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Variability in regional wheat yields as a function of climate, soil and economic variables: Assessing the risk of confounding
Abstract
Mechanisms that explain spatial variability and trends in agricultural productivity at the regional scale are not well understood. Statistical approaches may be used to relate crop yields and trends in crop yields to changes in the economic and bio-physical environment. However, potential yield-explaining variables tend to confound at the regional scale due to strong correlations between these variables, which complicates the interpretation of such empirically derived relationships. In this paper, we assess relationships between different physical and economic variables and yields and trends in yields at the regional scale along a climatic gradient in Europe. We assess the extent of confounding (i.e. confusing the roles of different variables due to strong correlations) among these variables and the associated risk for explaining yield variability and trends in yields.
... Accordingly, the abandonment of arable land due to declining productivity is an agricultural land use change that may be a result of drought and soil erosion. Bakker et al. (2005) showed that some of the environmental impacts of drought (such as soil erosion) influence yield loss. Consequently, farmers decide to convert agricultural land in order to achieve higher yield production and income. ...
... Erosion reduces productivity on average by about 4% for each 10cm of soil lost (Vaezi & Bahrami, 2014). Significant yield losses related to soil erosion, for example in Greece, France, and Germany over a 30 year period, from 1970-2000 (Bakker et al., 2005), demonstrates the fact that there is a relationship between erosion-induced yield losses and the decline in agricultural area, which is closely linked to land use change. For example, in Lesotho, a general decline in the productive capacity of the soil and the eventual abandonment of arable land due to soil erosion has been observed. ...
... For example, in Lesotho, a general decline in the productive capacity of the soil and the eventual abandonment of arable land due to soil erosion has been observed. Soil erosion also decreases soil biodiversity while also making soils vulnerable to diseases (Bakker et al., 2005). In a survey carried out by Mbilinyi et al. (2013) in Tanzania, farmers indicated that some common crops, such as cassava and bananas, are not cultivated anymore as a result of drought. ...
This paper aims to review the impacts of drought on agricultural land conversion (ALC) on the one hand and the impacts of ALC on intensifying drought on the other. The paper further investigates coping strategies at three levels; i.e., micro (local), meso (national), and macro (international), in order to mitigate drought impacts that are classified as economic, social, and environmental. This paper shows that ALC, drought and coping strategies are in a reciprocal relationship and can have either a positive or negative influence on each other. The paper concludes that the complex and multidimensional nature of drought requires the development of an integrated approach that focuses on the governments’ collaboration with different stakeholders. Such an integrated approach can improve drought risk management implementations, decrease vulnerability and construct resilience and coping capacity at all levels in order to deal with droughts.
... Also, at a wider range of temporal scales, new factors may emerge that turn out to be important (e.g. technological development), which are not accounted for in CGSMs (7,31). That leaves us with the question how to best use data and models at regional scale. ...
... The best metamodel to emulate the winter wheat yield estimates (in t ha -1 yr -1 ) by the LINTUL2 for the period 1983-1992 was: Yield= -0.2 + TD -0.3 W.SRAD + 0.1 SRain/SET According to this model, yields decrease with increasing global solar radiation during winter, which can probably be ascribed to the fact that rising radiation might have caused additional water stress by affecting the soil water balance (7,52). Yields increase with increasing the ratio of rainfall and evapotranspiration during summer (from now on referred to as summer drought), which can probably be ascribed to a reduced risk on drought damage (29,35). ...
... Yields decrease with increasing maximum temperature during the whole growing season and winter radiation, which can probably be attributed to the fact that increasing temperatures enhance development rate and reduce the growing period (16,24), which often counteracts the positive effect of temperature on photosynthesis. Additionally, higher radiation and temperatures might have caused additional water stress by affecting the soil water balance, which in turn resulted in reduced yields (7,52). The empirical model did not contain a precipitation variable. ...
The need for more comparisons among models is widely recognized. This study aimed to compare three different modelling approaches for their capability to simulate and predict trends and patterns of winter wheat yield in Western Germany. The three modelling approaches included an empirical model, a process-based model (LINTUL2), and a metamodel derived from the process-based model. The models outcomes were aggregated to general climate zones level of Western Germany to allow for a comparison with agricultural census data for validation purposes. The spatial patterns and temporal trends of winter wheat yield seemed to be better represented by the empirical model (R2=
70%, RMSE= 0.48 t ha-1 yr-1, and CV-RMSE= 8%) than by the LINTUL2 model (R2=65%, RMSE= 0.67 t ha-1 yr-1, and CV-RMSE=11%) and the metamodel (R2= 57%, RMSE= 0.77 t ha-1 yr-1, and CV-RMSE=13%). All models demonstrated a similar order of magnitude of yield prediction and associated uncertainties. The suitability of the three models is context dependent. Empirical modelling is most suitable to analyze and project past and current crop-yield patterns, while crop growth simulation models are more suited for future projections with climate scenarios. The derived metamodels are fast reliable alternatives for areas with well calibrated crop growth simulation models. A model comparison helps to reveal shortcomings and strengths of the models. In our case,
a performance comparison between the three modelling approaches indicated that, for simulating winter wheat growth in Western Germany, higher sensitivity to soil depth and lower sensitivity to drought in the LINTUL2 model would probably lead to better predictions.
... In addition, these complex models have often been implemented for few environments and their outscale use for large scale monitoring is somewhat inefficient (Landau et al., 1998;Priya and Shibasaki, 2001). Crop yield is generally predicted using simulation models, empirical models (Bakker et al., 2005), or hybrid (simulation-empirical) models (Landau et al., 2000). In international literature, most of crop yield prediction studies are based on crop simulation models with more or less success. ...
... Empirical models, particularly those obtained by Ordinary Least Squares (OLS) methods, have the advantage of being simpler, easier to understand, locally more adapted and need relatively less data to be elaborated than crop simulation models. In twelve European countries, a set of simple variables of the climatic, pedogenetic and economic environment explained variations in regional wheat yields and trends in yields across Europe (Bakker et al., 2005). ...
... But whereas ΣNDVI and rainfall always had a positive impact on yield (with early rain in El Hoceima as sole exception), the influence of temperature, whenever significant, is always negative (see the sign of the regression coefficients in table 7). This agrees with the observations by Bakker et al. (2005) for many countries of Europe. High temperatures may increase evaporation rate, fasten the development rate and shorten the growing period, which in turn reduce the final yield. ...
... Other factors such as soil characteristics that are known to influence crop yields were not included in the analysis. However, recent studies suggest that soil characteristics explained only little of the spatial variability in wheat yields across Europe (Bakker et al., 2005) and significant effects on farmers' income were not observed in other regions (Liu et al., 2004). It can be assumed that farms are randomly distributed throughout each region, minimizing the influence of local conditions. ...
... Spatial variability of both crop yields and farmers' income across Europe was high and largely explained by a set of selected climatic and socio-economic including management factors. This is consistent with recent investigations in which more than 80% of the variability in regional wheat yields across Europe could be explained by climatic and socio-economic factors (Bakker et al., 2005). However, our results also indicate that spatial yield variability across Europe and the importance of factors explaining this variability differs among crops. ...
... Statistical analyses have reported climate change impacts on yields (Lobell and Asner, 2003;Chen et al., 2004;Tao et al., 2006), but climate effects can also be confounded by these other factors. The risk of confounding factors and relationships is larger at higher aggregation levels (Bakker et al., 2005). ...
Climate change is considered as one of the main environmental problems of the 21st century. Assessments of climate change impacts on European agriculture suggest that in northern Europe crop yields increase and possibilities for new crops and varieties emerge. In southern Europe, adverse effects are expected. Here, projected increases in water shortage reduce crop yields and the area for cropping, which directly affects the livelihood of Mediterranean farmers. However, the effect of adaptation is not well understood and therefore often highly simplified. Assessments mainly focus on potential impacts and not on the actual impacts. The main objective of this study is to assess how adaptation influences the impact of climate change and climate variability on European agriculture. The aim is to improve insights into adaptation processes in order to include adaptation as a process in assessment models that aim to develop quantitative scenarios of climate change impacts at regional level. We examined agricultural vulnerability and adaptation based on crop yields, farmers’ income and agricultural biodiversity; the main ecosystem services provided by agriculture. We considered that farm performance concerning these ecosystem services is influenced by two groups of factors related to (1) farm characteristics and (2) regional conditions, such as biophysical, socio-economic and policy factors. The availability of extensive datasets for Europe, at regional and farm level, provided a unique opportunity to analyse farm performance in relation to climate and management, and hence, improve insights in adaptation. Results demonstrate that farms that seem better adapted to prevailing conditions (i.e. higher crop yields and farmers’ income) do not adapt better to climate change and climate variability. Regions and farm types that obtain higher crop yields and farmers’ income have lower (relative) variability herein, but relationships between crop yield or income variability and climate variability are generally stronger than for regions or farm types with low crop yields and farmers’ income. Impacts of climate variability on crop yields and farmers’ income are generally more pronounced for temperate regions compared to Mediterranean regions. These results suggest that, due to a larger adaptive capacity, actual impacts of climate change and associated climate variability will be less severe for Mediterranean regions than projected by earlier studies. Farmers adapt their management to prevailing climatic, socio-economic and policy conditions. This current management influences adaptation strategies that can be adopted in the future and hence on the climate impacts. As actual impacts of climate change and climate variability on crop yields differ largely from potential impacts, which are based on simulations of potential and water limited crop yields, crop models need improvement to simulate actual crop yields. Although mechanistic modelling of all the processes determining crop yield and agricultural performance is not feasible, for reliable projections of the impacts of climate change on agriculture, models are needed that represent the actual situation and adaptation processes more accurately. Farmers continuously adapt to changes, which affects the current situation as well as future impacts. Therefore, adaptation should not be seen anymore as a last step in a vulnerability assessment, but as integrated part of the models used to simulate crop yields and other ecosystem services provided by agriculture.
... By 2080, August precipitation could be 50% higher than the present day (Fig. 5.1d), which could affect the quality and quantity of the yield. Other studies have noted that a limitation to investigations has been the inability to include monthly or seasonal variation in future predictions of species distribution or yield forecasts (Bakker et al., 2005). ...
... Only the physiological effects of climate on arabica coffee plants are considered; no modelling of future pest dynamics, availability of pollinators or the impact on soil as a result of a changed climate are included (Klein et al., 2003;Bakker at al., 2005). ...
... East Africa has a poorer network of weather stations than many areas of the world and actual weather data for use in GCM models is interpolated to provide information for each 0.5°× 0.5° location (New et al., 2000;Schlenker and Lobell, 2010). High resolution climatic data and soil water holding capacity is unavailable for the region studied, but has provided detail and increased accuracy in other studies, such as modelling wheat in Europe (Bakker et al., 2005;Stehfest, 2007). ...
... Despite regular controversy between the two methods Jamieson et al., 1999;Semenov and Shewry, 2011), both types of approaches have shown useful and convincing results. Common across both types of approaches are the difficulties in choosing relevant scale, as yields at aggregated scales depend on yields obtained across a wide variety of fields differing in soil properties, local weather, and crop management (Bakker et al., 2005;Challinor et al., 2003). Background tendencies for yield increases, or decreases, through improved management and cultivars, or reduced investments, respectively, also need to be accounted for, and this is mostly accomplished by detrending observed yields, although the choice of the trend model in and of itself is not straightforward (Supit, 1997; Lobell and Field, http://dx. ...
... It can be argued that in situations outside the range of model development conditions, using a crop simulation model might be more robust, comparatively to the statistical approach presented here, in which variable selection limits the number of mechanisms accounted for, and in which the linear, additive approach clearly can not represent non-linearities and interactions known to exist (Jamieson et al., 1999;Semenov and Shewry, 2011). However, both types of approaches show relatively equivalent results, due to the difficulties in parametrizing crop models, the way processes are represented in them (Passioura, 1996;Wallach et al., 2006), and the complexity of obtaining and aggregating input data at the proper scale (Bakker et al., 2005). In our case, the objective of ease of communication and utilization (i.e. ...
... At the highest level of aggregation, predictive ability is improved. This result is consistent with those presented by Supit and van der Goot (1999) and Bakker et al. (2005). Such a result can probably be attributed to compensation between errors at lower scales of aggregation. ...
... Crop residue is defined as the portion of plants (biological yield) left in the field after harvesting the grain [21]. These crop yields vary with time and space due to the spatial and temporal heterogeneity of environmental and management factors [1,22]. Variability of climate variables such as temperature and precipitation play an important role in determining the spatial and inter annual variability of crop yields [12,14]. ...
... Variability of climate variables such as temperature and precipitation play an important role in determining the spatial and inter annual variability of crop yields [12,14]. Climate variables are the most important variables determining the projections of crop response at regional scale models [1,2]. ...
... Furthermore, the selected factors (predictors) may fail to consistently represent the spatial variability of growing conditions in a particular location [15]. However, the inclusion of climate variables in regional scale models would enormously improve the accuracy of crop yield projections [1,2]. ...
In an effort to advance fuel security in this era of increasing fuel demand and climate change, crop residue can play an important role by serving as an alternative source of biofuel feedstock. Crop grain yield and residue production are tied to the changing climate over regional and global scale. Precipitation and temperature are among the prime climate variables that drive agricultural production across the globe. This study was carried out to understand the effect of temperature and precipitation on spatial distribution of crop residue yield potential at regional scale. Spatial autoregressive models were fitted for county level crop residue yield potential (as a major potential biomass feedstock) in the north central region of the United States using daily mean temperature and total precipitation during the crop growing season. The results of this observational study found the linear increasing trend in crop residue yield potential in most of the states across north central region of USA. Crop residue potential was also identified to have significant spatial variability. The influences of temperature and precipitation on crop residue yield potential exhibited significant interactions. Positive interaction effects were observed in states including Iowa, North Dakota, and Wisconsin. Negative interaction effects of daily mean temperature and total precipitation were observed in states including Illinois and Indiana. These results emphasize that the availability of crop residues for biofuels feedstocks will be sensitive to climatic variability and that these sensitivities will vary geographically.
