0 in period 2 (mm/day)

7.5 7.0 6.5 6.0

R² = 0.299 p = 0.030

5.5 3.5

Grain yield (t/ha)

8.5

Grain yield (t/ha)

Grain yield (t/ha)

Fig. 3. (a) Grain yield, (b) grains per square meter and (c) thousand kernel weight for 1999–2009 for the five tillage–straw systems in CIMMYT’s long-term trial, Yaqui Valley, Mexico.

21

22

23

24

25

26

Tmx in period 2 (°C)

27

7.5 7.0 6.5 6.0

R² = 0.317 p = 0.023

5.5 28

60

65

70

75

80

85

Humidity in period 2 (%)

Fig. 4. Significant correlations between grain yield and climatic variables in CIMMYT’s long-term trial, Yaqui Valley, Mexico. Tmx, maximum temperature; ET0, reference evapotranspiration; Humidity, relative humidity.

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Table 1 Average and coefficient of variation (CV) for grain yield, grains per square meter and thousand kernel weight for 1999–2009 for the five tillage–straw systems in CIMMYT’s long-term trial, Yaqui Valley, Mexico. CTB-straw incorporated

PB-straw burned

Grain yield (Mg ha−1 at 12% H2 O) Mean 7.01 ba CV (%) 10.3 a 103 grains per m2 Mean 13.7 bc CV (%) 13.0 a Thousand kernel weight (g) Mean 45.4 c CV (%) 11.3 a a

6.65 c 11.3 a

PB-straw removed

7.24 a 9.0 a

PB-straw partly retained

PB-straw retained

6.91 b 9.9 a

7.31 a 9.2 a

12.3 d 11.1 a

13.8 b 9.8 a

13.4 c 12.8 a

14.3 a 10.3 a

47.7 a 8.6 a

46.4 b 9.6 a

45.7 c 11.5 a

45.4 c 10.8 a

Tillage–straw systems with the same letter are not significantly different at P < 0.05 (t-test).

as grain yield, with the highest number of grains per m2 in PB-straw retained and the lowest in PB-straw burned (Table 1). Thousand kernel weight ranged between 37.6 g (PB-straw retained in 2004) and 56.2 g (PB-straw burned in 1999) (Fig. 3). The main effect of year explained 88.3% of the total sum of squares, whereas the differences between tillage–straw system means and the interaction term both contributed 3.5% (Table 2). The differences between tillage–straw systems were opposite to the ones found for grain yield and grains per m2 . Thousand kernel weight was the highest in PB-straw burned and the lowest in PB-straw retained and CTB-straw incorporated (Table 1, Fig. 3).

3.3. Correlations between climatic variables and grain yield and yield components The analyses of variance showed that year explained the largest part of the variation in yield and yield components. To explain the large effect of year, correlations between the average yield and yield components per year and the climatic variables for the different periods were calculated. Average yield showed significant correlations with climatic variables during tillering (period 2): a positive correlation with maximum temperature and evapotranspiration and a negative correlation with relative humidity were found (Fig. 4). The correlation between yield and evapotranspiration during grain-filling (period 6) was negative and marginally significant (P = 0.0596; not shown). No significant correlations of climatic variables with average grains per m2 were found. Average thousand kernel weight was positively correlated with evapotranspiration during tillering (period 2). A negative correlation with relative humidity during tillering and maximum temperature during grain-filling was found (period 6; Fig. 5).

