STICS Model Research Articles

Adding a diversity of legumes to a crop decision-support system: Maintaining satisfactory accuracy while keeping the model simple

In a context of economic and environmental concerns in agriculture, legumes appear to be suitable alternative crops to diversify current cropping systems and reduce their dependence on synthetic nitrogen (N) fertiliser and protein from imported soya bean. However, legume-based cropping systems may increase N losses through nitrate leaching if the N available from legumes does not coincide with subsequent crop requirements. To help agricultural advisers manage N in these systems, we adapted the decision-support system Syst’N®, designed to assess N losses in cropping systems, to simulate three annual and one perennial legume crops: pea, faba bean, soya bean and lucerne. To this end, we adapted and simplified existing submodels of legume functioning to include them in Syst’N, to keep the latter simple. We adapted the submodels “BNF” (i.e. biological N fixation) from the STICS model and “dormancy” from the CropSyst model. We also added the ability to enter the flowering date to improve predictions (improvement in N fixation’s rRMSE from 57% to 41% and EF from 0.57 to 0.77). The equations and associated parameter set developed for the four legume crops yielded satisfying predictions of crop biomass (rMBE = 9%, EF = 0.82, rRMSE = 39%) and N content (rMBE = 5%, EF = 0.76, rRMSE = 37%). These performances support the philosophy of Syst’N® that requires minimising the number of additional parameters for users when representing new crops or processes.

Quantifying the impact of frost damage during flowering on apple yield in Shaanxi province, China

Frost damage during flowering is recognized as one of the most serious agro-meteorological disasters affecting apple production in Shaanxi province, a typical apple producing area in China. Quantitative assessments of flowering frost damage to apple yield are critical for the development of strategies on mitigating yield losses, but have been rarely conducted. For the first time, our study used the process-based STICS model and statistical methods based on Lagrange interpolation method to assess the impacts of frost damage during flowering on apple yield at five study sites over the past three decades. Four canopy temperature thresholds were set as 0 °C, − 2 °C, − 5 °C, and − 35 °C for 0, 10%, 90%, and 100% frost damage on fruit number, respectively. The study results showed that STICS could effectively simulate the phenology and yield of apple, with the simulation errors less than 15%. Simulated yield losses caused by frost damage during flowering by STICS model were consistent with that estimated by statistical methods. Average yield loss due to frost damage during flowering estimated by STICS model was 6.35–29.27% in frost years during past three decades at five study sites. In general, both the frost occurrence days and intensities were the highest in the recent ten years. Our study provides a method to quantitatively assess the impacts of frost damage during flowering on apple yield for the prevention and mitigation of frost disasters in apple production.

Cover crops maintain or improve agronomic performances of maize monoculture during the transition period from conventional to no-tillage

Maize monoculture systems often cause environmental impacts such as soil erosion, nitrate leaching and pesticide transfer. Conservation tillage and the use of cover crops appear to hold promise for limiting these impacts, but questions about their performance remain, particularly during the transition period after conventional soil tillage ceases. In this study, effects of no-tillage maize and fallow-period management with different cover crop species, either pure or in mixtures, were studied on the agronomic performance of maize and on soil N and water dynamics. The study combined field experiments over three years (2012–2014) and simulation modelling with the STICS model. The biomass of cover crops produced during the fallow period varied from 0.8 to 10.5 t dry matter ha−1 depending on the cover crop species and date of termination. Differences in soil water content were quantified among the treatments studied, with the lowest water content at the beginning of the maize growing season with late termination of a cover crop composed of pure faba bean or a vetch-oat mixture in 2014 (25–50% of plant-water available capacity). Maize grain yields ranged from 8.5 to 13.6 t dry matter ha−1, with the lowest yields in the bare soil treatment. Simulations with STICS were consistent with the observations and indicated a decrease in drainage of 8–38 mm year−1, depending on the year and cover crop. Introducing cover crops into maize systems is a powerful mechanism for maintaining production while improving environmental performances during the transition phase of soil properties in conservation tillage.

