An overview of APSIM, a model designed for farming systems simulation

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.     

A survey on actual agricultural practices and their effects on the mineral nitrogen concentration of the soil solution

In intensive integrated crop-livestock farming systems, the surplus of N at the farm scale may be large and reflects on the N balance at the field scale. A study was conducted to assess the N fertilizer efficiency in four private farms in intensively cropped areas of NW Italy, and to monitor the effects of agricultural practices on the mineral N concentration of the soil solution, sampled every 2 weeks for 2 years and considered as an indicator of potential leaching. Two cultivation systems were compared in each farm, one involving continuous maize rotation, the other assuring a continuous soil cover (permanent meadow or winter cereal-maize double cropping system). The fertilization level in the arable crops was high (369–509 kg N ha⁻¹ year⁻¹) compared to the crop removals, and resulted in a low efficiency, as indicated by the four examined efficiency indexes (calculated N surplus, N removal-fertilizer ratio, N apparent recovery, N use efficiency). The soil-water-nitrate concentration showed large temporal variations in the range of 1–150 mg l⁻¹ for five out of the eight cropping situations, while concentrations smaller than 10 mg l⁻¹ were always recorded in the meadows and in one of the four soils (Aeric epiaquept). The fertilizer management that characterized each cropping system affected the soil-mineral-nitrate content in shallow arable soils. The longer soil cover duration in double-cropping systems did not result in a reduction of soil N compared to maize as a single crop, not even in winter (the bare-soil intercropping period in maize-based systems). However, the temporal oscillations of the concentration were buffered by the crop cover duration and by the presence of a shallow water table (1 m deep) in the soil profile. The average nitrate content of the soil could be predicted by the N uptake of the crop, the N removal–fertilizer ratio, the soil pH and sand content, however no simple explanatory relationship was found with the experimental factors. Hence, in farm conditions, in the absence of sufficient data for a deterministic model approach, the target of reducing the risk of leaching should be achieved by maximizing the fertilizer efficiency.     

Temporal and Spatial Variation Characteristics of Soil Temperature in Cotton Fields under Different Cropping Systems

[Objective] Soil temperature affects the biochemical processes of crops; therefore, elucidating its spatial and temporal distribution characteristics in different cropping systems is an essential part of understanding how to boost cotton yield potential. [Method] In 2016 and 2017, continuous, real-time soil temperature monitoring was conducted at a depth of 10-110 cm in three cropping systems, including monoculture cotton (MC), wheat/intercropped cotton (WIC), and wheat/direct-seeded cotton (WDC). We investigated the growth process and various agronomic traits. [Result] Different cotton soil temperatures were found between MC and doubled in late May, indicating about 1-3 ℃ higher in the former during the symbiotic period. In early July, the cotton soil temperature of the double-cropping systems at 10-40 cm was higher than that of the MC, but showed the opposite at 40-110 cm. In early August, the differences in soil temperature reduced among the three cropping systems, while the soil temperature of the MC was still slightly lower than that of the double-cropping systems. After mid-September, the soil temperature of the double-cropping systems was lower than that of the MC. The soil temperature mainly influenced the duration of cotton seedling to squaring, and flowering to the boll-opening period. At the same time, there was a subtle effect on the squaring to flowering and boll period. In general, higher average soil temperatures were associated with shorter growth period durations. During the same period, the lowest daily soil temperature of the double-cropping systems occurred about one hour earlier than in the MC; however, the highest daily temperature appeared at the same time. There was a linear relationship between accumulated soil temperature and biomass at different layers across cropping systems. [Conclusion] Controlling the timing and quantity of irrigation water can assist agronomic practices by alleviating the effect of soil temperature on cotton growth. Increased accumulative soil temperature is beneficial to cotton emergence and boll opening in double-cropping systems. This study provides a theoretical basis for rational allocation and management of different cropping systems.     