... In addition, these complex models have often been implemented for few environments and their outscale use for large scale monitoring is somewhat inefficient (Landau et al., 1998; Priya and Shibasaki, 2001). Crop yield is generally predicted using simulation models, empirical models (Bakker et al., 2005), or hybrid (simulation-empirical) models (Landau et al., 2000). In international literature, most of crop yield prediction studies are based on crop simulation models with more or less success. ...
... It integrates the main tools developed by FAO and used in the EWS (Early Warning System), such as SUIVI 10 (agrometeorological database), FAOINDEX (Crop specific water balance), etc. Empirical models, particularly those obtained by Ordinary Least Squares (OLS) methods, have the advantage of being simpler, easier to understand, locally more adapted and need relatively less data to be elaborated than crop simulation models. In twelve European countries, a set of simple variables of the climatic, pedogenetic and economic environment explained variations in regional wheat yields and trends in yields across Europe (Bakker et al., 2005). Among these variables, climatic variables (rainfall, temperature, evapotranspiration and global radiation) together described the highest percentage of the observed variability in yields more than soil or economic variables using simple regression models. ...
... But whereas ΣNDVI and rainfall always had a positive impact on yield (with early rain in El Hoceima as sole exception), the influence of temperature, whenever significant, is always negative (see the sign of the regression coefficients intable 7). This agrees with the observations by Bakker et al. (2005) for many countries of Europe. High temperatures may increase evaporation rate, fasten the development rate and shorten the growing period, which in turn reduce the final yield.Table 9 shows the results of the side-analysis where ΣNDVI was regressed against the weather data. ...
Risk of drought for agriculture in Morocco is increasing due to dual pressure of decreasing and fluctuating precipitations and increasing domestic and industrial needs. This risk has to be considered and managed to insure food security. Early Warning Systems, and particularly agro-meteorological crop yield models, constitute early decision-making tools to warn of production drop due to drought. At continental to global scales, operational yield forecasting systems exist, which do provide timely estimates on the yields of the major crops in Morocco, but in a rudimentary way and only at national level. However, no specific system exists for Morocco and so far the official production estimates for the major crops are based on costly field surveys during May to September period and final results are published during the next crop season in July to October. The objective of this study is to prospect the feasibility of accurate early prediction models for wheat (Triticum aestivum L.) grain yields in Morocco, as it is by far the most consumed and cultivated crop. The challenge consists on elaborating prediction models to be used in an operational mode by decision-makers, based on reliable approaches and easily available agro-climatic indices or weather data.
The used approach to predict yields consists on using 4 different methodologies, depending on data availability: (1) Ordinary Least Squares (OLS) regression models, using only seasonal rainfall as predictor, (2) OLS regression models, using Seasonal rainfall, temperature and Normalized Difference Vegetation Index (NDVI) as predictors, (3) Artificial Neural Network (ANN) analysis, using Seasonal rainfall and NDVI as predictors and (4) AgroMetShell (AMS) water balance model, that uses dekadal rainfall and potential evapotranspiration as inputs. Seasonal rainfall, temperature and NDVI were used, as they display strong associated inter-annual variation with wheat yields in Morocco.
The first methodology is based on empirical OLS regression models to predict grain yields at province level, using only rainfall information. Grain yield was predicted at national level with 11.1% (119 kg.ha-1) error, when taking into account all the information at provincial level. The predicted error depends on province and can range from 67 kg.ha-1 to 595 kg.ha-1. Seemingly Unrelated Regressions (SUR) were used, as an original methodology in agro-meteorology, to improve these predictions taking into account spatial information for a set of neighbor provinces. SUR models improved yield predictions, as R2 between observed and predicted grain yields increased from R2=90.7%*** to R2=91.3%***. The models used are promising and could be applied for predicting wheat grain yields in Morocco if only rainfall information is available.
The second methodology keeps the same OLS modelling methodology to predict wheat yields but using seasonal NDVI and temperature information in addition to rainfall, still at both provincial and national levels. The predictions used dekadal (10-daily) NDVI/AVHRR, dekadal rainfall sums and average monthly air temperatures. The global land cover map GLC2000 was used to select only the NDVI pixels that are related to agricultural land. This second methodology is simpler and more accurate, comparatively to the first methodology, mainly thanks to NDVI information. At province and country levels most of the yield variation was accounted for by NDVI. Provincial wheat yields were assessed with errors varying from 80 to 762 kg.ha-1, depending on the province. At national level, wheat yield was predicted at the third dekad of April with 6.8% (73 kg.ha-1) error, using NDVI and rainfall. However, earlier forecasts are possible, starting from the second dekad of March with 84 kg.ha-1 error. The proposed models can be used in an operational context to forecast wheat yields in Morocco when NDVI is available in addition to rainfall.
The third methodology attempted to use ANN to predict yields using NDVI and rainfall information. ANN were used as they seem to have a great potential as they can theoretically deal with linear or even non linear relations for various levels of complexity, without any a priori assumption regarding the processes involved. This third methodology was compared to the second methodology, but only at country level. Multiple linear regression models performed better that ANN analysis for predicting wheat yields in Morocco. National wheat grain yields could be forecasted with 73 and 94 kg.ha-1 errors in validation at the second dekad of April, respectively using OLS models and ANN analysis. The lower performance on ANN analysis was probably due the linearity between yields and the predictors (NDVI and rainfall) and to the shortness of the used times series, in respect to the high year-to-year variation of NDVI, rainfall and yields.
The fourth methodology is based on AMS software, which derives agro-climatic indices, based on rainfall and evapotranspiration information. Three indices (Water Surplus Deficit, Water Requirement Satisfaction Index and Soil Water Storage), derived from AMS were correlated to wheat yields in Morocco. A fourth and new index was added to the evaluation, calculated as the integration of the Water Surplus Deficit index over dekads from the start of season in November. Two sample provinces, Meknès and Safi, located respectively in a sub-humid and a semi-arid agro-ecological zone were considered. Amongst the 4 indices, the Soil Water Storage (SWS) and the Water Requirement Satisfaction Index (WRSI) were the most correlated to wheat grain yield at the dekad level, respectively in Meknès (maximum R2=82% at 1st dekad of March) and Safi (maximum R2=67% at 1st dekad of April). AgroMetShell performance was also compared to the NDVI provided by NOAA-AVHRR. The four AgroMetShell indices were better correlated to yield than NDVI in Meknès, contrarily to Safi where NDVI was the best index. Regression models for predicting wheat yield at the province level were determined based on AMS and NDVI indices. Models combining AMS and NDVI explained 79% (330 kg.ha-1 error) and 92% (110 kg.ha-1 error) of wheat yield variability respectively in Meknès and Safi, even if most of grain yield variability was accounted for by NDVI in Safi. AMS seems to be an interesting tool in Morocco for wheat monitoring when no NDVI information is available in a sub-humid province such as Meknès.
At national level, potential improvements could stem from the inclusion of non-weather predictors (diseases, pests, soils, and irrigation) and water balance calculation in the proposed models. In addition, the predictions could be improved using better NDVI quality, derived from SPOT-vegetation for example, instead of NOAA/AVHRR. Our preliminary evaluation (not shown) of the SPOT-vegetation sensor, displayed better correlations between wheat yields and NDVI/SPOT in Morocco for the 1988-2004 time period. However, SPOT time series is actually too short for modelling use and, pooling these two datasets is restricted, as poor correlations were found between NDVIs derived from SPOT-vegetation and NOAA/AVHRR sensors. At provincial level, R2 widely ranged from 24 to 98.5% using only rainfall predictors and, from 64 to 98% using NDVI, rainfall and temperature predictors. The use of vegetation information could be more efficient at the provincial level, if higher spatial resolution NDVI images and land covers maps are used. In addition, provincial models should be based on more representative weather stations, as only one synoptic station per province was available in our study.
If adopted, the proposed models will certainly help policy-makers to warn populations for drought and plan well in advance annual imports, ultimately helping for food security of the country. These models are early, fast and low costly if compared to the actually used surveys-based methodology, needing only real time dekadal rainfall, temperature data and, NDVI images for nine dekads (February to April). The proposed approach is relatively easy to understand and not constraining, as it relies on well robust methodologies and could be adapted according to data availability. The proposed models could be applied by decision-makers to accurately predict wheat yields in an operational context, at both provincial and national levels in Morocco.
... Crop residue is defined as the portion of plant biological yield left in the field after harvesting the grain (Chintala et al., 2014). Crop yields vary with time and space due to the spatial and temporal heterogeneity of environmental and management factors (Bakker et al., 2005;Williams et al., 2008). One of the most important variables determining the projections of crop response at regional scale models is climate (Challinor, 2003;Bakker et al., 2005). ...
... Crop yields vary with time and space due to the spatial and temporal heterogeneity of environmental and management factors (Bakker et al., 2005;Williams et al., 2008). One of the most important variables determining the projections of crop response at regional scale models is climate (Challinor, 2003;Bakker et al., 2005). However, crop species, variety, soil conditions, fertilizer and other factors can also play a role. ...
Abstract. A significant reduction in greenhouse gas (GHG) emissions, as well as technologies
that ensure removal of CO 2 from the atmosphere, are necessary to achieve the set goals for the
transition to carbon neutrality. During the crop growth cycle, a significant amount of biomass is
produced, and carbon (C) and nitrogen (N) are captured both by the harvested crop removed from
the field and by residues left on the field. The trials were conducted to find out patterns between
crop and residues while trying to figure out the amount of captured C and N. In this study data of
the most widely grown cereal crops in Latvia are summarized. The data are representative,
obtained in different agroclimatic conditions, they vary both by species and variety, by year and
fertilizers applied. The mean amount of biomass from cereal crops left on the field was
1,070.9 g m -2 DM, besides, 906.7 g m -2 of that was made up of above-ground (AG) residues and
164.2 g m -2 of below-ground (BG) residues. On average, 471.8 g m -2 C and 14.3 g m -2 N were
captured, including: 411.2 g m -2 C and 12.9 g m -2 N by AG residues; 60.7 g m -2 C and 1.4 g m -2
N by BG residues. Regularities between grain yield and residues were found, however, they were
not very strong. The dataset should be enlarged to reduce uncertainty. As the data calculated from
crop have a greater uncertainty, the GHG inventory should be calculated according to the average
AG and BG biomass, which provide more accurate data.
Key words: cereal crops, crop residues, harvest index, shoot/root ratio.
... The approaches based on the combination of satellite imagery and climate data for yield spatial analysis do not allow taking full advantage of the spatial resolution offered by satellite products. Furthermore, this category of models cannot capture the yield variations in irrigated zones adversely to the proposed model based on phenological parameters, which is sensitive to the vegetation behaviour independently to the climate and production conditions (Bakker et al. 2005;Anagnostou et al. 2010). ...
... t ha À1 . The proposed model based on phenological parameters derived from NDVI time series allows overcoming missing weather, soil and yield data without losing precision and taking full advantage of the spatial resolution offered by satellite products (Bakker et al. 2005;Anagnostou et al. 2010). ...
Changes in crop yields may have important implications for food security in Morocco. This study intends to develop an explicit model based solely on phenological parameters derived from Moderate-Resolution Imaging Spectroradiometer (MODIS)/NDVI data to monitor wheat grain yield. The developed model allows overcoming missing weather, soil and irrigation supply data without losing the spatial resolution offered by images data. The study period covers a 16-year span between 2000 and 2016, and the considered region is the north-western of Morocco. The model showed a good correlation with ground measurements (R² = 0.62; p < 0.01). Spatio-temporal variability and trend of wheat yield were examined. The spatial analyses revealed an increase of instability of wheat grain yields across central and southern regions. Such a tool allows managers and policy makers to analyse the agricultural policy impact, to monitor the agronomic potential dynamic, to control the cropping season evolution and to optimize the land use choices.
... We take some examples from Western Europe, where detailed data are available. Bakker et al. (2005) found an R 2 of about 0.90 for the relationship between yield data (10 year average of regions in Europe) and soil, climate and economic variables. Variables in their study were all measured at a high aggregation level, not at farm level, and trends in yields over multiple years also poorly correlated with the explanatory variables (R 2 of 0.17 to 0.43). ...
... What could be done to better understand variability and improve the predictive power of future studies? First, many of the explanatory variables were confounded, which may lead to misidentification of the true explanatory factors (Bakker et al., 2005). For instance, varieties were confounded with location: varieties were targeted to ...
In this PhD research I identify options for sustainable intensification of African smallholder farming through legumes. In Nigeria, soybean yielded much more grain with the combined application of rhizobium inoculants and phosphorus fertilizer. As rhizobium inoculation was so cheap, the extra grain produced effectively paid for the more costly phosphorus fertilizer. In Uganda, together with farmers and other stakeholders we co-designed a ‘basket of options’ for climbing beans. Yield was only one of the criteria farmers used in their evaluations, so we included options for farmers with, for example, financial or labour constraints. Although we could draw some general conclusions on the suitability of technologies for different types of farmers, it proved difficult to predict the benefits and use of technologies from one year to the next. I therefore considered a ‘basket of options’ from which farmers can choose more useful than narrowly specified technologies for pre-defined farm types. Incorporation of farmers’ evaluations and their feedback on the testing of technologies in their own fields improve the relevance of technologies – a lesson that could be applied in other projects.