Impacts of climate change on wheat phenology and yield in Indus Basin, Pakistan

Aim of this study is to quantify the impacts of climate change on phenology and yield of winter wheat in rainfed and irrigated regions of Pakistan by using integration of two well-known crop models including STICS and APSIM with CORDEX-SA regional climate models (RCMs). A number of different adaptation strategies based on early sowing (i.e. S1:10 and S2:20 days), irrigation (I1:15% and I2:30% additional water) and a combination of sowing and irrigation adaptations were examined to recover the potential losses that would occur due to climate change. The data for the wheat phenology, biomass (t/ha) at different stages and yield (t/ha) was obtained from several experiments at national research institutes in Pakistan under both rainfed and irrigated conditions. After calibration and validation of both crop models (STICS and APSIM), the current climate data were replaced with the CORDEX-SA RCM-projections for climate change impact analysis. A significant rising and declining trends were observed in temperature and precipitation patterns, respectively, for the selected study regions. Consequently, a substantial impact of climate change on wheat phenology (anthesis stage, maturity stage, growing length), biomass (t/ha) and yield (t/ha) was observed under scenario periods for RCP4.5 and RCP8.5. Additionally, the adaptation strategies on wheat for rainfed regions showed a substantial improvement in wheat biomass and yield simulated by STICS model particularly for sowing-2 under RCP4.5. Irrigated regions showed more improvement for irrigation-2 (I2) and combination of sowing-1 + irrigation-2 (S1 + I2) using the STICS model under both RCPs. Overall, it was observed that changes in crop phenology had a stronger impact in terms of crop yield for RCP8.5 as compare to RCP4.5. This study provides a valuable understanding and way forward for the better wheat management under changes in precipitation and temperature patterns. The study also discuss in detail, the adaptation strategies to cope with potential damage, over two different irrigation zones (rainfed and irrigated) in Pakistan.

基于STICS模型的黄土高原苹果园生态系统服务评估

苹果园在确保最大生产力的同时应适当考虑环境和自然资源，如何权衡其生态系统服务是苹果园可持续发展面临的重要问题之一。利用大田试验和STICS模型相结合的方法研究农业管理措施和气候对黄土高原苹果园生态系统服务的影响，并在此基础上对黄土高原南部半湿润区和北部半干旱区土壤氮可利用性、气候调节、水循环调节和果实生产四种苹果园生态系统服务进行了系统的评估。结果表明：（1）STICS模型均能较好模拟白水和子洲试验果园的产量、单果重、土壤含水量和蒸散发等生态系统服务指标；（2）除固碳与产量、单果重及果树相关指标是协同关系以外，其他生态系统服务之间都是权衡关系；（3）对于各管理措施下的果园平均服务标准值，秸秆覆盖（0.67） > 地布覆盖（0.52） > 清耕（0.30），充分灌溉（0.56） > 轻度亏缺灌溉（0.44） > 重度亏缺灌溉（0.30），其中秸秆覆盖和充分灌溉的果园服务概况最佳，地布覆盖和轻度亏缺灌溉的果园服务概况较相似且最平衡；（4）地布覆盖和轻度亏缺灌溉管理措施缓和权衡作用的效果优于其他管理措施。综上所述，STICS模型能够较好的量化果园生态系统服务概况，农业管理措施是果园生态系统服务强有力的驱动因子。;Apple orchards face the major challenge of ensuring maximal productivity with due consideration for the environment and natural resource. How to balance its ecosystem services is one of the important issues facing the sustainable development of apple orchards. The field experiment and the STICS model were used to study the impacts of agricultural management and climate on the ecosystem services of apple orchards in the Loess Plateau of China. And on this basis, four apple orchard ecosystem services including soil nitrogen availability, climate regulation, water circulation regulation,and fruit production were systematically evaluated in the south semi-humid and north semi-arid regions of the Loess Plateau of China. This model was parameterized by using data collected on two experimental apple orchard sites under irrigating and mulching management in the Loess Plateau. The results showed that: (1) the STICS models could well simulate the ecosystem service indicators such as yield, fruit mass, soil water content, and evapotranspiration of the Baishui and Zizhou experimental orchards. (2) Except for the synergies between carbon sequestration and yield, fruit mass and fruit-related indicators, there is a trade-off relationship among other ecosystem services. The contribution of carbon allocaton to fruit in sequestrated carbon was considerable. Nitrogen absorption, impacted by mulch and irrigation, was a major driver of these ecosystem service relationships. (3) For the average service standard value of the orchard under various management measures, straw mulching (0.67) > film mulching (0.52) > clearing cultivation (0.30), comfort irrigation (0.56) > low water stress irrigation (0.44) > high water stress irrigation (0.30). The service profile of the orchard with straw mulching and comfort irrigation were the best. And the service profile of the orchard with film mulching and low water stress irrigation were similar and the most balanced, showing higher yield, fruit mass and soil water content but lower N₂O emission. (4) The effect of mitigating trade-offs of straw mulching and low water stress irrigation are better than other management measures, and these two apple orchards are the most ideal orchards. In summary, the STICS model can quantify orchard ecosystem services profiles well, and agricultural management is a powerful driving factor of orchard ecosystem services.