3.4. Tillage–straw system × year interactions explained by climatic co-variables The analyses of variance showed that the agronomic system × year interaction was significant for grain yield, grains per m2 and thousand kernel weight (Table 2). Partial least squares (PLS) regression and factorial regression (FR) were used to identify the climatic co-variables associated with this interaction. The PLS results are represented as biplots in which tillage–straw systems, years and their climatic components are displayed (Figs. 6 and 7). The tillage–straw systems and climatic co-variables are shown as points, and years as vectors. The relative position of a tillage–straw system with respect to a year vector is based on their interaction (not on main effects). The significance of a tillage–straw system, year, or co-variable on the biplot is related to distance from the origin. For example in the biplot for the dependable variable yield (Fig. 6), the fact that the year vectors for 2004 and 2005 are the longest indicated that the S × E interaction was most significant in those years. Similarly, climatic variables which were further from the origin (such as relative humidity in periods 1 and 4, H1 and H4, or radiation in period 5, R5) are likely to have a greater influence in determining the S × E interaction. The relationship between any two climatic co-variables, tillage–straw systems or years is defined by the angle formed when the two coordinates are projected as straight lines back to the origin. The more acute the angle, the greater is the relatedness. Angles of 90◦ indicate a zero relationship, angles greater than 90◦ a negative relationship and angles approaching 180◦ indicate strong negative relationships. For example, 2004 was associated with relatively high values of precipitation in period 2 and 5 (P2 and P5) and low values of relative humidiy in periods 1 and 4 (H1 and H4) and the same year had a postive response of PB-straw partly retained.

Table 2 Analysis of variance of grain yield, number of grains per m2 and thousand kernel weight from 1999–2009 in CIMMYT’s long-term trial, Yaqui Valley, Mexico. Source Grain yield Tillage–straw system Year Tillage–straw system × Year Error 103 grains per m2 Tillage–straw system Year Tillage–straw system × Year Error Thousand kernel weight Tillage–straw system Year Tillage–straw system × Year Error

df

Sum of squares

Mean squares

F

P

4 10 40 110

9.24 65.83 6.97 11.56

2.31 6.58 0.17 0.11

21.99 62.64 1.66

<0.0001 <0.0001 0.0207

4 10 40 110

71.56 298.56 60.19 55.59

17.89 29.86 1.50 0.51

35.40 59.08 2.98

<0.0001 <0.0001 <0.0001

4 10 40 110

129.55 3301.61 131.41 178.47

32.39 330.16 3.29 1.62

19.96 203.48 2.02

<0.0001 <0.0001 0.0021

N. Verhulst et al. / Field Crops Research 124 (2011) 347–356

50 45 40 35

R² = 0.565 p = 0.001

30

Thousand kernel weight

Thousand kernel weight (g)

Thousand kernel weight (g)

55

55

55

50 45 40 35

R² = 0.475

2.5

3.0

50 45 40 35

R² = 0.381

p = 0.003 30

2.0

353

3.5

p = 0.011

30 55

60

ET0 in period 2 (mm/day)

65

70

75

80

85

27

28

Humidity in period 2 (%)

29

30

31

Tmx in period 6 (°C)

Fig. 5. Significant correlations between thousand kernel weight and relevant climatic variables in CIMMYT’s long-term trial, Yaqui Valley, Mexico. ET0, reference evapotranspiration; Humidity, relative humidity; Tmx, maximum temperature.

(a)

0.6

-0.2 -0.4 -0.6

-0.2 -0.4

PB-B -0.8

-0.6

P4

05

P1

03 Tmn3

R6

H5 Tmn5 Tmn4 Tmn1 09 H3 P3 Tmn2 02 H2 01 PB-R P6 PB-K E6 PB-P Tmx1 Tmn6 P2

-0.8

-0.8

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Factor 1 (18.60%) Fig. 6. Biplot of the first and second partial least squares (PLS) factors showing years, agronomic management systems and climatic co-variables for grain yield in CIMMYT’s long-term trial, Yaqui Valley, Mexico. Tillage–straw systems (indicated in black squares): CTB, conventionally tilled raised beds; PB, permanent raised beds; B, straw burned; R, straw removed; P, straw partly retained; K, straw retained; climatic covariables (indicated in grey): H, relative humidity, R, solar radiation; E, reference evapotranspiration; Tmn, minimum temperature; Tmx, maximum temperature; P, precipitation; periods of the growing season: 1, emergence; 2, tillering; 3, stem elongation and booting; 4, head emergence; 5, flowering and grain setting; 6, grain filling; growing season (indicated in black): indicated by last two digits of harvest year. The 6 climatic co-variables selected by factorial regression are indicated in bold.