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... We take some examples from Western Europe, where detailed data are available. Bakker et al. (2005) found an R 2 of about 0.90 for the relationship between yield data (10 year average of regions in Europe) and soil, climate and economic variables. Variables in their study were all measured at a high aggregation level, not at farm level, and trends in yields over multiple years also poorly correlated with the explanatory variables (R 2 of 0.17 to 0.43). ...
... What could be done to better understand variability and improve the predictive power of future studies? First, many of the explanatory variables were confounded, which may lead to misidentification of the true explanatory factors (Bakker et al., 2005). For instance, varieties were confounded with location: varieties were targeted to ...
... Tahıl üretimi ve verim eğilimlerinin birincil etkenleri, başta iklim, toprak ve ekonomik faktörlerin çeşitliliği nedeniyle Avrupa Birliği'nde büyük farklılıklar görülmektedir. Bununla birlikte, bu değişkenliği belirleyen ilişkilerin anlaşılması sınırlıdır ve çevresel koşullardaki değişikliklere verilen verim tepkilerinin tahmin edilmesi hala sorunludur (Bakker et al., 2005). Bölgesel ölçekte ürün verimi değişkenliğini incelemek için yapılan araştırmalar bu yüzden çok fazla dikkat çekmemiştir. ...
... Buğday veriminin iklim, toprak ve diğer değişkenlere bağlı olarak değişimini inceleyen pek çok çalışma bulunmaktadır. Bunların bir kısmı sadece ekolojik değişkenlerin (yağış, sıcaklık, çevre vb.) incelerken, bir kısmı da verim üzerinde etkili olası tüm faktörleri dikkate alan çalışmalardır (Palosuo et al., 2011;Briston et al., 2010;You et al., 2009;Bakker et al., 2005;Leilah and Al-Khateeb, 2005;Sadras et al., 2002). ...
In this study, the variables affecting wheat yield in Hatay and Şanlıurfa provinces were examined. For both regions, 14 non-ecological explanatory variables considered effective in wheat yield were used in the model. In the model, the wheat yield in the unit area was dependent variable as low or high level. The data of 159 producers determined by sampling were analyzed by binary logistic regression, and the variables that effective in efficiency were determined. The results show that wheat yield in the two regions with ecological factors, input use level and cultivation period practices were determined to be effective. While the effect of irrigation and fertilizer amount on wheat yield in Hatay province was significant, irrigation, amount of fertilizer and increase of producer age were determined as important variables in Şanlıurfa province. Wheat production in order to increase the yield per unit area, it is important that farmers be trained in the use of inputs, time and methods of implementation. Also determined at regional level most appropriate input amount and method of applications, dissemination will be useful at the producer level.
... This is difficult to explain from a crop physiological perspective. Further, regression parameters derived when estimating CSI (equation (2) in Zampieri et al [1]) vary for each country and can also be expected to vary depending on the time period analysed. This makes it difficult to use these relationships for projections, for example in climate change impact assessments. ...
... This makes it difficult to use these relationships for projections, for example in climate change impact assessments. Using statistical relationships between weather variables and crop yield is appropriate to detect associations between variables but, due to the risk of confounding effects, these associations cannot explain causal relationships as variables contained in the statistical regression equations could be correlated with other variables not considered in the analysis, such as vapour pressure deficit [2]. The approximation of heat and water stress effects on crop yield by a single combined stress indicator is a strong simplification of reality. ...
... Evaluation of the heat stress effect around anthesis is a particular challenge due to its specific nature, whereby effects on grain yield can already be observed as a result of short episodes of high temperature (Bakker et al., 2005). In addition, our understanding of the processes and relationships involved in heat stress effects on crops are mainly obtained under controlled environment conditions, with little understanding about their relevance under field conditions. ...
... Based in this assumption they calculated that the proportion of oats will decline and that of maize will increase in the whole of Europe while the fraction of wheat will increase in northern Europe but decrease in southern Europe. A similar approach was used by (Ewert et al., 2005) to estimate possible changes in crop productivity. ...
The production of cereal crops is increasingly influenced by heat and drought stress. Despite the typical small-scale sub-regional variability of these stresses, impacts on yields are also of concern at larger regional to global scales. Crop growth models are the most widely used tools for simulating the effects of heat and drought stress on crop yield. However, the development and application of crop models to simulate heat and drought is still a challenging issue, particularly their application at larger spatial scales. Previous research showed that there is a lack of information regarding the:
1. Response of cereal crops to heat stress,
2. Interactions between phenology and heat stress under climate change,
3. Improvement of crop models for reproducing heat stress effects on crop yield,
4. Upscaling of heat and drought stress effects with crop models,
5. Effects of climate and management interactions on crop yield in semi-arid environments.
Five detailed studies were arranged to improve the understanding on the aforementioned gaps of knowledge:
1. A review study was set up to understand how crop growth processes responded to short episodes of high temperature. In addition, the possible ways for improvement of the heat stress simulation algorithms in crop models were investigated at a field scale. The reproductive phase of development in cereals was found to be the most sensitive phase to heat stress. Crop models aiming to model heat stress effects on crops under field conditions should consider the modelling of canopy temperature. This may also provide a mechanistic basis to link heat and drought stress in crop models. Generally, these two stresses occur simultaneously.
2. In a nationwide study, the interactions between the advancements of phenology and heat stress on winter wheat (Triticum aestivum L.) due to global warming, were evaluated between1951-2009 across Germany. The increase in temperature (~1.8°C) shifted crop phenology to cooler parts of the growing season (~14 days) and compensated for the effect of global warming on heat stress intensity in the period 1976-2009. The intensity of heat stress on winter wheat could have increased by up to 59% without any advancement in phenology.
3. A large-scale simulation study was conducted to investigate the effects of input (climate and soil) and output data aggregation on simulated heat and drought stress for winter wheat over the period of 1980-2011 across Germany. Aggregation levels were compared in several steps from 1 km × 1 km to 100 km × 100 km. Simulations were performed with SIMPLACE . Aggregation of weather and soil data showed a slight impact on the mean and median of simulated heat and drought stress at the national scale. No remarkable differences in simulated mean yields of winter wheat were evident for the different resolutions ranging from 1 km × 1 km to 100 km × 100 km across Germany. However, high resolution input data was essential to reproduce spatial variability of heat and drought stress for the more heterogeneous regions.
4. Two regional studies were arranged to evaluate the interactions between management and climate on crop production under climate change conditions. A crop model (DSSAT v4.5) was employed to assess the interactions between fertilization management of pearl millet (Pennisetum americanum L.), crop substitution [pearl millet instead of maize (Zea mays L)], and climate in semi-arid environments of Iran and the Republic of Niger, respectively. The pearl millet biomass production showed a strong response to different fertilization management in Niger. The highest dry matter production of pearl millet was obtained in combination with crop residues and mineral fertilizer treatment. The dry matter production of pearl millet was reduced by 11% to 62% under different climate change scenarios and future time periods (2011-2030 and 2080-2099). Results of this study showed that higher soil fertility could compensate for the negative effects of high temperature on biomass production. This was a result of the strong positive relationship between biomass production and the sum of precipitation under high soil fertility.
Crop substitution as an adaptation strategy (new hybrids of pearl millet instead of maize) enhanced fodder production and water use efficiency in present and potential future climatic conditions in northeast Iran. However, the fodder production of both crops was reduced due to shortening of the period from floral initiation to the end of leaf growth under various climate change conditions. Benefits of crop substitution may decline under climate change resulting in higher temperature sensitivity of the new hybrids of pearl millet.
Several conclusions were drawn from this study: It is necessary to consider canopy temperature instead of air temperature in crop models and use data from experiments under field conditions to improve and properly calibrate crop models for heat and drought stress responses. Crop models must also consider that effects of heat and drought stress on crops differ with phenological phases and can be compensated for by responses of other processes. An increase in the intensity of heat stress around anthesis can, for instance, be fully compensated for by the advancement in phenology in winter cereals under climate change. It is not necessary to use high resolution weather and soil input data for simulating the effects of heat and drought stress on crop yield at a national scale; but, high resolution input data are necessary to reproduce spatial patterns of heat and drought. Finally, implementation of management practices in cropping systems may change the response of crops to climate change. For this reason, management practices should be considered as an adaptation strategy.
... This may be attributed to a confounding effect because in very humid areas (the eastern edge of the Pampas), in which fine-textured soils predominate, high rainfall scenarios determine disease problems in wheat (Annone, 2001) and possible temporarily flooding conditions. Confounding effects between environmental variables were one of the main problems of yield modeling and special care must be taken when interpreting model predictions (Bakker et al., 2005). ...
... Modeling yield estimations at the regional scale results in improved fits because outliers are averaged (Bakker et al., 2005). We tested the generalization ability of our data aggregation method by developing an ANN model for yield estimation using county information instead of geographic unit data; we attained similar results to those reported here (n = 4440, R 2 = 0.608, RMSE = 331 kg ha -1 ). ...
The Pampas of Argentina is a vast fertile plain that covers approximately 60 Mha and is considered as one of the most suitable regions for grain production worldwide. Wheat production represents a main national agricultural activity in this region. Usually, regression techniques have been used in order to generate wheat yield models, at regional and subregional scales. In a whole regional analysis, using these techniques, climate and soil properties explained 64% of the spatial and interannual variability of wheat yield. Recently, an artificial neural network (ANN) approach was developed for wheat yield estimation in the region. In this chapter we compared the performance of multiple regression methods with the ANN approach as wheat yield estimation tools and propose developing productivity indexes by the latter technique. The ANN approach was able to generate a better explicative model than regression, with a lower RMSE. It could explain 76% of the interannual wheat yield variability with positive effects of harvest year, soil available water holding capacity, soil organic carbon, photothermal quotient and the ratio rainfall/crop potential evapotranspiration. Considering that the input variables required to run the ANN can be available 40-60 days before crop harvest, the model has a yield forecasting utility. The results of the ANN model can be used for estimating climate and soil productivity. A climate productivity index developed assessed the effect of the climate scenario and its changes on crop yield. A soil productivity index was also elaborated which represents the capacity to produce a certain amount of harvest grain per hectare, depending on soil characteristics. These indices are tools for characterizing climatic regions and for identifying productivity capabilities of soils at regional scale. The methodology developed can be applied in other cropping areas of the World and for different crops.
... Some unexpected results were observed in ne-textured soils, with some decreases in predicted productivity at high average R/PET ratios. is may be attributed to a confounding e ect because in very humid areas (the eastern edge of the Pampas), in which ne-textured soils predominate, high rainfall scenarios determine disease problems in wheat (Annone, 2001) and possible temporarily ooding conditions. Confounding e ects between environmental variables were one of the main problems of yield modeling and special care must be taken when interpreting model predictions (Bakker et al., 2005). ...
... Modeling yield estimations at the regional scale results in improved ts because outliers are averaged (Bakker et al., 2005). We tested the generalization ability of our data aggregation method by developing an ANN model for yield estimation using county information instead of geographic unit data; we attained similar results to those reported here (n = 4440, R 2 = 0.608, RMSE = 331 kg ha -1 ). ...
Soil productivity indices represent ratings of the potential plant biomass production of soils. Inductive approaches determine productivity based on inferred effects of soil properties on yield. Conversely, deductive approaches use yield information to estimate productivity. Our objective was to compare the performance of both types of productivity indices for assessing regional soil productivity for wheat (Triticum aestivum L.) yield in the Pampas. Soil data from soil surveys and interpolated climate information were utilized. Wheat yield data from a 40-yr period and representing similar to 45 Mha were used. Inductive productivity indices showed a low correlation with observed yield (R-2 < 0.45, P = 0.05). The best performance of deductive empirical methods was attained using a blind guess option, but soils could only be rated when yield data were available. Yield models based on the neural network approach had good performance (R-2 = 0.614, root mean square error [RMSE] = 548 kg ha(-1)) and was used for regional productivity index development. This index could be extrapolated to soils for which yield data are not available, and its validation with yield averages was optimal (R-2 = 0.728, P = 0.05). Regional high productivity was achieved for combinations of medium to high levels of soil organic C and soil available water storage capacity variables, which showed a positive interaction. This methodology for assessing soil productivity based on an empirical yield-based model may be applied in other regions of the world and for different crops.
... The investment cost for VR technologies depends largely on the type of technology. For instance, satellite images are freely available but require additional processing costs and purchasing N sensors or drones can be very costly (Bakker et al., 2005;Fabiani et al., 2020;Späti et al., 2021). The equipment of a tractor with a VR-compatible fertilizer spreader can be estimated at around €15,000. ...
CONTEXT
Reducing N surplus from agriculture without compromising yield and quality requires economically and ecologically viable solutions.
OBJECTIVE
Based on field data, we investigated a technical and market-based solution to balance the economic and environmental performance of nitrogen (N) fertilizer application in winter wheat in Switzerland.
METHODS
The technical solution, i.e. variable rate (VR) technology, was compared to the standard uniform fertilizer application (ST) in terms of revenues and N balance over seven site-years between 2018 and 2020. The potential of a market-based solution to align revenues and N surplus was investigated based on the relationship between two indicators: the economic optimum (EO) of the revenues and the balanced N supply (BNS). The EO was estimated using a production function approach. The BNS was empirically defined as the point at which the N surplus estimated from total N input (N fertilizer + soil N supply) reaches a limit value of 30 kg N ha⁻¹.
RESULTS AND CONCLUSIONS
On average, the revenues of VR were about 4% higher than in ST. The N surplus was, on average, 32% (21 kg N ha⁻¹) lower in VR compared with ST due to a 13% reduction in N inputs with no significant differences in yield. Despite the differences across years and fields, VR appeared to be reducing N surplus without losses in revenues in 5 out of 7 site-years. The revenue curve reached an EO at total N input of 205, 249 and 246 kg N ha⁻¹, in the years 2018, 2019, and 2020, respectively. The BNS was calculated at 220, 195, and 178 kg N ha⁻¹ N inputs for the years 2018, 2019, and 2020, respectively. The results show that a price increase of up to 5.4 times the current fertilizer price through taxes would be necessary in order to reduce the N surplus to an environmentally friendly level. Such an increase would hardly be politically feasible.