C and N dynamics with repeated organic amendments can be simulated with the STICS model

Repeated applications of organic amendments increase soil organic carbon (SOC) storage and nitrogen (N) availability for crops. Soil-crop models can facilitate the study of these effects and optimize the management of amendments. In soil-crop models, C and N mineralization of amendments is simulated by several pools of soil organic matter which have their specific turnover rate. We used the STICS model to quantify the effects of amendments on C and N dynamics in the long-term QualiAgro experiment, in which four amendments were spread every 2 years since 1998. We studied the model’s ability to simulate laboratory incubation and field experiments, depending on the calibration method and the partitioning of the amendment into one or two pools. The one-pool model simulated C and N dynamics in the field experiment as accurately as the two-pools model with a calibration based on field data. However, C and N dynamics measured under laboratory conditions could only be simulated with the two-pools model, which was then used to simulate C and N in the field. The root mean square errors were 1.6 t DM ha−1 for grain yield, 2.6 t C ha−1 for SOC and 41 kg N ha−1 for soil mineral N. Model parameters could be determined using the C:N ratio of amendments and the indicator of remaining organic carbon (IROC), measured at the laboratory. The STICS model can thus be used to simulate SOC and N dynamics with long-term amendments with a simple calibration.

Nitrogen benefits of ten legume pre-crops for wheat assessed by field measurements and modelling

Abstract The positive effect of grain legume pre-crops on the yield of the subsequent crop has been studied widely, whereas less information is available on the nitrogen (N) processes related to this positive effect, especially for a wide range of grain legume species. The objective was to quantify and understand the effect of grain legume compared to cereal pre-crops (sown in 2014 and 2016) on grain and shoot N yields and shoot N concentration of wheat (Triticum aestivum) grown the following year (2015 and 2017). Spring legumes (faba bean (Vicia faba), fenugreek (Trigolia foenum-graecum), common vetch (Vicia sativa), lentil (Lens culinaris), lupin (Lupinus albus), and pea (Pisium sativum)) were compared to barley (Hordeum vulgare). Summer legumes (chickpea (Cicer arietinum), common bean (Phaseolus vulgaris), soybean (Glycine max) and Narbonne vetch (Vicia narbonensis)) were compared to sorghum (Sorghum bicolor). Inorganic N remaining in the soil at pre-crop harvest (N sparing) was measured. The STICS model which accurately predicted soil humidity and soil inorganic N in the pedoclimatic conditions of the field experiments was used to calculate N mineralisation from pre-crop residues and N leaching between pre-crop harvest and wheat harvest. Grain and shoot N yields of unfertilised N wheat were respectively 27 and 25 % higher after faba bean and lentil compared to barley pre-crops, and 66 and 51 % higher after summer legumes compared to sorghum pre-crop. In the second experiment, N fertilisation reduced the positive effect of fenugreek, and lentil on wheat yield compared to barley while it did not modify the pre-crop effect of the other legumes. A positive relationship between N mineralisation from pre-crop residues and wheat shoot N yield was established, with higher amounts of N available for the subsequent wheat after legume pre-crops. In 2014, N sparing was higher after spring pre-crops compared to summer pre-crops inducing higher N leaching after spring legume pre-crops, especially in the first experiment which was characterised by heavy rain in summer and autumn. Estimating N availability by taking into account N sparing, N mineralisation from soil and pre-crop residues and N leaching explained 49 % of wheat shoot N yield variability. Unquantified N processes and non-N processes might also have contributed to the positive effects of legumes on the subsequent wheat.