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For grain yield, the first PLS factor explained 18.6% of the S × E interaction and the second factor 15.3%. The first PLS factor separated the CTB-straw incorporated from the PB treatments, whereas the second PLS factor separated the PB-straw burned from the other management systems (Fig. 6). The PB-straw retained had the smallest interactions with year. The CTB-straw incorporated had positive interactions in years (1999, 2000, 2006, 2007, 2008) with high solar radiation and reference evapotranspiration in the first five periods (Fig. 6). The PB-straw burned had positive interactions when relative humidity was high at emergence and head emergence (period 1 and 4) and negative interactions when precipitation was high during tillering and flowering (in period 2 and 5, Fig. 6). The FR model with a stepwise regression procedure for variable selection was used to determine the most informative subset of climatic co-variables to explain the S × E interaction. The selected subset of six co-variables explained 90.2% of the total S × E interaction (Table 3). Relative humidity at emergence (period 1) and head emergence (period 4) and minimum temperature and radiation at flowering (period 5) each explained over 16% of the S × E interaction. However, variables selected by FR should not be emphasized

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Factor 1 (28.74%) Fig. 7. Biplot of the first and second partial least squares (PLS) factors showing years, agronomic management systems and climatic co-variables for (a) grains per m2 and (b) thousand kernel weight in CIMMYT’s long-term trial, Yaqui Valley, Mexico. Tillage–straw systems (indicated in black squares): CTB, conventionally tilled raised beds; PB, permanent raised beds; B, straw burned; R, straw removed; P, straw partly retained; K, straw retained; climatic co-variables (indicated in grey): H, relative humidity, R, solar radiation; E, reference evapotranspiration; Tmn, minimum temperature; Tmx, maximum temperature; P, precipitation; periods of the growing season: 1, emergence; 2, tillering; 3, stem elongation and booting; 4, head emergence; 5, flowering and grain setting; 6, grain filling; growing season (indicated in black): indicated by last two digits of harvest year. The 6 climatic co-variables selected by factorial regression are indicated in bold.

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Table 3 Proportion of the variation in S × E interaction explained by the first six co-variables selected by factorial regression for grain yield, grains per m2 and thousand kernel weight in CIMMYT’s long-term trial, Yaqui Valley, Mexico. Variable Grain yield Minimum temperature during flowering (period 5) Radiation during flowering (period 5) Relative humidity during emergence (period 1) Relative humidity during head emergence (period 4) Maximum temperature during tillering (period 2) Ref. evapotranspiration during stem and head growth (period 3) % explained by the first 6 variables Grains m−2 Relative humidity during emergence (period 1) Minimum temperature during flowering (period 5) Radiation during tillering (period 2) Relative humidity during flowering (period 5) Ref. evapotranspiration during stem and head growth (period 3) Radiation during grainfilling (period 6) % explained by the first 6 variables Thousand kernel weight Minimum temperature during flowering (period 5) Relative humidity during flowering (period 5) Ref. evapotranspiration during emergence (period 1) Ref. evapotranspiration during stem and head growth (period 3) Precipitation during emergence (period 1) Minimum temperature during stem and head growth (period 3) % explained by the first 6 variables