SIGNIFICANCE
The reported data showed that VR technology appears as a viable solution for producing lower N surplus at comparable revenue levels, thereby making it an option for small- to medium-scale winter wheat production in Switzerland. The environmental benefit could encourage the financial support of technologies for precise N management, which are often too expensive for these systems. Future research should verify or extend the numeric values found in this study.
... Crop yield signal that would otherwise be explained by subseasonal climate is subsumed by geography and time in the absence of those climate variables, a trend that gets amplified in periods of anomalous weather. This is a classic case of confounding which is a common problem in observational studies (Bakker et al. 2005;Ogundari and Onyeaghala 2021). ...
Statistical crop models, using observational data, are widely used to analyze and predict the impact of climate change on crop yields. But choices in model building can drastically influence the outcomes. Using India as a case study, we built multiple crop models (rice, wheat, and pearl millet) with different climate variables: from the simplest ones containing just space and time dummy variables, to those with seasonal mean temperature and total precipitation, to highly complex ones that accounted for within-season climate variability. We observe minimal improvement in overall model performance with increasing model complexity using standard accuracy metrics like the root mean square error and adjusted R², suggesting the simplest models, also the most parsimonious, are often the best. However, we find that simpler models, such as those including only seasonal climate variables, fail to fully capture impacts of climate change and extreme events as they can confound the influence of climate on crop yields with space and time. Automated model and variable selection based on parsimony principles can produce predictions that are not fit for purpose. Statistical models for estimating the impacts of climate change on crop yields should therefore be based on a conjunctive use of domain theory (for example plant physiology) with accuracy and performance metrics.
... More specifically, widespread agriculture and the clearance of natural vegetation cover dramatically increased erosion and sedimentation rates [3][4][5][6]. Whilst the impact of contemporary soil erosion on soil productivity can be assessed through experimental studies [7], and it has been demonstrated that soil erosion may be a driver of subrecent land use changes [8], it remains unclear to what extent soil erosion in the more distant past reduced ancient crop productivities to a level that it impacted society. To a large extent, this uncertainty can be related to the fact that a quantification of historic soil erosion remains limited to a few case studies [9]. ...
Most contemporary crop yield models focus on a small time window, operate on a plot location, or do not include the effects of the changing environment, which makes it difficult to use these models to assess the agricultural sustainability for past societies. In this study, adaptions were made to the agronomic AquaCrop model. This adapted model was ran to cover the last 4000 years to simulate the impact of climate and land cover changes, as well as soil dynamics, on the productivity of winter wheat crops for a Mediterranean mountain environment in SW Turkey. AquaCrop has been made spatially explicit, which allows hydrological interactions between different landscape positions, whilst computational time is kept limited by implementing parallelisation schemes on a supercomputer. The adapted model was calibrated and validated using crop and soil information sampled during the 2015 and 2016 harvest periods. Simulated crop yields for the last 4000 years show the strong control of precipitation, while changes in soil thickness following erosion, and to lesser extent re-infiltration of runoff along a slope catena also have a significant impact on crop yield. The latter is especially important in the valleys, where soil and water accumulate. The model results also show that water export to the central valley strongly increased (up to four times) following deforestation and the resulting soil erosion on the hillslopes, turning it into a marsh and rendering it unsuitable for crop cultivation.
... For example, in Reference [58], atmospheric pressure was used in wheat yield prediction in China. The influence of air temperature in the surface layer on wheat productivity was considered in References [20,59]. However, the experiments for the Khabarovsk District showed that these climatic indicators do not significantly affect soybean yield. ...
Crop yield modeling at the regional level is one of the most important methods to ensure the profitability of the agro-industrial economy and the solving of the food security problem. Due to a lack of information about crop distribution over large agricultural areas, as well as the crop separation problem (based on remote sensing data) caused by the similarity of phenological cycles, a question arises regarding the relevance of using data obtained from the arable land mask of the region to predict the yield of individual crops. This study aimed to develop a regression model for soybean crop yield monitoring in municipalities and was conducted in the Khabarovsk Territory, located in the Russian Far East. Moderate Resolution Imaging Spectroradiometer (MODIS) data, an arable land mask, the meteorological characteristics obtained using the VEGA-Science web service, and crop yield data for 2010–2019 were used. The structure of crop distribution in the Khabarovsk District was reproduced in experimental fields, and Normalized Difference Vegetation Index (NDVI) seasonal variation approximating functions were constructed (both for total district sown area and different crops). It was found that the approximating function graph for the experimental fields corresponds to a similar graph for arable land. The maximum NDVI forecast error on the 30th week in 2019 using the approximation parameters according to 2014–2018 did not exceed 0.5%. The root-mean-square error (RMSE) was 0.054. The maximum value of the NDVI, as well as the indicators characterizing the temperature regime, soil moisture, and photosynthetically active radiation in the region during the period from the 1st to the 30th calendar weeks of the year, were previously considered as parameters of the regression model for predicting soybean yield. As a result of the experiments, the NDVI and the duration of the growing season were included in the regression model as independent variables. According to 2010–2018, the mean absolute percentage error (MAPE) of the regression model was 6.2%, and the soybean yield prediction absolute percentage error (APE) for 2019 was 6.3%, while RMSE was 0.13 t/ha. This approach was evaluated with a leave-one-year-out cross-validation procedure. When the calculated maximum NDVI value was used in the regression equation for early forecasting, MAPE in the 28th–30th weeks was less than 10%.
... Grain production is usually expressed as a function of natural factors (climate, soil, etc.), human behaviour (physical inputs, technology, etc.), and economic development (Bakker et al., 2005;Chen and Li, 2013). This study adopts a Cobb-Douglas production function to represent this relationship. ...
... 气候模式输出与作物模型输入之间的尺度差异 是气候变化对农业影响评估不确定性的主要来源之 一 [46] . 通常, 气候变化对作物影响的模拟基于一定 的降尺度方法使 GCM 模式输出结果与作物模型相 匹配 [47] , 在站点尺度完成模拟再集成到区域影响评 估. 最近的研究表明, 基于站点尺度的模拟受土壤 质地、管理措施等的影响较为明显; 而在区域尺度 上, 气候要素的影响体现更显著 [48,49] . 模型升尺度 可以降低模型的复杂性且大大减少输入的参数 [50] . ...
... The graphics were elaborated using the software Sigmaplot v.11 and only the significant correlations (p<0.05) and those relevant for breeding purposes were shown. (Bakker et al., 2005). Therefore, multi-environment trials have significant importance and the breeder have to perform assessments combining crop locations and years in order to reduce the effect of single-environment atypical conditions. ...
The present study aimed to evaluate the associations between agronomic and bread-making quality traits in wheat under different growing environments. Nineteen Brazilian wheat cultivars were evaluated at six locations (Cascavel-PR, Castro-PR, Guarapuava-PR, Palotina-PR, Abelardo Luz-SC, and Não-Me-Toque-RS) during five years (2007 to 2011). The direction and magnitude of the associations between agronomic and bread-making quality traits were strongly dependent on the test location and year of evaluation. This study discusses the implications of choosing test locations on bread-making quality traits and for breeding purposes, with emphasis on indirect selection.
... Exploring the interplay of various climatic and other relevant factors by multiple linear regression, as suggested by Hlavinka et al. (2009) and Kolar et al. (2014), may help to better explain yield variability. However, such approaches entail a high risk of confounding effects (Asseng et al. 2011;Bakker et al. 2005) caused by multicollinearity, which should be avoided. ...
Climate change constitutes a major challenge for high productivity in wheat, the most widely grown crop in Germany. Extreme weather events including dry spells and heat waves, which negatively affect wheat yields, are expected to aggravate in the future. It is crucial to improve the understanding of the spatiotemporal development of such extreme weather events and the respective crop-climate relationships in Germany. Thus, the present study is a first attempt to evaluate the historic development of relevant drought and heat-related extreme weather events from 1901 to 2010 on county level (NUTS-3) in Germany. Three simple drought indices and two simple heat stress indices were used in the analysis. A continuous increase in dry spells over time was observed over the investigated periods from 1901–1930, 1931–1960, 1961–1990 to 2001–2010. Short and medium dry spells, i.e., precipitation-free periods longer than 5 and 8 days, respectively, increased more strongly compared to longer dry spells (longer than 11 days). The heat-related stress indices with maximum temperatures above 25 and 28 °C during critical wheat growth phases showed no significant increase over the first three periods but an especially sharp increase in the final 1991–2010 period with the increases being particularly pronounced in parts of Southwestern Germany. Trend analysis over the entire 110-year period using Mann-Kendall test revealed a significant positive trend for all investigated indices except for heat stress above 25 °C during flowering period. The analysis of county-level yield data from 1981 to 2010 revealed declining spatial yield variability and rather constant temporal yield variability over the three investigated (1981–1990, 1991–2000, and 2001–2010) decades. A clear spatial gradient manifested over time with variability in the West being much smaller than in the east of Germany. Correlating yield variability with the previously analyzed extreme weather indices revealed strong spatiotemporal fluctuations in explanatory power of the different indices over all German counties and the three time periods. Over the 30 years, yield deviations were increasingly well correlated with heat and drought-related indices, with the number of days with maximum temperature above 25 °C during anthesis showing a sharp increase in explanatory power over entire Germany in the final 2001–2010 period.
... The management of agricultural systems in European regions varies considerably in space and time due to differences in environmental conditions (e.g., pedo-climatic conditions), available technologies (e.g., crop varieties), agricultural policies (e.g. subsidies, environmental regulations), and market prices (Bakker et al., 2005;Dury et al., 2012;Pettorelli et al., 2005). Monitoring and understanding the diversity and dynamics of agricultural systems on a regional scale is crucial to support their evolution towards a more sustainable future (Zheng et al., 2012). ...
Crop simulation models are commonly used to forecast the performance of cropping systems under different hypotheses of change. Their use on a regional scale is generally constrained, however, by a lack of information on the spatial and temporal variability of environment-related input variables (e.g., soil) and agricultural practices (e.g., sowing dates) that influence crop yields. Satellite remote sensing data can shed light on such variability by providing timely information on crop dynamics and conditions over large areas. This paper proposes a method for analyzing time series of MODIS satellite data in order to estimate the inter-annual variability of winter wheat sowing dates. A rule-based method was developed to automatically identify a reliable sample of winter wheat field time series, and to infer the corresponding sowing dates. The method was designed for a case study in the Camargue region (France), where winter wheat is characterized by vernalization, as in other temperate regions. The detection criteria were chosen on the grounds of agronomic expertise and by analyzing high-confidence time-series vegetation index profiles for winter wheat. This automatic method identified the target crop on more than 56% (four-year average) of the cultivated areas, with low commission errors (11%). It also captured the seasonal variability in sowing dates with errors of ±8 and ±16days in 46% and 66% of cases, respectively. Extending the analysis to the years 2002–2012 showed that sowing in the Camargue was usually done on or around November 1st (±4days). Comparing inter-annual sowing date variability with the main local agro-climatic drivers showed that the type of preceding crop and the weather conditions during the summer season before the wheat sowing had a prominent role in influencing winter wheat sowing dates.
... Apart from the study by Ray et al (2015) and similar predecessors (see references therein) other causes of YV were also researched, but focusing on subsets of possible causes only. Bakker et al (2005) decipher the contribution of soil, climate and management as important sources of spatial wheat YV in Europe. Porter and Semenov (2005) or Asseng et al (2011) consider the impacts of heat stress on crop yields, but do not consider other climatic factors like water or solar radiation, or do not discuss plant physiological processes. ...
Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. A systematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields.
... Among the weather variables, the majority employs temperature and precipitation, where radiation and evapotranspiration are also found, though sparsely. Variables capturing soil moisture are rarely applied since this requires spatially detailed data usually not available (e.g., Bakker et al., 2005). More recent papers emphasize vapor pressure deficit (VPD) as an important yield-determining variable (Lobell et al., 2014;Roberts et al., 2013). ...
Increases in cereals’ production risk are commonly related to increases in weather risk. We analyze weather-induced changes in wheat yield volatility as a systemic weather risk in Germany. However, we disentangle the relative impacts of inputs and weather on regional yield volatility. For this purpose we augment a production function with phenologically aggregated weather variables. Increasing volatility can be traced back to weather changes only in some regions. On average, inputs explain 49% of the total actual wheat yield volatility, while weather explains 43%. Models with only weather variables deliver biased but reasonable approximations for climate impact research.
... The analysis relies on the integration of biophysical factors at regional scale for modeling attainable yield, average yield, and the yield gap between these production levels. As available data on crop production were generated at county scale from field surveys, and as in these datasets uncertainties were previously detected (Paruelo et al., 2004;Sadras et al., 2014), the information was aggregated up to groups of counties with similar area, to eliminate outliers and decrease variability (Bakker et al., 2005;Grassini et al., 2015). Conversely, climate and soil data were estimated using information from 960 climate interpolation maps and 1000 modeled soil profiles of seven soil variables accounting for their influence area. ...