Adaptation of crop management to water-limited environments

Farmers must combine various crop management strategies to cope with water deficit resulting from soil, weather or limited irrigation: drought escape, avoidance or tolerance, crop rationing, irrigation (supplemental, deficit). These strategies can be translated into six objectives: (i) increasing soil stored water at plant sowing, (ii) increasing soil water extraction, (iii) reducing the contribution of soil evaporation to total water-use, (iv) optimizing the seasonal water use pattern between pre- and post-anthesis, (v) tolerate water stress and recover after stress alleviation, and (vi) irrigate at the most-sensitive growth phases. To reach these objectives, tactical decisions concern soil tillage, type of crop and cultivar, sowing date and density, N fertilization, irrigation timing, amount and frequency. Flexible crop management systems based on decision rules should be preferred to the recommendation of fixed packages of techniques. Timing, intensity, and predictability of drought (intermittent, terminal) are important features for choosing the cropping alternatives. Simulation models may help the farmer to select best-bet management options on the basis of historical long-term weather records. Simple soil and plant indicators associated with real-time decision support systems should be developed to revise the initial management plan by integrating in-season weather information.     

Evaluation of CropSyst for simulating the yield of flooded rice in northern Italy

Research on rice cropping systems carried out in Europe has to face the great variability of pedo-climatic conditions, and the linked abundance of cultivated varieties, characteristic of the high latitudes-temperate areas where rice is traditionally grown.

Dynamic simulation models can provide an useful tool for system analysis needed to improve the knowledge, the agronomic management and crop monitoring.

For calibrate and validate CropSyst (never used for rice), a process-based simulation model, for Indica-type and Japonica-type varieties, data obtained from five field experiments, carried out in Northern Italy between 1989 and 2002, were used.

Plants were sampled during the life cycle from rice plots of five cv Loto, Cripto, Ariete, Drago, Thaibonnet and Sillaro, maintained at potential production, to determine some important crop variables and parameters such as aboveground biomass (AGB), leaf area index, specific leaf area, harvest index, the date of the main phonological stages.

At the end of the calibration process to the parameters (the others were set to the default value, taken from the Literature or measured) optimum mean daily temperature for growth, specific leaf area (for Japonica varieties), stem/leaf partition coefficient (empirical), leaf duration, were assigned the following values: 28 and 27 °C respectively for Japonica and Indica varieties, 27 and 29.5 m² kg⁻¹ respectively for Japonica early and medium-late varieties, 4.5, 3, 1.5 for Japonica early, medium-late and Indica varieties, 700, 850, 950 °C-days for the three groups of varieties.

The assessment of model performances has shown average RRMSEs of 20 and 22% at the end of calibration and for the validation process; the modelling efficiency is always positive and the coefficient of determination always very close to 1. General improvements will be achieved by the model by considering the thermal profile (strongly influenced by flooding water at mid latitudes) evolving in and over the canopy.     

Intercropping maize and wheat with conservation agriculture principles improves water harvesting and reduces carbon emissions in dry areas

In arid and populated areas or countries, water shortage and heavy carbon emissions are threatening agricultural sustainability with food security severely, and becoming a major issue. It is unclear whether improved farming systems can be developed to tackle those issues through a sustainable agriculture. Here three farming practices that have proven to be essential and successful, which were: (a) crop intensification through strip intercropping, (b) water harvesting through conservation tillage; and (c) carbon sequestration through improved crop residue management options, were integrated in one cropping system. We hypothesize that the integrated system allows the increase of crop yields with improved water use efficiency, while reducing carbon emissions from farming. The hypothesis was tested in field experiments at Hexi Corridor (37°96′N, 102°64′E) in northwest China. We found that the integrated system increased soil moisture (mm) by 7.4% before sowing, 10.3% during the wheat–maize co-growth period, 8.3% after wheat harvest, and 9.2% after maize harvest, compared to the conventional sole cropping systems. The wheat/maize intercrops increased net primary production by 68% and net ecosystem production by 72%; and when combined with straw mulching on the soil surface, it decreased carbon emissions by 16%, compared to the monoculture maize without mulch. The wheat/maize intercrops used more water but increased grain yields by 142% over the monoculture wheat and by 23% over the monoculture maize, thus, enhancing water use efficiency by an average of 26%. We conclude that integrating strip intercropping, conservation tillage as well as straw mulching in one cropping system can significantly boost crop yields, improve the use efficiency of the limited water resources in arid areas, while, lowering the carbon emissions from farming. The integrated system may be considered in the development of strategies for alleviating food security issues currently experienced in the environment-damaged and water-shortage areas.     