to the exclusion of other variables represented in the PLS biplot. FR chooses the best subset of variables in a stepwise manner to maximize the variation explained by as few degrees of freedom as possible. Hence in a group of correlated variables (e.g. radiation and reference evapotranspiration in period 3, 4, 5 as evident from the PCA analysis in Fig. 2), those best related to each other over years, are likely to be represented by only one variable from the group, even though more than one may be significantly associated with the S × E interaction. When looking at the PLS biplot for yield, it is clear that the FR has selected the most representative co-variables for each of the clusters that can be distinguished. The radiation and evapotranspiration clusers on the right side of the biplot were represented by radiation in period 5 and evapotranspiration in period 3, respectively. The cluster of minimum temperature on the left side was represented in the FR model by minimum temperature in period 5. Relative humidity in period 1 and 4 on the top side of the biplot were both selected. For grains per m2 , the first PLS factor explained 21.1% of the S × E interaction and the second factor 21.3%. The first factor separated CTB-straw incorporated from PB-straw removed (Fig. 7a). The CTB-straw incorporated had positive interactions for grains per m2 in the same years as for yield, associated with high radiation and evapotranspiration. The PB-straw removed had positive interactions with year in 2003, 2005 and 2009, associated with high humidity and minimum temperature at flowering (period 5) and to a lesser extent with minimum temperature in period 1, 2, 3, and 6 and humidity in period 2, 3, 4, and 6 (Fig. 7a). The second PLS factor separated the PB-straw burned from the other tillage–straw systems. The PB-straw burned had high negative loadings due to a negative interaction with year in 2004 and positive interactions associated with high humidity at emergence (period 1, Fig. 7a). As for yield, the PB-straw retained had the smallest interactions with year. The subset of six co-variables selected by FR explained 90.5% of the total S × E interaction (Table 3). The evapotranspiration and minimum temperature clusters were represented by evapotranspiration in period 3 and minimum temperature in period 5, respectively. The radiation cluser on the right side of the biplot was represented by radiation in period 2 and again relative humidity in

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43.94 19.64 16.00 14.77 13.43 10.09

33.4 14.9 12.2 11.2 10.2 7.7 89.6

period 1 was the first selected co-variable, explaining 22.5% of the variation in S × E interaction. For thousand kernel weight, the first PLS factor explained 28.7% of the S × E interaction and the second factor 16.0%. The years with high radiation and evapotranspiration (1999, 2006, 2007, 2008) showed positive interactions with PB-straw removed and negative interactions with CTB-straw incorporated and PB-partly retained (Fig. 7b). The PB-straw burned was separated from the other tillage–straw systems by a high negative loading on the first and second PLS factor due to a positive interaction with year in 2004 and a negative interaction in 2009. The positive interaction in 2004 was associated with high precipitation during tillering, flowering and grain-filling (period 2, 5 and 6) and high maximum temperature during grain-filling (period 6, Fig. 7b). The negative interaction in 2009 was associated with high minimum and maximum temperatures during flowering and grain setting (period 5, Fig. 7b). The subset of six co-variables selected by FR explained 89.6% of the total S × E interaction (Table 3). Minimum temperature in period 5 explained the largest proportion of the variation (33.4%), followed by relative humidity in the same period (14.9%). The radiation and evapotranspiration clusters on the left side of the PLS biplot were represented by evapotranspiration in period 3. 4. Discussion 4.1. Tillage–straw system effect on grain yield and yield components Yield and yield components showed little cross-over interaction of tillage–straw system and year; i.e. changes in rank of tillage–straw systems in different years (Fig. 3). PB-straw retained had the highest yield and grains per m2 in most of the reported years and PB-straw burned the lowest. The PB-straw burned had the highest thousand kernel weight in most years. The low grain yield and grains per m2 in PB-straw burned coincides with the physical and chemical soil degradation found in this management practice compared to PB where the straw was not burned (Verhulst et al., 2011). Sayre et al. (2005) reported that 5 years after the initiation of