As global grain demand is expected to keep on rising, productive but underachieving regions like the Argentine Pampas play a key role. Reduction of yield gaps in these regions would allow an increase in global food production. The objectives were to model the spatial patterns of the wheat ( Triticum aestivum L.) yield gap in the Pampas and relate it to environmental factors. The study comprised an area of approximately 45 Mha during a 40‐yr interval. Attainable yield was estimated by a stochastic frontier production function adjusted on statistical data generated at county scale. Yield gap was calculated for each combination of climate and soil variables as the difference between attainable yield and the average yield estimated using an artificial neural network (ANN) model. Yield gap was then modeled by another ANN using as inputs climate and soil factors. Average yield gap was 865 kg ha ⁻¹ (25% of average attainable yield), ranging from 740 kg ha ⁻¹ (26%) in humid environments to 1140 kg ha ⁻¹ (42%) under semiarid ones during the last 5 yr. Yield gap could be adequately modeled with an ANN ( R ² = 0.745, RMSE = 144 kg ha ⁻¹ ). The model showed that soil factors deeply impacted yield gap and minimum values were obtained in soils with medium to high organic C contents and available water storage capacity. Yield gap and a soil productivity index developed locally were negatively correlated. The methodology developed for yield gap analysis can be used for other crops and regions.
Core Ideas Yield gap calculation combined two modeled yield levels, applying a frontier production function and an artificial neural network.
Climate partially defined yield gaps; in semiarid environments these were largest.
Soil properties explained 50% of yield gap variability at regional scale.
Soil organic carbon and available water holding capacity interacted positively defining a minimum yield gap.
Yield gap reducing efforts should be focused in low productivity soils.
... Management driven stress factors, like the crop variety, fertilizer, plant protection, and machinery, are reflected in the mean yield level and the yield trend. However, there are also economic conditions, e.g., statutory set-aside quotas or renewable energy subsidies for biogas and biodiesel, which influence the annual yield variability (Krause, 2008;Bakker et al., 2005). We use the fertilizer price and the acreage of the respective crops as proxy variables to control the economic yield impacts in the models. ...
For agriculture in Germany and generally all around the world, yield variability due to uncertain climate conditions represents an increasing production risk. Regional assessments of future yield changes can diminish this risk. For Germany's two most important crops winter wheat (Triticum aestivum L.) and silage maize (Zea mays L.), we investigate three regression models estimating relative climate impacts on relative crop yield changes: the separate time series model (STSM), the panel data model (PDM) and the random coefficient model (RCM). These regression models use the Cobb-Douglas function to capture climatic and non-climatic impacts on yields (e.g., changing prices or inventory management). The yield influencing climatic impacts contain the potential growth and stress factors during vegetative and reproductive plant development. The models are estimated and validated at the county scale. To improve the robustness and goodness of fit, the models are aggregated at the scale of German federal states, river basins and at the national scale. The observed yield changes are satisfactorily reproduced by all models for all aggregated scales (measured by the Nash-Sutcliffe efficiency (NSE)). According to their NSE values, the methodically simple STSMs reproduce extreme yield changes better (0.85) than the RCMs (0.79) and PDMs (0.72) at the national scale. This order can be also found across all scales when considering the models' goodness of fit. Generally, spatial aggregation increases the goodness of fit by +0.16 for federal states and river basins and by +0.29 for entire Germany compared to the county scale. The mean NSE increase is lowest for STSMs (+0.11), followed by RCMs (+0.13) and PDMs (+0.25) for federal states and river basins, which is opposite to the goodness of fit order. The model parameters show clear spatial patterns, which reflect regional differences of climate and soil. Within its methodological limits, our approach can directly be combined with the output of climate models and is suitable for assessing short- and medium-term yield effects for the current agronomic practice. It requires neither bias correction of the climate variables nor explicit modeling of crop yield trends.
... Generally, the impacts of climate change on crop production at a regional level were assessed by simulating at the plot scale first, using downscaled climate models and crop models. However, the latest research has shown that significant factors of crop production at the plot level include soil properties and management measures, whereas it was the climate factor that significantly affect crop production at a regional level (Bakker et al. 2005;Challinor et al. 2009). Therefore, some uncertainties in the cultivars and management measures would exist when a crop model is applied from the plot scale to regional simulations. ...
Rice (Oryza) is a staple food in China, and rice yield is inherently sensitive to climate change. It is of great regional and global importance to understand how and to what degree climate change will impact rice yields and to determine the adaptation options effectiveness for mitigating possible adverse impacts or for taking advantage of beneficial changes. The objectives of this study are to assess the climate change impact, the carbon dioxide (CO2) fertilization effect, and the adaptation strategy effectiveness on rice yields during future periods (2011–2099) under the newly released Representative Concentration Pathway (RCP) 4.5 scenario in the Sichuan Basin, one of the most important rice production areas of China. For this purpose, the Crop Estimation through Resource and Environment Synthesis (CERES)-Rice model was applied to conduct simulation, based on high-quality meteorological, soil and agricultural experimental data. The modeling results indicated a continuing rice reduction in the future periods. Compared to that without incorporating of increased CO2 concentration, a CO2 fertilization effect could mitigate but still not totally offset the negative climate change impacts on rice yields. Three adaptive measures, including advancing planting dates, switching to current high temperature tolerant varieties, and breeding new varieties, could effectively offset the negative climate change impacts with various degrees. Our results will not only contribute to inform regional future agricultural adaptation decisions in the Sichuan Basin but also gain insight into the mechanism of regional rice yield response to global climate change and the effectiveness of widely practiced global thereby assisting with appropriate adaptive strategies.
... We take some examples from Western 607 Europe, where detailed data are available. Bakker et al. (2005) found 608 an R 2 of about 0.90 for the relationship between yield data (10 609 year average of regions in Europe) and soil, climate and economic 610 variables. Variables in their study were all measured at a high aggre-611 gation level, not at farm level, and trends in yields over multiple 612 years also poorly correlated with the explanatory variables (R 2 of 613 0.17-0.43). ...
... Management driven stress factors, like the crop variety, fertilizer, plant protection, and machinery, are reflected in the mean yield level and the yield trend. However, there are also economic conditions, e.g., statutory set-aside quotas or renewable energy subsidies for biogas and biodiesel, which influence the annual yield variability (Krause, 2008;Bakker et al., 2005). We use the fertilizer price and the acreage of the respective crops as proxy variables to control the economic yield impacts in the models. ...
... Management driven stress factors, like the crop variety, fertilizer, plant protection, and machinery, are reflected in the mean yield level and the yield trend. However, there are also economic conditions, e.g., statutory set-aside quotas or renewable energy subsidies for biogas and biodiesel, which influence the annual yield variability (Krause, 2008;Bakker et al., 2005). We use the fertilizer price and the acreage of the respective crops as proxy variables to control the economic yield impacts in the models. ...
Weather-related yield volatility is an important production risk for agriculture. Especially, negative yield anomalies could increase through climate change. We develop and investigate statistical crop yield models which can be used to predict crop yield impacts of weather and climate projections. The models are applied to winter wheat and silage maize, which are the most important annual crops as winter and spring crops, respectively, in Germany. The yields of both crops were modelled on county level, but evaluated on federal state, river basin or national level. We use three regression methods: separate time series model, panel data model, and random coefficient model. Within the Cobb-Douglas production function, relative changes (of yield and factor anomalies) are related to each other. To include the conditions of vegetative and generative plant development, we use climate variables summed to quarter- and half-year values. Furthermore, our models are controlled with proxy variables for economic impacts to estimate unbiased climatic parameters. Our study shows that the simple separate time series models explain (measured by the Nash-Sutcliffe model efficiency coefficient) yield anomalies best. They perform generally better (0.81) than the panel data models (0.72) due to a more accurate reproduction of exceptional yield changes at the county level. The random coefficient models performed between the separate time series models and panel data models (0.78). The aggregation of county yields to federal state and river basin yields improves the model accuracy by + 0.14. The aggregation effect is at highest for the panel data model on river basin scale (+0.26). The models for both crops achieve a similar goodness of fit. The spatial distribution of model parameters reflects the prevailing soil and climate characteristics within Germany relevant for the different plant development periods. Our statistical models capture collinear factors within yield formation. These are, for example, pests and diseases, or the adaptation behaviour of farmers on changing climatic or economic conditions. Due to the normalization, the yield changes are independent of technological levels and can be combined with weather and climate projection without any bias correction. The coarse temporal subdivision of the climatic variables supports robust assessments of climate change projections. To conclude, our models are suitable for the combination of yield assessments with weather and climate projections, because they reproduce yields from out-of-sample years robustly. In general, the separate time series models reproduce best the measured yield changes.
... On the other hand, this study highlights the knowledge gaps concerning interactions between climate and soil factors which might affect the biological limits of further yield increases. Another limitation of yield studies at a higher level of spatial aggregation (i.e. the European NUTS-2 level with 39 regions in Germany) emerges from a study examining the effects of driving forces on yields (Bakker et al., 2005). Here the authors found that significant correlations between wheat yields and the used economical and bio-physical variables increased further when using data on NUTS-2 level instead of NUTS-3 level. ...
In the future, Germany's land-use policies and the impacts of climate change on yields will affect the amount of biomass available for energy production. We used recent published data on biomass potentials in the federal states of Germany to assess the uncertainty caused by climate change effects in the potential supply of biomass available for energy production. In this study we selected three climate scenarios representing the maximum, mean and minimum temperature increase for Germany out of 21 CMIP5-projections driven by the Representative Concentration Pathways (RCP) 8.5 scenario. Each of the three selected projections was downscaled using the regional statistical climate model STARS. We analysed the yield changes of four biomass feedstock crops (forest, short-rotation coppices (SRC), cereal straw (winter wheat) and energy maize) for the period 2031–2060 in comparison to 1981–2010. The mean annual yield changes of energy wood from forest and short-rotation coppices were modelled using the process-based forest growth model 4C. The yield changes of winter wheat and energy maize from agricultural production were simulated with the statistical yield model IRMA. Germany's annual biomass potential of 1500 PJ varies between minus 5 % and plus 8 % depending on the climate scenario realisation. Assuming that 1500 PJ of biomass utilisation can be achieved, climate change effects of minus 75 (5 %) PJ or plus 120 (8 %) PJ do not impede overall bioenergy targets of 1287 PJ in 2020 and 1534 PJ in 2050. In five federal states the climate scenarios lead to decreasing yields of energy maize and winter wheat. Impacts of climate scenarios on forest yields are mainly positive and show both positive and negative effects on yields of SRC.
... Evaluation of the heat stress effect around anthesis is a particular challenge due to its specific nature, whereby effects on grain yield can already be observed as a result of short episodes of high temperature [19]. In addition, our understanding of the processes and relationships involved in heat stress effects on crops are mainly obtained under controlled environment conditions, with little understanding about their relevance under field conditions. ...
Higher temperatures during the growing season are likely to reduce crop yields with implications for crop production and food security. The negative impact of heat stress has also been predicted to increase even further for cereals such as wheat under climate change. Previous empirical modeling studies have focused on the magnitude and frequency of extreme events during the growth period but did not consider the effect of higher temperature on crop phenology. Based on an extensive set of climate and phenology observations for Germany and period 1951–2009, interpolated to 1 × 1 km resolution and provided as supplementary data to this article (available at stacks.iop.org/ERL/10/024012/mmedia), we demonstrate a strong relationship between the mean temperature in spring and the day of heading (DOH) of winter wheat. We show that the cooling effect due to the 14 days earlier DOH almost fully compensates for the adverse effect of global warming on frequency and magnitude of crop heat stress. Earlier heading caused by the warmer spring period can prevent exposure to extreme heat events around anthesis, which is the most sensitive growth stage to heat stress. Consequently, the intensity of heat stress around anthesis in winter crops cultivated in Germany may not increase under climate change even if the number and duration of extreme heat waves increase. However, this does not mean that global warning would not harm crop production because of other impacts, e.g. shortening of the grain filling period. Based on the trends for the last 34 years in Germany, heat stress (stress thermal time) around anthesis would be 59% higher in year 2009 if the effect of high temperatures on accelerating wheat phenology were ignored. We conclude that climate impact assessments need to consider both the effect of high temperature on grain set at anthesis but also on crop phenology.
... Models selected do not represent the entire diversity comprised by each modelling approach and that may hamper the generalization of the findings. The required model structure complexity is usually dependent on the temporal and spatial heterogeneity where the impact assessment is carried out (Heuvelink, 1998) for two main reasons; (1) data availability and (2) the level of aggregation required in the model (Bakker et al., 2005;Challinor et al., 2009). Value laden assumptions made by different groups of researchers can lead to value laden estimates of uncertainty of projections (Petersen, 2012). ...
... Comparing wheat yield variability in Switzerland (coefficients of variation of 0.13 and 0.15 for low-input and intensive production) with observations from other countries (see, e.g., Bakker et al., 2005;for Europe;Florin et al., 2009; for Australia), Swiss wheat production is characterised by rather low production risks. Thus, the importance of risk in farmers' decision making, also with respect to participation in low-input production schemes, may be much more pronounced in other countries. ...
The role of risks in the adoption of low-input wheat production in Switzerland is investigated using farm-level panel data. Due to governmental support with environmental payments, low-input wheat production is found to be on average more profitable. However, low-input production is also more risky, in particular due to higher yield variability. The here presented analysis reveals that these production risks could become a decisive factor in farmers' adoption decisions of risk-averse farmers if environmental payments were to be reduced or abolished.
... Th eir limitations include being invalid beyond the conditions under which they were developed, confounding eff ects (e.g. Bakker et al., 2005) and not illuminating underlying processes or interactions driving cropping systems' responses. Th ese defi cits can, however, be tackled through validating the models under diff erent conditions (H. ...
This book contains 17 chapters focusing on the impacts of climate change on ecosystems, food security, water resources and economic stability. Strategies to develop sustainable systems that minimize impact on climate and/or mitigate the effects of human activity on climate change are also presented.
... This reality that both heat stress and drought occur together, makes it difficult to assess the unique impact of heat stress on crop yields under field conditions and bares the risk of confounding effects [23]. Previous work, has used statistical methods to analyze the relationship between high temperature and crop yields for different crops and regions [15,20], though such methods are not able to identify nor explain the specific processes responsible for yield losses [16,25]. Process-based crop models are increasingly used to quantify the impact of heat and drought on crop yield but differ with respect to the stresses and processes considered [26][27][28]. ...