Ideotype definition to adapt legumes to climate change: A case study for field pea in Northern Italy

One of the key strategies to alleviate negative impacts of climate change on crop production is the development of new cultivars better adapted to the conditions expected in the future. Despite the role of legumes as protein sources, medium- and long-term strategies currently debated mainly focus on agricultural policies and on improved management practices, whereas ideotyping studies using climate projections are scarcely reported. The objective of this study was to define pea ideotypes improved for yield and irrigation water productivity targeting current climate and four future projections centred on 2040, resulting from the combination of two General Circulation Models (HadGEM2 and GISS-ES) and two Representative Concentration Pathways (RCP4.5 and RCP8.5). The STICS model was used, with the default pea parameterization refined using data from two years of dedicated field experiments. Ideotypes were defined by combining STICS and the E-FAST sensitivity analysis method focusing on model parameters representing traits on which breeding programs are ongoing. Results showed that climate change is expected to decrease the productivity of current pea cultivars (up to -12.6%), and that increasing irrigation (to cope with the expected less favourable rainfall distribution) would not avoid yield losses. The proposed ideotypes, characterized by a shorter vegetative phase and by increased tolerance to high temperature, performed better than current varieties, providing higher yields (+4.5%) and reduced water consumption (-20%). For the first time, we demonstrated the suitability of STICS for ideotyping purposes and used a simulation model to define pea breeding strategies targeting future climate conditions.

Long-term modelling of crop yield, nitrogen losses and GHG balance in organic cropping systems

Although organic cropping systems are promoted for their environmental benefits, little is known about their long-term impact on nitrogen (N) fate in the soil–plant-atmosphere system. In this paper, we analyze two long-term experiments: DOK in Switzerland (39-yr) and Foulum organic in Denmark (19-yr). Four treatments were considered in each experiment: two conventional treatments with (CONFYM) or without manure (CONMIN), organic with manure (BIOORG) and unfertilized treatment (NOFERT) at DOK; conventional (CGL-CC+IF) and three organic treatments, one with cover crops only (OGL+CC-M) and two including cover crops and grass-clover with (OGC+CC+M) or without manure (OGC+CC-M), at Foulum. STICS model was used to simulate crop production, N surplus, nitrate leaching, gaseous N losses and changes in soil organic N. It was calibrated in the conventional treatments and tested in organic systems. The crop production, N surplus and soil organic N stocks were satisfactorily predicted. The mean N surplus greatly differed between treatments at DOK, from −58 (NOFERT) to +21 kg N ha−1 yr−1 (CONFYM), but only from −9 (OGL+CC-M) to +21 kg N ha−1 yr−1 (OGC+CC+M) in Foulum. Soil N pools declined continuously in both sites and treatments at a rate varying from −18 to −78 kg N ha−1 yr−1, depending on fertilization and crop rotation. The decline was consistent with the observed N surpluses. Although not all simulations could be tested against field observations and despite of prediction uncertainties, simulations confirm the hypothesis that environmental performances resulting from C and N cycles depend more on specificities of individual than nominal treatments. Significant correlations appeared between long-term N surplus and soil N storage and between total N fertilization and total N gaseous losses. Results showed in both experiments that arable organic systems do not systematically have lower N surplus and N losses than conventional ones, providing opportunity for increasing N use efficiency of these systems.