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the experiment, yield started to decrease in PB-straw burned compared to the other management systems resulting in a yield loss of 0.5–1.3 t ha−1 compared to the highest yielding treatments (PBstraw retained and PB-straw removed). The application of irrigation water appeared to compensate for the soil quality degradation associated with continuous residue burning in the first years (Sayre et al., 2005). In rainfed conditions where water availability is limiting crop production, the effect of management practice on yield might be expressed from the first years after adoption (Govaerts et al., 2005). The low number of grains per m2 in PB-straw burned was partly compensated by a higher kernel weight compared to the other management practices. Even when the effect of tillage–straw systems was established, the yield effect of tillage–straw system was relatively small with difference between minimum and maximum yield varying from 0.49 t ha−1 (in 2003) to 1.33 t ha−1 (in 2004 and 2007). This can be explained by the irrigation strategy which minimizes water stress and the favorable growing conditions for wheat in the area. Moreover, planting dates have been kept similar for all agronomic systems in this experiment, which has minimized the yield differences between PB and CTB. In the true field situation, it can be possible to plant the permanent beds 10–15 days earlier than the tilled beds, resulting in a larger yield advantage of PB where straw is not burned over CTB than in this study, as has been reported for other experiments at the same site (Sayre et al., 2005). Timely planting is important to avoid heat events during grainfilling which reduce kernel weight (Fig. 5). Also, the use of PB reduces costs compared to the use of CTB, resulting in a higher net return for farmers with PB where straw is not burned than for CTB (Sayre et al., 2005). 4.2. Relation between climatic variables and grain yield and yield components Days with temperatures too low to ensure normal biomass production mainly occurred during tillering and head differentiation (period 2). Temperature could affect spikelet initiation and thus sink development, explaining the positive correlations of yield with maximum temperature and evapotranspiration and of thousand kernel weight with evapotranspiration in that period (Figs. 4 and 5). Moreover, higher temperatures could accelerate the time to flowering assisting in avoiding the commonly observed high temperatures during grain-filling (Asseng et al., 2011). Association of low temperatures with high humidity might explain partly the negative correlation of humidity with yield and thousand kernel weight. Another factor could be an increased disease pressure associated with high humidity. Optimum temperature for grain-filling (period 6) lies between 19 and 22 ◦ C (Porter and Gawith, 1999) and is frequently exceeded in the last period of the growing season in the Yaqui Valley, reducing grain growth. This resulted in a negative correlation between thousand kernel weight and maximum temperature during grainfilling (Fig. 5). Wardlaw et al. (1989) and Calderini et al. (1999) also reported that high temperatures during grain-filling reduced kernel weight. Planting wheat in a timely way can help to avoid the rising temperatures at the end of the season. Conservation agriculture-based practices, in this case PB without residue burning, reduce land preparation time and increase flexibility compared to systems involving tillage. Erenstein and Laxmi (2008) report a 5–7% wheat yield increase, particularly due to more timely planting, for wheat planted with zero tillage after rice in the Indo-Gangetic Plains, where spiking temperature during grain-filling limit wheat productivity. No correlations between solar radiation and yield or yield components were found for any period of the growing season. Solar radiation integrated over the growing season was not correlated to yield or yield components either (not shown). From these results it