Increasing crop productivity while simultaneously reducing the environmental footprint of crop production is considered a major challenge for the coming decades. Even short episodes of heat stress can reduce crop yield considerably causing low resource use efficiency. Studies on the impact of heat stress on crop yields over larger regions generally rely on temperatures measured by standard weather stations at 2 m height. Canopy temperatures measured in this study in field plots of rye were up to 7 °C higher than air temperature measured at typical weather station height with the differences in temperatures controlled by soil moisture contents. Relationships between heat stress and grain number derived from controlled environment studies were only confirmed under field conditions when canopy temperature was used to calculate stress thermal time. By using hourly mean temperatures measured by 78 weather stations located across Germany for the period 1994–2009 it is estimated, that mean yield declines in wheat due to heat stress during flowering were 0.7% when temperatures are measured at 2 m height, but yield declines increase to 22% for temperatures measured at the ground. These results suggest that canopy temperature should be simulated or estimated to reduce uncertainty in assessing heat stress impacts on crop yield.
... Agricultural production and productivity diverge to a great degree within the European Union, due to variability in weather, land and various economic factors. Bakker et al. (2005) demonstrated that spatial variability in wheat productivity is high in Europe. ...
Soil is a multifunctional, non-renewable natural resource for Europe as clearly expressed in the European Union (EU) Thematic Strategy for Soil Protection (COM (2006)231). Soil carries out multiple functions, including the support of food production. Urban development and its associated land take poses a major threat to soil and could have significant effects on agricultural production. This paper aims to evaluate the potential productivity losses in European agriculture due to land-take processes between 1990 and 2006. Agricultural land take was calculated using CORINE Land Cover maps of 1990, 2000 and 2006. For 21 of the 27 EU member states, agricultural land take was computed to be 752,973 ha for 1990–2000 and
436,095 ha for 2000–2006, representing 70.8% and 53.5%, respectively, of the total EU land take for these periods. The impact of this land take on the production capabilities of the agricultural sector for the period 1990–2006 for 19 of the 21 states was estimated to be equivalent to a loss of more than six million tonnes of wheat. The paper demonstrates that Europe’s intense urbanisation has a direct impact on its
capability to produce food.
This paper evaluates the potential value of a weather index insurance for the agriculture sector in an high income country (Germany). In our theoretical analysis we model an index insurance, a loss-based insurance market as well as a combination of both kinds of insurance and compare the resulting expected utility of a risk averse crop farmer. To find a suitable index, we conduct a panel estimation and evaluate the link between different weather variables and losses of crop farmers in Germany. Following our estimation, mean temperatures in summer have the highest potential for an valuable index insurance. Finally, we simulate the theoretical model using the results from the estimation and using different thresholds for the definition of a NatCat. According to this simulation, index-insurance is more attractive for the lower and more frequently occurring losses and loss-based insurance is more attractive for rare high losses. A combination of both kinds of insurance could be optimal for intermediate cases.
Evaluation of physical and quantitative data of soil erosion is crucial to the sustainable development of the environment. The extreme form of land degradation through different forms of erosion is one of the major problems in the sub-tropical monsoon-dominated region. In India, tackling soil erosion is one of the major geo-environmental issues for its environment. Thus, identifying soil erosion risk zones and taking preventative actions are vital for crop production management. Soil erosion is induced by climate change, topographic conditions, soil texture, agricultural systems, and land management. In this research, the soil erosion risk zones of Ratlam District was determined by employing the Geographic Information System (GIS), Revised Universal Soil Loss Equation (RUSLE), Analytic Hierarchy Process (AHP), and machine learning algorithms (Random Forest and Reduced Error Pruning (REP) tree). RUSLE measured the rainfall eosivity (R), soil erodibility (K), length of slope and steepness (LS), Land cover and management (C), and support practices (P) factors. Kappa statistic was used to configure model reliability and it was found that Random Forest and AHP have higher reliability than other models. About 14.73% (715.94 km²) of the study area has very low risk to soil erosion, with an average soil erosion rate of 0.00–7.00 × 10³ kg/(hm²·a), while about 7.46% (362.52 km²) of the study area has very high risk to soil erosion, with an average soil erosion rate of 30.00 × 10³–48.00 × 10³ kg/(hm²·a). Slope, elevation, stream density, Stream Power Index (SPI), rainfall, and land use and land cover (LULC) all affect soil erosion. The current study could help the government and non-government agencies to employ developmental projects and policies accordingly. However, the outcomes of the present research also could be used to prevent, monitor, and control soil erosion in the study area by employing restoration measures.
Küresel İklim Değişikliği ve Buğday
Durum wheat is an important crop in semi-arid Mediterranean regions as Andalusia, an autonomous community in the southern part of Spain. Accurate early predictions of durum wheat yield can provide precious information for within-season adjustment of crop managing as well as for economical and political stakeholders. In this study, an alternative methodology to mechanistic crop models is proposed for within-season early prediction of durum wheat yield in Spain based on estimates for its larger producer community, Andalusia. The proposed Radial Basis Functions (RBF) interpolation models are based on the sown area and a large number of climatic variables. Global warming and increasing occurrence of extreme weather events are only two of the factors that make crop yield forecast extremely difficult as they can lead to an increased interannual yield variability. Nevertheless, the RBF models proposed presented good quality yield predictions clearly outperforming multivariate linear models used as benchmark. Moreover, RBF models’ predictions made four months prior to harvest are able to capture the trend of the yield series as well as near-harvest predictions.
Sich ändernde Klima- und Wetterbedingungen in Verbindung mit einer begrenzt ausdehnbaren Ackerfläche werden den Druck auf Nahrungsmittelproduktionssysteme weiter erhöhen. Um dieser Herausforderung gerecht zu werden, ist eine Erhöhung und Stabilisierung der Ernteerträge unverzichtbar. Dies erfordert aber ein tieferes Verständnis der Einflussfaktoren, die auf die Ertragsvariabilität wirken. Diese Dissertation leistet einen Forschungsbeitrag zu Ertragsmodellen in Deutschland, Tansania und auf globaler Ebene. Dazu analysiere und kombiniere ich statistische und prozessbasierte Ertragsmodelle in fünf Schritten: (i) Zunächst entwickele ich einen statistischen Modellansatz, um den Einfluss von Wetter und agronomischem Management auf Winterweizenerträge in Deutschland zu separieren. (ii) Auf der Grundlage dieses Modells erweitere ich die statistischen Methoden und wende sie für Winterweizen und Silomais auf regionale Ebene an. (iii) Diesen erweiterten Modellansatz verwende ich daraufhin zum Testen einer Kreuz-Validierung um zukünftige Ertragsänderungen unter Klimawandel zu projizieren. (iv) Anschließend wird in einer globalen statistischen Anwendung dieses Modell für kurzfristige Ertragsprognosen getestet. (v) Schließlich kombiniere ich für das Fallbeispiel Mais in Tansania statistische und prozessbasierte Ertragsmodelle, um wetterbedingte Ertragsverluste von nicht-wetterbedingten Ertragsverlusten zu separieren. Als Ergebnis lässt sich zusammenfassen, dass der Anteil der wetterbedingten Ertragsvariabilität in Deutschland höher ist als in Tansania. Dementsprechend sind die Ertragsschwankungen in Tansania eher auf das agronomische Management und sozioökonomische Einflüsse zurückzuführen. Für beide Länder stelle ich fest, dass der Anteil der wetterbedingte Ertragsvariabilität auf aggregierter Ebene höher ist als auf regionaler Ebene. Der kombinierte statistisch-prozessbasierte Ansatz zur Bewertung von wetterbedingten Ertragsverlusten kann für Versicherungszwecke genutzt werden.
Water resource management in a basin depends upon the hydrological response of upstream basin area. Upstream basin area may produce different amounts of run-off for a given rainfall based on its hydrologic response. Present communications show the importance of geomorphologic characteristics in understanding the hydrologic response of a basin. This study is carried out through Geomorphologic Instantaneous Unit Hydrograph (GIUH) analysis, wherein Horton's morphometric ratios were used to define the drainage network in comparison with Snyder, SCS and Triangle unit hydrographs for determination of shape and dimensions of the outlet runoff hydrograph in the Varband river basin located in Fars province in Iran. Comparison of calculated and observed hydrographs showed that GIUH had the most direct agreement in two parameters of peak time and peak flow of direct runoff. Also, GIUH indicated the least amount of main relative and square error. Results also showed the efficiency of GIUH ratio for Snyder, SCS and Triangle hydrographs in the basin are 91.06, 99.11 and 88.64, respectively. The study shows the length ratio (RL) significantly influences the hydrologic response of the river basin. Hence, computation of this parameter should be included in the flood analysis of any rivers.
A 2 year field experiment was conducted in northern Greece to study the biomass effects of four oregano (Origanum vulgare) biotypes, used as incorporated green manure, on the emergence and growth of barnyard grass (Echinochloa crus-galli), bristly foxtail (Setaria verticillata), common purslane (Portulaca oleracea), cotton (Gossypium hirsutum), and corn (Zea mays). The oregano biotypes were selected on the basis of their high phenolic content. The phytotoxic potential of the oregano biotype extracts also was determined in the laboratory by using a perlite-based bioassay with cotton, corn, and barnyard grass. The bioassays indicated that the germination, root elongation, and fresh weight of cotton, corn, and barnyard grass were reduced by the oregano biotype extracts. In the field, the emergence of common purslane, barnyard grass, and bristly foxtail was reduced by 0–55%, 38–52%, and 43–86%, respectively, in the oregano green manure treatments, as compared with the oregano green manure-free treatments (the controls).At harvest, the cotton lint and corn grain yields in the oregano green manure treatments were 24–88% and 5–16%, respectively, greater than those in the corresponding green manure-free, weedy treatments.These results indicated that when the biomass of the oregano biotypes with a high phenolic content were incorporated into the soil as green manure, they could be used to suppress barnyard grass, bristly foxtail, and common purslane in cotton and corn and consequently to minimize herbicide usage.
We present a study on the impact of soil and climatic variability on the yield of winter wheat in the Herault-Libron-Orb Valley in southern France. The study was based on the use of a crop simulation model (Euro-ACCESS), run at 63 individual sites throughout the study area, for the current climate (1976 to 1984) and for potential future changes in temperature and precipitation (2047 to 2054). Three climate scenarios were selected to represent low, mid and high changes, although significant winter wheat yield decreases were only observed for the climate scenario with the largest change. In general, the influence of climate change on yields was small (less than 0.1 t ha(-1) over the whole simulation period), but strong inter-annual variation was found, which is typical of the Mediterranean climate. Soil variability within the study region was the most important source of spatial variability for the simulated yields, and the soil available water capacity was identified as a good indicator of yield change for large climatic change. Soil variability was important in this study because of the small size of the study region and because of the strong influence of water limitation on crop growth in Mediterranean areas. Statistical relationships were established between crop yields, yield changes and the soil available water capacity. These relationships were used to extrapolate the crop simulation results from individual sites to the whole region using data from soil maps at a scale of 1:250 000. This modelling exercise demonstrated the importance of explicit consideration of soil as well as climatic variability in crop-climate impact studies.
1962 and 1988, yield increase was also associated with higher flag- leaf photosynthetic rate and related traits, but not higher biomass. Trends in Yield Potential 1966 to Present, Nevertheless, significantly higher biomass has been reported in more and Future Demand recent CIMMYT lines. Improved HI is associated with higher N use efficiency (yield per unit of available N) and improved yield of semi- Though comprehensive studies quantifying genetic dwarf lines is expressed at high and low levels of N input. Where gains in yield potential under irrigated conditions have interplant competition for light and soil factors are manipulated, yield been conducted in relatively few locations, these studies improvement is associated with adaptation to high plant density. Stud- suggest steady rates of gains of a little under 1% per ies have confirmed that the juvenile spike growth phase is critical in year since the mid 1960s, for example in India (Kulshres- determining both grain number and kernel weight (sink) potential. tha and Jain, 1982) and Mexico (Waddington et al., Improving assimilate availability during this stage, perhaps by length- 1986, 1987; Sayre et al., 1997). Similar results have been ening its relative duration, may be one way to improve yield potential. shown in studies employing irrigation in high-yielding Traits that could potentially be exploited for improving assimilate
This paper proposes a methodology for using soil and climatic data to assess the risk of drought impacts in Europe. In the case of agricultural drought, it is the soil water available to plants (SWAP) that is the most important soil factor in assessing this risk and a simple model for estimating this is described. This model can be linked to spatial and point data from the European Soil Database and a preliminary map of SWAP in Europe has been produced using a pedotransfer rule. The study concludes that more precise modelling of droughtiness, based on interactions of soil available water with the average soil moisture deficit, estimated from meteorological data, is needed, to support policy making today but that currently the necessary soil physical data are lacking to fully implement this approach.
A crop-growth-simulation model based on SUCROS87 was used to study effects of temperature rise and increase of atmospheric CO2 concentration on wheat yields in several regions in Europe. The model simulated potential and water-limited crop production (growth with ample supply of nutrients and in the absence of damage by pests, diseases and weeds). Historic daily weather data from 13 sites in Western Europe were used as starting point.
For potential production (optimal water) a 3 °C temperature rise led to a yield decline due to a shortening of the growing period on all locations. Doubling of the CO2 concentration caused an increase in yield of 40% due to higher assimilation rates. It was found that effects of higher temperature and higher CO2 concentration were nearly additive and the combination of both led to a yield increase of 1–2 ton ha-1. A very small CO2-temperature interaction was found: the effect of doubled CO2 concentration on crop yield was larger at higher temperatures. The inter-annual yield variability was hardly affected.