Long-term modelling of soil N mineralization and N fate using STICS in a 34-year crop rotation experiment

Net soil N mineralization is a driver for N uptake and N losses at an annual scale, but is itself dependent on long-term N surplus and C-N storage in agricultural systems. The accurate modelling of N mineralization remains challenging. Thus, the STICS research version V1610 that includes modified soil organic nitrogen (SON) mineralization and root biomass turnover modules was assessed in this study regarding its predictions of net N mineralization and long-term N fate in a 34-year experiment comparing crop rotations with or without catch crops (CC) and bare soil. The in situ gross balance method was used as a reference to estimate net N mineralization based on measured N variables (i.e. N uptake, exported N and N leaching). The Index of Agreement (IA) of STICS predictions concerning crop biomass, crop yield, N uptake and exported N ranged between 0.61 and 0.76, 0.79–0.89, 0.49–0.64 and 0.47–0.58, respectively, depending on the crop rotations. STICS also enabled a good simulation of annual drainage and N leaching with IA ranges of 0.92–0.96 and 0.78–0.93, but high leaching values were not captured by the model. The STICS research version simulates the decay of deep roots (below a depth of 25 cm) but it neglects their decomposition. This simplification could cause an underestimation of N leaching. The observed N surplus ranged from 27 to 51 kg N ha−1 yr−1 in the cropped rotations depending on the crop rotations, and the N surplus was accurately simulated with an IA of 0.75–0.84. STICS produced a good prediction of changes in SON stocks under cropped rotations and bare soil, with both the rRMSE and rMBE being lower than 10%. Estimated mean annual N mineralization was 115 kg N ha−1 under cropped rotations and 42 kg N ha−1 under the bare soil treatment. STICS relatively well predicted net N mineralization regarding both differences between crop rotations and over time. Moreover, STICS correctly simulated the long-term effects of CC on drainage, N leaching, SON accumulation and net N mineralization. To conclude, STICS is a useful model to predict net N mineralization and N fate in long-term crop rotations. Moreover, this work raised new questions concerning the long-term fate of N stored in deep dead roots. Further improvements to describe the fate of these residues should enhance the prediction of N leaching by the STICS model and enable the optimization of N management in cropping systems.-

Simulation of Growth and Leaf Area Index of Quality Protein Maize Varieties in the Southwestern Savannah Region of the DR-Congo

Logistic and exponential approaches have been used to simulate plant growth and leaf area index (LAI) in different growing conditions. The objective of the present study was to develop and evaluate an approach to simulate maize LAI that expresses key physiological and phonological processes using a minimum entry requirement for Quality Protein maize (QPM) varieties grown in the southwestern region of the DR-Congo. Data for the development and testing of the model were collected manually in experimental plots using a non-destructive method. Simulation results revealed measurable variations between crop seasons (long season A and short season B) and between the two varieties (Mudishi-1 and Mudishi-3) for height, number of visible leaves, and LAI. For both seasons, Mudishi-3, a short stature variety was associated with expected stable yield based on simulation data. In general, the model simulated reliably all the parameters including the LAI. The LAI value for mudishi-1 was higher than that of Mudishi-3. There were significant differences among the model parameters (K, Ti, a, b, Tf) and between the two varieties. In all crop conditions studied and for the two varieties, the senescence rate (a) was higher, while the growth rate (b) was lower compared to the estimates based on the STICS model.

Carbon footprint of cropping systems with grain legumes and cover crops: A case-study in SW France