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seems that solar radiation is not limiting wheat performance in the Yaqui Valley. This coincides with results reported by Lobell et al. (2005) who did not find a wheat yield response to solar radiation during January–April, the main months of wheat growth, in the Yaqui Valley. 4.3. Tillage–straw system × year interaction explained by climatic co-variables For yield and grains per m2 , the same three groups of management systems were distinguished as in the soil quality analysis of this experiment: PB-straw burned, CTB-straw incorporated and PB where straw is not burned (Verhulst et al., 2011). For thousand kernel weight, only PB-straw burned was separated from the other management systems (Figs. 6 and 7). The PB-straw retained had the most stable yield and grains per m2 for the different climatic conditions over the 11 reported years. This indicates that management system could contribute to maintain wheat yield in a changing climate. In the central highlands of Mexico, zero tillage with residue retention also resulted in more stable wheat yields compared to practices involving tillage (Govaerts et al., 2005). The large negative interaction for yield and grains per m2 of PBstraw burned with year in 2004 indicated that PB-straw burned was relatively more affected by the adverse weather conditions (excess water) than the other treatments. This could be due to the degradation of physical soil quality measured in this management system (Verhulst et al., 2011), reducing the capacity of the soil to cope with high rainfall amounts. At the same time, PB-straw burned showed positive interactions in years with high relative humidity. The physical degradation might result in some water stress in the PB-straw burned in spite of the irrigations, which could be reduced by higher relative humidity. The negative effect on grains per m2 is partly compensated by a higher kernel weight, resulting in a positive interaction of PB-straw burned with year in 2004 for thousand kernel weight. In years with high maximum temperature during tillering (period 2), evapotranspiration and radiation, the CTB-straw incorporated had a positive interaction with year for yield. Maximum temperature and evapotranspiration during tillering had a positive correlation with yield (Fig. 4). It seems that favorable environmental conditions benefit the CTB-straw incorporated relatively more than other management systems. This might be explained by the higher stability of PB-straw retained or removed than CTBstraw incorporated. High yields and small differences between management systems in favorable conditions could lead to a positive interaction effect for management systems such as CTB-straw incorporated that are less stable but able to perform well in those conditions. The CTB-straw incorporated showed a negative interaction with year in years with high minimum temperatures, a condition which is predicted to increase due to climate change. 5. Conclusions The PB-straw retained and PB-straw removed had the highest yields and grains per m2 . The PB-straw burned had the lowest yield and grains per m2 , but the highest thousand kernel weight. Maximum temperature during tillering and head differentiation was positively correlated to final grain yield, but was negatively correlated to thousand kernel weight during grain-filling, indicating the importance of quick turnaround times between crops to allow for timely planting. For the tillage–straw system year interaction, three groups of management systems were distinguished for yield and grains per m2 : PB-straw burned, CTB-straw incorporated and PB where straw is not burned. The

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CTB-straw incorporated performed relatively better, i.e. had a positive interaction with year, in favorable years with high radiation and evapotranspiration. The PB-straw burned was relatively more affected by excess water conditions than the tillage–straw systems and showed positive interactions in years with high relative humidity. The PB-straw retained was the most stable in different climatic conditions, indicating that this management system could contribute to maintaining wheat yield in a changing climate. Acknowledgements N.V. received a PhD fellowship of the Research Foundation Flanders. We thank M. Ruiz Cano, J. Gutierrez Angulo, J. Sanchez ˜ C. Rascon and B. Martínez Ortiz for technical Lopez, A. Zermeno, assistance. We are grateful to the CENEB (Campo Experimental Norman E. Borlaug) and PIEAES (Patronato para la Investigación y Experimentación Agricola del Estado de Sonora) for the provision of the weather data. The research was funded by the International Maize and Wheat Improvement Center (CIMMYT, Int.) and forms part of the strategic research for ‘Desarrollo sustentable con el productor’, part of ‘Modernización Sustentable de la Agricultura Tradiacional’, supported by SAGARPA. References Aastveit, H., Martens, H., 1986. ANOVA interactions interpreted by partial least squares regression. Biometrics 42, 829–844. Aquino, P., 1998. The adoption of bed planting of wheat in the Yaqui Valley, Sonora, Mexico. Wheat Special Report 17a, CIMMYT, Mexico DF. Asseng, S., Foster, I., Turner, N.C., 2011. The impact of temperature variability on wheat yields. Glob. Change Biol. 17, 997–1012. Braun, H.J., Atlin, G., Payne, T., 2010. Multi-location testing as a tool to identify plant response to global climate change. In: Reynolds, M.P. (Ed.), Climate Change and Crop Production. CABI, Oxfordshire, UK, ISBN: 9781845936334. Calderini, D.F., Abeledo, L.G., Savin, R., Slafer, G.A., 1999. Final grain weight in wheat as affected by short periods of high temperature during pre- and post-anthesis under field conditions. Aust. J. Plant Physiol. 26, 453–458. ˜ J., Vargas, M., 2010. Statistical models for stud and underCrossa, J., Burgueno, standing genotype × environment interaction in an era of climate change and increased genetic information. In: Reynolds, M.P. (Ed.), Climate Change and Crop Production. CABI, Oxfordshire, UK, pp. 263–283, ISBN: 9781845936334. Denis, J-B., 1988. Two-way analysis using co-variates. Statistics 19, 123–132.

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