Optimizing wheat productivity in two rain-fed environments of the West Asia–North Africa region using a simulation model

The performance of a crop simulation model (agricultural production systems simulator model, APSIM-Nwheat) was tested using data obtained from several locations in the rain-fed environments of West Asia and North Africa (WANA) in Morocco and Jordan. The model was able to simulate wheat grain yields reasonably well except at one site in one season in Morocco. The model was subsequently used to analyze the effect of soil type (soil water-holding capacity), rate and timing of nitrogen (N) fertiliser, initial soil moisture storage, cultivars (early versus late), sowing dates and density and supplemental irrigation (SI) in optimizing wheat production using 20 years of historical weather records from Morocco. The simulation indicated that yields were often limited by the amount and timing of rainfall. While the effect of N fertiliser was minimal or detrimental in dry years, it improved grain yields in wet years and when crops were sown early combined with pre-sown stored plant available water in the soil. The analysis showed that early sowing is important for achieving high yields by avoiding terminal water deficit. There is little difference between grain yields when current practice of about 300 plants/m² was compared with a density of 150 plants/m². This implies that there is scope for reducing current planting density to save seeds without reducing yields. The simulation analysis highlighted that 40 mm of SI at sowing significantly improved average grain yields as a result of enabling early crop establishment, in particular with a N fertiliser application of 40 kg N/ha. The analysis indicated that wheat grain yields in the arid and semi-arid rain-fed regions of WANA can be improved compared to current yield levels by adjusting N management to soil type, pre-sowing soil water availability, sowing opportunity and the availability of SI.     

Multi-model uncertainty analysis in predicting grain N for crop rotations in Europe

Realistic estimation of grain nitrogen (N; N in grain yield) is crucial for assessing N management in crop rotations, but there is little information on the performance of commonly used crop models for simulating grain N. Therefore, the objectives of the study were to (1) test if continuous simulation (multi-year) performs better than single year simulation, (2) assess if calibration improves model performance at different calibration levels, and (3) investigate if a multi-model ensemble can substantially reduce uncertainty in reproducing grain N. For this purpose, 12 models were applied simulating different treatments (catch crops, CO₂ concentrations, irrigation, N application, residues and tillage) in four multi-year rotation experiments in Europe to assess modelling accuracy. Seven grain and seed crops in four rotation systems in Europe were included in the study, namely winter wheat, winter barley, spring barley, spring oat, winter rye, pea and winter oilseed rape. Our results indicate that the higher level of calibration significantly increased the quality of the simulation for grain N. In addition, models performed better in predicting grain N of winter wheat, winter barley and spring barley compared to spring oat, winter rye, pea and winter oilseed rape. For each crop, the use of the ensemble mean significantly reduced the mean absolute percentage error (MAPE) between simulations and observations to less than 15%, thus a multi–model ensemble can more precisely predict grain N than a random single model. Models correctly simulated the effects of enhanced N input on grain N of winter wheat and winter barley, whereas effects of tillage and irrigation were less well estimated. However, the use of continuous simulation did not improve the simulations as compared to single year simulation based on the multi-year performance, which suggests needs for further model improvements of crop rotation effects.     

An overview of the crop model

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, relies essentially on well-known relationships or on simplifications of existing models. One of the key elements of 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 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 model and its ability to adapt to a wide range of agro-environmental issues. Finally, the conceptual limits of the model are discussed.     