When water was limiting crop-production effects of temperature rise and higher CO2 levels were different than for the potential production. Rise in temperature led to a smaller yield reduction, doubled CO2 concentration to a larger yield increase and combination of both led to a large yield increase (3 ton ha-1) in comparison with yields simulated for the present situation. Both rise in temperature and increase in the CO2 concentration reduced water requirements of the crop. Water shortages became smaller, leading to a reduction in inter-annual variability. It is concluded that when no major changes in precipitation pattern occur a climate change will not affect wheat yields since negative effects of higher temperatures are compensated by positive effects of CO2 enrichment.
stics is a model that has been developed at INRA (France) since 1996. It simulates crop growth as well as soil water and nitrogen balances driven by daily climatic data. It calculates both agricultural variables (yield, input consumption) and environmental variables (water and nitrogen losses). From a conceptual point of view, stics relies essentially on well-known relationships or on simplifications of existing models. One of the key elements of stics is its adaptability to various crops. This is achieved by the use of generic parameters relevant for most crops and on options in the model formalisations concerning both physiology and management, that have to be chosen for each crop. All the users of the model form a group that participates in making the model and the software evolve, because stics is not a fixed model but rather an interactive modelling platform. This article presents version 5.0 by giving details on the model formalisations concerning shoot ecophysiology, soil functioning in interaction with roots, and relationships between crop management and the soil–crop system. The data required to run the model relate to climate, soil (water and nitrogen initial profiles and permanent soil features) and crop management. The species and varietal parameters are provided by the specialists of each species. The data required to validate the model relate to the agronomic or environmental outputs at the end of the cropping season. Some examples of validation and application are given, demonstrating the generality of the stics model and its ability to adapt to a wide range of agro-environmental issues. Finally, the conceptual limits of the model are discussed.
Sirius is a wheat simulation model that calculates biomass production from intercepted photosynthetically active radiation (PAR) and grain growth from simple partitioning rules. Leaf area index (LAI) is developed from a thermal time sub-model. Phenological development is calculated from the mainstem leaf appearance rate and final leaf number, with the latter determined by responses to daylength and vernalisation. Effects of water and N deficits are calculated through their influences on LAI development and radiation-use efficiency. This paper describes the model and its validation using data from independent and near independent experiments at Lincoln, New Zealand, and Rothamsted, UK. Despite there being no calculation of tiller dynamics or grain number, the model accurately simulated the behaviour of crops exposed to a wide range of conditions. We conclude that the accurate prediction of phenological development and LAI is much more important for grain yield prediction than are the components of yield. Although grain population is not a necessary step in yield calculation in Sirius, the model proved useful in investigating the effects of stress in setting grain number. The analysis showed that the influence of stress on partitioning of biomass to the ear during pre-anthesis ear growth was much more important in determining grain number than was the effect on biomass accumulation during the same phase.
Since the pioneering work of C.T. de Wit in the 1960s, the Wageningen group has built a tradition in developing and applying crop models. Rather than focusing on a few models, diversity is its trademark. Here we present an overview of the Wageningen crop and crop-soil modelling approaches along three criteria. The first criterion relates to the production situations the models are dealing with (i.e. potential, water and/or nutrient-limited, and actual production situations including pests, diseases and weeds). Second, models differ as a result of the objectives of model development, and hence required scale and degree of detail and comprehensiveness. Third, models have at least three potential application domains, i.e. research, education and support of learning and decision making processes.
The decision support system for agrotechnology transfer (DSSAT) has been in use for the last 15 years by researchers worldwide. This package incorporates models of 16 different crops with software that facilitates the evaluation and application of the crop models for different purposes. Over the last few years, it has become increasingly difficult to maintain the DSSAT crop models, partly due to fact that there were different sets of computer code for different crops with little attention to software design at the level of crop models themselves. Thus, the DSSAT crop models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation and maintenance. The basis for the new DSSAT cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. It has one Soil module, a Crop Template module which can simulate different crops by defining species input files, an interface to add individual crop models if they have the same design and interface, a Weather module, and a module for dealing with competition for light and water among the soil, plants, and atmosphere. It is also designed for incorporation into various application packages, ranging from those that help researchers adapt and test the CSM to those that operate the DSSAT–CSM to simulate production over time and space for different purposes. In this paper, we describe this new DSSAT–CSM design as well as approaches used to model the primary scientific components (soil, crop, weather, and management). In addition, the paper describes data requirements and methods used for model evaluation. We provide an overview of the hundreds of published studies in which the DSSAT crop models have been used for various applications. The benefits of the new, re-designed DSSAT–CSM will provide considerable opportunities to its developers and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems at field, farm, and higher levels.
Major increases in crop yields will be required to meet the future demand for food worldwide, yet changes in climate and diminishing returns from technological advances may limit the ability of many regions to achieve the necessary gains (1, 2). Many researchers have predicted the effect of future climate changes on crop production using a combination of field studies and models (3), but there has been little evidence relating decadal-scale climate change to large-scale crop production. Here, we show that recent trends in temperature have increased the productivity of the two major U.S. crops and that accounting for climate significantly reduces the perceived gains due to management and other factors.
An analysis of water shortages for municipal and industrial, and (project type) irrigation uses is presented. The investigation was undertaken to compile data on water shortages for these uses and to establish general practical guides for use in planning studies by governmental agencies and consulting firms. It is concluded that shortages of any magnitude should not be permitted in studies in which water is to be supplied for municipal and industrial purposes. In regard to irrigation, no firm guides and limitations based entirely on water shortages are recommended. In determining the feasibility of irrigation, economic analyses will be made, on a project by project basis, that will consider reduced crop yields due to water shortages. Desirable limits on water shortages for irrigation in planning work are considered to be as follows: Maximum average annual for 50-yr period, 3% maximum for a single year, 50% maximum for two consecutive years (total), 70% and maximum shortage years, 27%.
Winter wheat (Triticum aestivum L., cv. Hereward) was grown in the field within four double-walled polyethylene-covered tunnels along which near-linear temperature gradients were imposed at normal atmospheric or at an elevated CO2 concentration (c. 700 μmol mol−1 CO2) in 1991/92 and in a further experiment in 1992/93. Development was more rapid the warmer the temperature. In 1991/92 an increase in mean seasonal temperature of 3·5 °C reduced the duration from sowing to harvest maturity (the stage when grain moisture content reduced naturally to 15–18%) by c. 38 days, and reduced the duration from the double ridge stage to harvest maturity by c. 34 days. A similar difference resulted from only 1·6 °C warming in 1992/93. Although the range of mean seasonal temperatures differed between years, the relation between temperature and rate of development from sowing to harvest maturity was common to both years (base temperature, −0.8 °C; thermal time 2410 °C d). Carbon dioxide concentration had no effect on this relation or on that between temperature and the rate of development from sowing to the double ridge stage and from the double ridge stage to harvest maturity. Carbon dioxide enrichment increased tillering substantially in 1991/92; there were 200 more shoots m−2 at terminal spikelet formation in crops grown at elevated compared to normal CO2 (additional shoots were principally coleoptile tillers and those developing after tiller 2) and this difference was reduced to 100 shoots m−2 approaching harvest maturity (additional shoots remaining were those developing after tiller 2). In contrast, no effect of CO2 enrichment on tillering was detected at any stage of development in 1992/93. The number of tillers per plant at terminal spikelet formation was a linear function of main stem dry weight at this developmental stage; this relationship was not affected by year or CO2. As CO2 enrichment increased main stem dry weight in the first year only, when main stem dry weights at normal CO2 were only one half of those values determined in the following year, it is concluded that any benefit of increase in CO2 concentration to tillering in winter wheat may be greatest in those crop production environments where main stem dry weights at terminal spikelet are least and vice versa.
A global assessment of the potential impact of climate change on world food supply suggests that doubling of the atmospheric carbon dioxide concentration will lead to only a small decrease in global crop production. But developing countries are likely to bear the brunt of the problem, and simulations of the effect of adaptive measures by farmers imply that these will do little to reduce the disparity between developed and developing countries.
We assert that the simulation of fine-scale crop growth processes and agronomic adaptive management using coarse-scale climate change scenarios lower confidence in regional estimates of agronomic adaptive potential. Specifically, we ask: 1) are simulated yield responses tolow-resolution climate change, after adaptation (without and with increased atmospheric CO2), significantly different from simulated yield responses tohigh-resolution climate change, after adaptation (without and with increased atmospheric CO2)? and 2) does the scale of the soils information, in addition to the scale of the climate change information, affect yields after adaptation? Equilibrium (1 CO2
versus 2 CO2)climate changes are simulated at two different spatial resolutions in the Great Plains using the CSIRO general circulation model (low resolution) and the National Center for Atmospheric Research (NCAR) RegCM2 regional climate model (high resolution). The EPIC crop model is used to simulate the effects of these climate changes; adaptations in EPIC include earlier planting and switch to longer-season cultivars. Adapted yields (without and with additional carbon dioxide) are compared at the different spatial resolutions. Our findings with respect to question 1 suggest adaptation is more effective in most cases when simulated with a higher resolution climate change than its more generalized low resolution equivalent. We are not persuaded that the use of high resolution climate change information provides insights into the direct effects of higher atmospheric CO2 levels on crops beyond what can be obtained with low resolution information. However, this last finding may be partly an artifact of the agriculturally benign CSIRO and RegCM2 climate changes. With respect to question 2, we found that high resolution details of soil characteristics are particularly important to include in adaptation simulations in regions typified by soils with poor water holding capacity.
The climate of the 1930s was used as an analog of the climate that might occur in Missouri, Iowa, Nebraska and Kansas (the MINK region) as a consequence of global warming. The analog climate was imposed on the agriculture of the region under technological and economic conditions prevailing in 1984/87 and again under a scenario of conditions that might prevail in 2030. The EPIC model of Williamset al. (1984), modified to allow consideration of the yield enhancing effects of CO2 enrichment, was used to evaluate the impacts of the analog climate on the productivity and water use of some 50 representative farm enterprises. Before farm level adjustments and adaptations to the changed climate, and absent CO2 enrichment (from 350 to 450 ppm), production of corn, sorghum and soybeans was depressed by the analog climate in about the same percent under both current and 2030 conditions. Production of dryland wheat was unaffected. Irrigated wheat production actually increased. Farm level adjustments using low-cost currently available technologies, combined with CO2 enrichment, eliminated about 80% of the negative impact of the analog climate on 1984/87 baseline crop production. The same farm level adjustments, plus new technologies developed in response to the analog climate, when combined with CO2 enrichment, converted the negative impact on 2030 crop production to a small increase. The analog climate would have little direct effect on animal production in MINK. The effect, if any, would be by way of the impact on production of feed-grains and soybeans. Since this impact would be small after on-farm adjustments and CO2 enrichment, animal production in MINK would be little affected by the analog climate.
Montly mean values of the global radiation intensity (Q
s) and the screen temperature (T) for a year have been gathered from 67 places at latitudes below 60 degrees. Plotting temperature versus radiation in each case yiclds a loop, whose range, mid-point, area and general slope have been evaluated. Various regularities have been discovered in the interrelationships of these features, and between them and the altitude, the upwind distance overland (d), and the latitude (A). Annual means of the radiation (Qs cal/cms · min) and the temperature corrected for altitude (T
c°C) are connected thus: Tc/Qs=102−1.2A. The quotient of the annual ranges of monthly mena temperatures and global radiation intensities (R
T/RQ) varies asd
0.2
The empirical relationships allow approximate estimation of the annual loop of radiation and temperature of any place at a latitude less than 60 degrees. For this, it is necessary to know only the latitude, altitude and the overland distance upwind, so this method of obtaining indicative values of the monthly mean global radiation intensity and screen temperature may be useful for poorlyinstrumented regions.
Land use in Ecuador was investigated by means of statistical analysis with the purpose of deriving quantitative estimates of the relative areas of land use types on the basis of biogeophysical, socio-economic and infrastructural conditions. The smallest spatial units of investigation were 5 by 5 minute (9.25×9.25 km) cells of a homogenous geographical grid covering the whole country. Through aggregations of these cells, a total of six artificial aggregation levels was obtained with the aim of analysing spatial scale dependence of land use structure. For all aggregation levels independent multiple regression models were constructed for the estimation of areas within cells of the land use/cover types permanent crops, temporary crops, grassland and natural vegetation. The variables used in the models were selected from a total of 23 variables, that were considered proxies of biogeophysical, socio-economic and infrastructural conditions driving Ecuadorian land use. A spatial stratification was applied by dividing the country into three main eco-regions. The results showed that at higher aggregation levels, the independent variables explained more of the variance in areas of land use types. In most cases, biogeophysical, socio-economic as well as infrastructural variables were important for the explanation of land use, although the variables included in the models and their relative importance varied between land use types and eco-regions. Also within one eco-region, the model variables varied with aggregation level, indicating spatial scale effects. It is argued that these types of analyses can support the quantitative multi-scale understanding of land use, needed for the modelling of realistic future land use change scenarios that take into account local and regional conditions of actual land use.