Agriculture contributes to a significant proportion of global emissions of greenhouse gases (GHG) but can also participate in climate change mitigation. The introduction of legumes in crop rotations reduces the dependence on N fertilizers and may mitigate the carbon (C) footprint of cropping systems. The aim of this study was to quantify the C footprint of six low-input arable cropping systems resulting from the combination of three levels of grain legumes introduction in a 3-yr rotation (GL0: no grain legumes, GL1: 1 grain legume, GL2: 2 grain legumes) and the use of cover crops (CC) or bare fallow (BF) between cash crops, covering two rotation cycles (6 years). The approach considered external emissions, on-site emissions and soil organic carbon (SOC) stock changes, and prioritized (i) field observations and (ii) simulation of non-measured variables with the STICS model, rather than default emission factors. As expected, fertilizers accounted for 80–90% of external emissions, being reduced by 50% and 102% with grain legumes introduction in GL1-BF and GL2-BF, compared to the cereal-based rotation (GL0-BF). Cover crops management increased machinery emissions by 24–35% compared to BF. Soil nitrous oxide (N2O) emissions were low, ranging between 205 and 333 kg CO2 eq. ha−1 yr−1 in GL1-BF and GL0-BF, respectively. Nitrate leaching represented the indirect emission of 11.6 to 27.2 kg CO2 eq. ha−1 yr−1 in the BF treatments and 8.2 to 10.7 kg CO2 eq. ha−1 yr−1 in the CC treatments. Indirect emissions due to ammonia volatilization ranged between 8.4 and 41.8 kg CO2 eq. ha−1 yr−1. The introduction of grain legumes strongly influenced SOC changes and, consequently, the C footprint. In the BF systems, grain legumes introduction in the rotations led to a significant increase in the C footprint, because of higher SOC losses. Contrarily, the use of cover crops mitigated SOC losses, and lowered the C footprint. These results indicated the need of CC when increasing the number of grain legumes in cereal-based rotations. Despite the multiple known benefits of introducing grain legumes in cropping systems our research highlights the need to consider soil organic carbon changes in environmental assessments.

Analyzing ecosystem services in apple orchards using the STICS model

Abstract Fruit tree production faces the major challenge of ensuring maximal productivity with due consideration for the environment and human health. The increasingly recognized concept of ecosystem service could help to address this duality. In this paper, we propose an analytical framework based on a soil crop model to investigate how agricultural management and pedoclimatic conditions affect the joint production of marketed and non-marketed ecosystem services through underlying ecosystem functions in apple orchards. The ecosystem services considered on an annual scale were soil nitrogen availability, climate regulation, water regulation and fruit production. Ecosystem functions and services were described by specific indicators that were quantified using the STICS soil crop model. This model was parameterized using data collected on two experimental apple orchard sites under conventional and low-input or organic management in southeastern France. The interdependencies between environmental components, cultural operations and ecosystem functions were dynamically integrated by the model and highlighted significant interactions between the indicators of ecosystem services. Thus, the service indicators soil organic nitrogen variation and the prevention of nitrogen denitrification and of leaching were positively correlated and in conflict with soil mean nitrate concentration and mean soil humidity. They were also linked negatively to nitrogen mineralization enhanced by irrigation and positively to soil carbon sequestration impacted by fertilization; these two functions were impacted by soil conditions. Yield and carbon sequestration presented a strong synergy and were positively correlated to nitrogen absorption increased by mineral fertilization. Globally, nitrogen fertilization management and planting density were particularly important for the delivery of multiple ecosystem services, but soil and climate effects were far from negligible, especially for nitrogen and water-related services. The ecosystem service profiles of the studied cropping systems were diversified, with contrasted profiles showing high yield and carbon sequestration but low prevention of nitrogen denitrification and of nitrogen leaching, and more balanced profiles. The STICS crop model made it possible to quantify and analyze profiles of ecosystem services and should be helpful in instrumenting the dialogue between fruit growers and other stakeholders by simulating scenarios to optimize multiple services. However, it has to be improved to address the impact of grass cover on soil functions and the long-term functioning of apple orchards.

Assessing and modeling economic and environmental impact of wheat nitrogen management in Belgium

Future progress in wheat yield will rely on identifying genotypes and management practices better adapted to the fluctuating environment. Nitrogen (N) fertilization is probably the most important practice impacting crop growth. However, the adverse environmental impacts of inappropriate N management (e.g., lixiviation) must be considered in the decision-making process. A formal decisional algorithm was developed to tactically optimize the economic and environmental N fertilization in wheat. Climatic uncertainty analysis was performed using stochastic weather time-series (LARS-WG). Crop growth was simulated using STICS model. Experiments were conducted to support the algorithm recommendations: winter wheat was sown between 2008 and 2014 in a classic loamy soil of the Hesbaye Region, Belgium (temperate climate). Results indicated that, most of the time, the third N fertilization applied at flag-leaf stage by farmers could be reduced. Environmental decision criterion is most of the time the limiting factor in comparison to the revenues expected by farmers.