An analysis of trends in yield and yield stability throughout the century was made for 21 countries (Algeria, Argentina, Australia, Canada, Chile, Egypt, France, Germany, India, Italy, Japan, Mexico, New Zealand, South Africa, Spain, Sweden, Tunisia, UK, Uruguay, USA and the former USSR). Regressions (linear, bi-linear or tri-linear fitted with an optimisation technique) were used to evaluate the trends in yield during the century. Residuals and relative residuals of these regressions were used to evaluate in absolute and relative terms, respectively, trends in yield stability. Countries varied greatly in their yields and yield gains as well as in changes in harvested area. But almost all of them showed a remarkable lack of yield gain during the initial 3 to 5 decades of this century, followed by noticeable increases in yield. Yield trends for relatively young agricultural wheat-exporting countries, such as Argentina, Australia, Canada and USA, reveal an important breakpoint ca. 2 decades earlier than European countries with longer tradition in wheat production. In addition, yield gains in many countries have apparently been levelling off during the last decade. Trends in yield residuals during the present century revealed a decrease in yield stability in 14 of the 21 countries analysed, but the increase in yield residuals was relatively small (≤0.3 Mg ha−1) compared with increases in yield. Therefore, relative yield residuals indicated that yield stability, as a percentage of yield, increased or at least did not change for most of the analysed countries. Moreover, it is suggested that wheat production systems have been, in general, highly successful in increasing yield while maintaining or increasing relative yield stability with respect to that existing at the beginning of the century. Finally, no relationship was found between variations in yield stability, both in absolute and relative terms, and the increase in yield comparing the present values and those at the beginning of the century.
Phenological development, leaf emergence, tillering and leaf area index (LAI), and duration (LAD) of spring wheat cv. Minaret, grown in open-top chambers at different sites throughout Europe for up to 3 years at each site, were investigated in response to elevated CO2 (ambient CO2×2) and ozone (ambient ozone ×1.5) concentrations.Phenological development varied among experiments and was partly explained by differences in temperature among sites and years. There was a weak positive relationship between the thermal rate of development and the mean daylength for the period from emergence to anthesis. Main stems produced on average 7.7 leaves with little variation among experiments. Variation was higher for the thermal rate of leaf emergence, which was partly explained by differences in the rate of change of daylength at plant emergence among seasons. Phenological development, rate of leaf emergence and final leaf number were not affected by CO2 and ozone exposure. Responses of tillering and LAI to CO2 and ozone exposure were significant only in some experiments. However, the direction of responses was consistent for most experiments. The number of tillers and ears per plant, respectively, was increased as a result of CO2 enrichment by about 13% at the beginning of stem elongation (DC31), at anthesis and at maturity. Exposure to ozone had no effect on tillering. LAI was increased as a result of CO2 elevation by about 11% at DC31 and by about 14% at anthesis. Ozone exposure reduced LAI at anthesis by about 9%. No such effect was observed at DC31. There were very few interactive effects of CO2 and ozone on tillering and LAI. Variations in tillering and LAI, and their responses to CO2 and ozone exposure, were partly explained by single linear relationships considering differences in plant density, tiller density and the duration of developmental phases among experiments. Consideration of temperature and incident photosynthetically active radiation in this analysis did not reduce the unexplained variation. There was a negative effect of ozone exposure on leaf area duration at most sites. Direct effects of elevated CO2 concentration on leaf senescence, both positive and negative, were observed in some experiments. There was evidence in several experiments that elevated CO2 concentration ameliorated the negative effect of ozone on leaf area duration. It was concluded from these results that an analysis of the interactive effects of climate, CO2 and ozone on canopy development requires reference to the physiological processes involved.
Costa Rican land use and cover (in 1973 and 1984) were investigated using a nested scale analysis. Spatial distributions of potential biophysical and human land use/cover drivers were statistically related to the distribution of pastures, arable lands, permanent crops, natural and secondary vegetation, for 0.1° grid units and five artificially aggregated spatial scales. Multiple regression models describing land use/cover variability have changing model fits and varying contributions of biophysical and human factors, indicating a considerable scale dependence of the land use/cover patterns. The observation that for both years each land use/cover type has its own specific scale dependencies suggests a rather stable scale-dependent system. In Costa Rica two land use/cover trends between 1973 and 1984 can be discerned: (a) intensification in the urbanized Central Valley and its surroundings, where agriculture is extended to steeper and less favourable soils due to a high population density; and (b) land use expansion in remoter areas, where the extension of arable land and pastures increased at the cost of natural vegetation. This deforestation was not driven by land shortage. The scale analysis of the Costa Rica land use/cover confirms that land use/cover heterogeneity is, like ecosystem and landscape heterogeneity, a multiscale characteristic which can best be described as a nested hierarchical system.
Average yield of most crops in many countries increased significantly during the past 50 to 100 years. Although atmospheric CO2 concentration, [CO2]a, also increased during that time period, and although crop growth and yield can respond positively to [CO2]a increase, yield increases were due mainly to factors other than increasing [CO2]a. Similarly, some yield increases prior to 1900 were also associated primarily with factors other than changes in [CO2]a. In particular, past national average yield increases were the result chiefly of technological advances such as nitrogen fertilization; selection of genotypes with increased harvest index and disease resistance; mechanization of planting, cultivation, and harvesting; and chemical weed and pest control. If technology continues to increase average yields at recent rates, near-future increases in [CO2]a will have only small impacts on yield in comparison to technology in many countries. Conversely, if future increases in [CO2]a are the main drivers of future yield increases, those yield increases will be small. These points are demonstrated through a comparison of (i) long-term records of yield, (ii) data from key controlled-[CO2] experiments, and (iii) records of past [CO2]a. Finally, it is noted that continued [CO2]a increase may bring with it climatic changes that could have negative or positive impacts on future yield.
Crop production was simulated with the Erosion Productivity Impact Calculator (EPIC) for five representative farms in the Midwestern USA under a variety of climate scenarios. The impact on yields and water use in corn, soybean, winter wheat and sorghum was simulated over a range of temperature, precipitation, solar radiation, humidity and atmospheric carbon dioxide concentration ([CO2]) conditions. Stomatal resistance and leaf area index, plant physiological factors affected by changes in [CO2], were also varied in some of the simulations. Changes in each of these variables altered crop yields and water use. Increases in temperature accelerated the phenological development for all crops, shortened time to maturity, lowered yields, and decreased water use efficiency. Changes in precipitation and vapor pressure affected crop yield and water use by altering the degree of water stress experienced by the crop. For all crops, changes in precipitation and vapor pressure were positively correlated with changes in yield. Changes in solar radiation, which affect the amount of photosynthetically active radiation captured by the plant, were positively correlated with changes in crop yield. Increases in [CO2] and consequent increases in leaf area index and stomatal resistance increased crop yield and water use efficiency, lessening any negative impacts of changes in temperature, precipitation, vapor pressure and solar radiation and amplifying their positive effects. Interactions between different climate variables resulted in crop yield responses ranging from a multiplicative decrease when humidity and precipitation are decreased.to a reduction in crop yield when solar radiation is increased and precipitation decreased. The demonstrated interactions of climatic factors indicate that future studies of climate change impacts should consider the full spectrum of climate variables and changes in atmospheric CO2 and not just temperature and precipitation.
The spatial aggregation of climate and soils data for use in site-specific crop models to estimate regional yields is examined. The purpose of this exercise is to determine the optimum spatial resolution of observed climate and soils data for simulating major crops grown in the central Great Plains (maize, wheat), beginning at a scale of 2.8°×2.8° (T42), which is close to that of the European Centre for Medium-Range Forecasting (ECMWF) general circulation model (GCM) grid cell and progressively disaggregating climate and soils data to finer spatial scales. Using the Erosion Productivity Impact Calculator (EPIC) crop model, observed crop yields for the period 1984–1992 are compared with yields simulated with observed 1984–1992 climate. The goal is to identify the spatial resolution of climate and soils data which minimizes statistical error between observed and modeled yields. Agreement between simulated and observed maize and wheat was greatly improved when climate data was disaggregated to approximately 1°×1° resolution. No disaggregation results for hay were statistically significant. Disaggregation of climate data finer than the 1°×1° resolution gave no further improvement in agreement. Disaggregation of soils data gave no additional improvement beyond that of the disaggregation of climate data.
To investigate the impact of recent climatic changes on the plant development in Europe, this study uses phenological data of the International Phenological Gardens for the period 1969–1998. For this study, the leafing dates of four tree species (Betula pubescens, Prunus avium, Sorbus aucuparia and Ribes alpinum) were combined in an annual leaf unfolding index to define the beginning of growing season. The end of growing season was defined using the average leaf fall of B. pubescens, P. avium, Salix smithiana and R. alpinum. A nearly Europe-wide warming in the early spring (February–April) over the last 30 years (1969–1998) led to an earlier beginning of growing season by 8 days. The observed trends in the onset of spring corresponded well with changes in air temperature and circulation ( North Atlantic Oscillation Index (NAO-index)) across Europe. In late winter and early spring, the positive phase of NAO increased clearly, leading to prevailing westerly winds and thus to higher temperatures in the period February–April. Since the end of the 1980s the changes in circulation, air temperature and the beginning of spring time were striking. The investigation showed that a warming in the early spring (February–April) by 1°C causes an advance in the beginning of growing season of 7 days. The observed extension of growing season was mainly the result of an earlier onset of spring. An increase of mean annual air temperature by 1°C led to an extension of 5 days.
In several land use models statistical methods are being used to analyse spatial data. Land use drivers that best describe land use patterns quantitatively are often selected through (logistic) regression analysis. A problem using conventional statistical methods, like (logistic) regression, in spatial land use analysis is that these methods assume the data to be statistically independent. But, spatial land use data have the tendency to be dependent, a phenomenon known as spatial autocorrelation. Values over distance are more similar or less similar than expected for randomly associated pairs of observations. In this paper correlograms of the Moran’s I are used to describe spatial autocorrelation for a data set of Ecuador. Positive spatial autocorrelation was detected in both dependent and independent variables, and it is shown that the occurrence of spatial autocorrelation is highly dependent on the aggregation level. The residuals of the original regression model also show positive autocorrelation, which indicates that the standard multiple linear regression model cannot capture all spatial dependency in the land use data. To overcome this, mixed regressive–spatial autoregressive models, which incorporate both regression and spatial autocorrelation, were constructed. These models yield residuals without spatial autocorrelation and have a better goodness-of-fit. The mixed regressive–spatial autoregressive model is statistically sound in the presence of spatially dependent data, in contrast with the standard linear model which is not. By using spatial models a part of the variance is explained by neighbouring values. This is a way to incorporate spatial interactions that cannot be captured by the independent variables. These interactions are caused by unknown spatial processes such as social relations and market effects.
Crop growth models are essentially site-based, and use of such models for assessing regional productivity of crops requires methods for aggregating over space. Different method for aggregating simulated county and national crop yields for winter wheat (Triticum aestivum L.) in Denmark were tested using a crop simulation model (CLIMCROP), which was run with and without irrigation for a range of soil types and climatic conditions. The aggregated county or national yield was calculated by summing simulated yield of each category multiplied by the area, they represent. Ten different combinations of scales of climate and soil data were used. The wheat area was distributed between the different soil types using either a uniform distribution or a distribution that gave preference to soils with high water-holding capacity. The simulated results were compared with Danish county and national yield statistics for winter wheat from the period 1971–1997. There was, in general, a poor relationship between simulated and observed yields when the observed yields had been detrended to remove the technology effect. A larger fraction of the inter-annual variability was captured by the model on the loamy soils compared with the sandy soils. The model was able to capture most of the spatial variation in observed yields, except at the coarsest resolutions of the soil data. The finest resolution of soil and climate data gave a better fit of simulated to observed spatial autocorrelation in yield. The results indicate that upscaling of simulated productivity of crops for Danish conditions requires a spatial resolution of soil data of or finer. A single climate station may be sufficient if only national yields are estimated, but more stations are required, if regional yields are to be estimated. Consideration should also be given to the distribution of crop area on the different soil types.
A method was developed for scaling-up the AFRCWHEAT2 model of phenological development from the site to the continental scale. Four issues were addressed in this methodology: (i) the estimation of daily climatic data from monthly values, (ii) the estimation of spatially variable sowing dates, (iii) the simulation of multiple cultivars, and (iv) the validation of broad-scale models. Three methods for estimating daily minimum and maximum temperatures from monthly values were compared using AFRCWHEAT2: a sine curve interpolation, a sine curve interpolation with random daily variability, and two stochastic weather generators (WGEN and LARS-WG). The sine curve interpolation was selected for the continental scale application of AFRCWHEAT2 because computational time was short and errors were acceptably small. The average root mean square errors (RMSEs) for the dates of double ridges, anthesis and maturity were 6.4, 2.2 and 2.1 days, respectively. The spatial variability of European sowing dates was reproduced using a simple climatic criterion derived from the AFRCWHEAT2 vernalization curve. The use of several cultivar calibrations enabled the broad-scale model to capture current responses and compare responses to future climate change. Results from the continental scale model were validated using a geographically-referenced database of observed phenological dates, output from other site-based models and sensitivity analysis. The spatial model was able to emulate a similar spatial and temporal variability in phenological dates to these sources under the present climate. The predominant effect of an increase in mean temperature was a reduction in the emergence to double ridges phase. The shift in the timing of subsequent development stages to earlier in the season meant that changes in their duration were relatively minor. Changes in inter-annual temperature variability resulted in only small changes in the mean date of development stages, but their standard deviation altered significantly.
The Agricultural Production Systems Simulator (APSIM) is a modular modelling framework that has been developed by the Agricultural Production Systems Research Unit in Australia. APSIM was developed to simulate biophysical process in farming systems, in particular where there is interest in the economic and ecological outcomes of management practice in the face of climatic risk. The paper outlines APSIM's structure and provides details of the concepts behind the different plant, soil and management modules. These modules include a diverse range of crops, pastures and trees, soil processes including water balance, N and P transformations, soil pH, erosion and a full range of management controls. Reports of APSIM testing in a diverse range of systems and environments are summarised. An example of model performance in a long-term cropping systems trial is provided. APSIM has been used in a broad range of applications, including support for on-farm decision making, farming systems design for production or resource management objectives, assessment of the value of seasonal climate forecasting, analysis of supply chain issues in agribusiness activities, development of waste management guidelines, risk assessment for government policy making and as a guide to research and education activity. An extensive citation list for these model testing and application studies is provided.