The suitability of non-legume cover crops for inorganic soil nitrogen immobilisation in the transition period to an organic no-till system

The aim of the study was to evaluate non-legume cover crops for growing no-till grain legumes in organic farming systems. Evaluated cover crops should be able to suppress weed growth, reduce plant available nitrogen in the soil and produce large amounts of biomass with slow N mineralisation. Six non-legume species; spring rye (Secale cereale L.), black oat (Avena sativa L.), sunflower (Helianthus annuus L.), white mustard (Sinapis alba L.), buckwheat (Fagopyrum esculentum Moench) and hemp (Cannabis sativa L.) were tested. Plots with organic fertiliser (50 kg N ha−1) and without fertiliser incorporation at three locations in south-east Germany were trialled and the cover crops’ ability to produce biomass and accumulate N in plant compartments was evaluated. The N mineralisation from stem and leaf material was simulated using the STICS model. The biomass production ranged from 0.95 to 7.73 Mg ha−1, with fertiliser increasing the total biomass at locations with low-N status. Sunflower consistently displayed large biomass and N accumulation at all locations and fertiliser variations, although not always significantly more than other species. Most N was stored in sunflower leaf material, which can be easily mineralised making it less suited as cover crop before no-till sown spring grain legumes. Rye, which produced slightly less biomass, but accumulated more N in the stem biomass, would be better suited than sunflower in this type of system. The N mineralisation simulation from rye biomass indicated long N immobilisation periods potentially improving weed suppression within no-till sown legume cash crops.

Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions

At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque–Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha−1), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of −1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha−1) of the distribution.

Improved snow-cover model for multi-annual simulations with the STICS crop model under cold, humid continental climates

The objective of this study was to evaluate and improve existing simple snow-cover models to pre-process weather data for improving simulations of soil temperature, soil moisture content, and crop growth under cold, humid continental climates using a crop model. Three snow-cover models were calibrated using readily available input data (minimum and maximum daily air temperature and precipitation) and were evaluated using several data sets from across eastern Canada. The snow-cover model providing the best validation results (model efficiency=0.89) was then used to pre-process climate data used to run the STICS crop model over 3 yr at two sites (Quebec City, QC, and Ottawa, ON). Three output variables (shoot biomass, soil temperature at two depths, and soil moisture content at two depths) were compared with field measurements when the STICS model was used with and without pre-processing of the weather data with the snow-cover model. When climate data were pre-processed with the snow-cover model, the performance of STICS in simulating soil temperature was significantly improved during the non-growing season (November–April). Model performance during the growing season (May–October) was thus similar to the model performance during the non-growing season, with a small bias (<0.5°C), a root mean square error (RMSE) of about 2°C, and a model efficiency higher than 0.93. The simulation of soil moisture with STICS also improved slightly with the use of the snow-cover model, but model performance was better in the 15-to-30-cm soil layer than in the 0-to-15-cm layer (RMSE of about 2vol.% in the 15-to-30-cm layer versus 3.5vol.% in the 0-to-15-cm layer). The prediction of plant shoot dry biomass (spring wheat and corn) with STICS was affected little by the use of the snow-cover model and was good with and without the snow-cover model (RMSE of 1.1tha−1). This study showed that STICS can run over several years in the eastern Canada pedo-climatic context without affecting model performance in predicting crop biomass and the two main soil variables. This result opens new opportunities for using STICS or other crop models under a northern climate characterized by a short growing season and significant snow cover during the non-growing season. This result also offers a foundation for future studies aimed at simulating the winter survival of perennial crops as well as annual water and nitrogen cycling in agricultural soils.

Simulation of Biomass and Nitrogen Dynamics in Perennial Organs and Shoots of Miscanthus × Giganteus Using the STICS Model

Biomass production by perennial plants promises to increase land use efficiency and reduce greenhouse gas emissions from cropping systems dedicated to bioenergy production. The modelling of both biomass production and the environmental impacts of these systems over the long term is needed in order to evaluate their sustainability. New equations have been added to the STICS soil-crop-atmosphere model to provide a better description of perennial organs and their relationship with non-perennial ones, corresponding to the rhizomes and shoots, respectively in the Miscanthus × giganteus case study. Their description is intended to be generic for perennial plants, supported by the functional approach of STICS. The new version of STICS 8 was calibrated using published data and then validated against independent data. It was able to simulate the biomass and nitrogen content of the shoots (with a model efficiency of 0.95 and 0.70, respectively) and reproduce the dynamic of biomass and nitrogen in perennial organs (with a model efficiency of 0.41 and 0.63, respectively). Some of the model’s improvements are discussed. Modifications to the model allowed simulations of the effect of cultural practices, such as nitrogen fertilisation or harvest date, on the biomass and nitrogen content of rhizomes and shoots.

Application of STICS Model in Assessment of the Effects of Irrigation Practices and Soil Properties on Yield of a Durum Wheat (Triticum durum Desf.) Cultivar in the Irrigated Area of Oued Rmel in Tunisia

The progress in computer technology has enabled the development of crop models reproducing the behavior of a crop in a wide range of pedoclimatic conditions and technical itineraries. This work aims to study the impact of total available soil water in the root zone (TAW) on durum wheat yield (Triticum durum Desf.) as affected by irrigation regime in Mediterranean climatic conditions of Tunisia. In this work, STICS model was used to simulate effects of farmer’s irrigation practices in wheat in the pedoclimatic conditions of the irrigated area of Oued Rmel in Tunisia over a 20-year period. Assessment of irrigation practices in the study area was performed, compared to rainfed system, in terms of yield Original Research Article Annual Research & Review in Biology, 4(5): 747-765, 2014 748 and produced biomass at harvest. The model was calibrated to simulate the growth and development of winter wheat using the field observed crop data collected from three growing seasons in two locations in Tunisia. To carry out the study, three types of soil (S1, S2 and S3) in the Oued rmel irrigation scheme were chosen on the basis of their available soil water in the root zone (TAW). The study showed that the model adequately predicts crop yield and biomass. Simulation results showed that the farmers’ irrigation practice results, in higher grain yield and dry matter at harvest as compared to rainfed system. Simulated grain yield was significantly higher in soil with high TAW than in the other soils. Results showed that the highest difference (30%) in simulated grain yield, between the two water regimes, was obtained in soil having high TAW. Depending on the soil type, simulated dry matter at harvest increased from 4% to 12% compared to rainfed system.

Assessment of yield and water use efficiency of durum wheat as affected by irrigation practices and soil properties in the irrigated area of oued rmel in Tunisia

This work aims to study the impact of total available soil water in the root zone (TAW) on the relation between irrigation regime and water use efficiency of a durum wheat (Triticum durum Desf. Var. Karim.) cultivar in the pedoclimatic conditions of the irrigated area of Oued Rmel in Tunisia. After calibration, STICS model was used to simulate effects of two irrigation scenarios on water use efficiency of durum wheat over a 20-year period with regard to farmers irrigation practices in the study area. To carry out the study, three types of soil in the Oued rmel irrigation scheme were chosen on the basis of their TAW. Two scenarios of irrigation scheduling were designed through the STICS model: (i) irrigation was scheduled when 50% of the TAW was depleted (I50); (ii) irrigation was scheduled when 25% of the TAW was depleted (I25). Results showed that lowest values of grain yield were obtained in soil having low TAW as a consequence of a frequent water stress occurring during the reproductive stage of wheat. The highest values of WUE, as determined at grain and biomass basis (WUEg and WUEbio indexes), were obtained when irrigation was scheduled at 25% of the TAW depletion. In I50 and I25 scenarios, highest WUEg, averaging respectively 2.25 and 2.23 kg m -3 was