Six-year transition from conventional to organic farming: effects on crop production and soil quality

Organic farming has become increasingly important in recent decades as the consumer has grown its focus on the food and environmental benefits of the technique. However, when compared to conventional farming systems, organic farm system are known to yield less.Presented in this paper are the results from two organic cropping systems following six years of organic management. Fertilisation management differentiated the two systems; one was fertilised with green manure and commercial organic fertilisers, while the other was fertilised with dairy manure. A conventional cropping system, managed with mineral fertiliser as typical in the southern Piemonte region (Italy), served as the bussiness as usual crop management. The first hypothesis tested related to crop yield variation during the initial phase of organic management; we expected a sharp reduction in the early phase, then minor reductions later on. The second hypothesis tested related to soil fertility variation; we expected enhanced soil fertility under organic management.Overall, the organic system produced less, relative to the conventional system in interaction with year effect. Yield reduction seemed related to the lower soil nutrient availability of organic fertilisers that provided nutrients consequent to mineralisation. Therefore, summer crops are well-suited to manure-fertilised organic farms as mineralisation happens at higher temperatures, as opposed to winter wheat, which is largely reduced in such systems. Commercial organic fertilisers can, however, limit this effect through their high nutrient availability in the winter and early springAlso shown was that soil quality, defined as a general decrease in soil organic carbon (SOC) over time in the three analysed arable systems, can be mitigated by manure additions. Green manuring can maintain SOC and increase total N in soil, only if introduced for a sufficient number of years during crop rotation. Finally, soil fertility and Potential Mineralisable N in the different systems demonstrated that organic systems managed with commercial organic nitrogen fertilisers and green manure do not improve soil quality, compared to systems managed with mineral fertilisers.     

PRACT (Prototyping Rotation and Association with Cover crop and no Till) – a tool for designing conservation agriculture systems

Moving to more agroecological cropping systems implies deep changes in the organization of cropping systems. We propose a method for formalizing the process of innovating cropping system prototype design using a tool called PRACT (Prototyping Rotation and Association with Cover crop and no Till) applied to a Malagasy case study. The input information for PRACT is comprised of: (i) crop and cover crop adaptation to biophysical conditions, (ii) agroecological functions of the cover crops, (iii) crop production, (iv) association possibilities between crop and cover crop, and (v) agroecological functions of the cropping system. All the information was derived from expert knowledge developed over more than 12 years of agronomic experiments in Madagascar. The final output from PRACT is a list of cropping systems, i.e., crop and cover crop associations and their sequences over three years. These cropping systems are characterized by their potential agroecological functions and crop production. The PRACT model selects a list of cropping systems taking into account the above information by using elaborate rules governing the intercropping and sequences between crops and cover crops. Examples of the outcomes of model simulations are provided for four different kinds of field. Taking into account the range of potential crops and cover crops, the number of cropping systems that was theoretically possible for the different field types ranged from 19,683 to 2.98 ×  10¹³. In a first step, PRACT reduced this number by a factor of up to 28 times to propose possible cropping systems. To do so, cropping systems are selected in terms of the biophysical requirements of plants, plant compatibility and agronomic rules. Not all of these systems are suitable for every farmer. Thus using PRACT output, a second cropping system selection step can be taken based on these cropping system characteristics, i.e., crop production and agroecological functions. By doing so the number of cropping systems selected can reach a reasonable value that can be handled by technicians and farmers. Possible uses and further development of the tool are discussed.     

Production, nitrogen and carbon balance of maize-based forage systems

Nitrogen (N) and carbon (C) surplus can be used as indicators of an agroecosystems’ ability to maintain soil fertility. Maize is the key crop of intensive forage systems in northern Italy, and large amounts of manure are often supplied to this crop. Different maize-based cropping systems and manure managements were compared in this paper. The following were assessed, using the results of an 11-year experiment: crop production and N uptakes; C and N surpluses; soil C and N contents. The treatments were maize for silage (Ms), maize for grain (Mg), double annual crop rotation maize–Italian ryegrass (Mr), and rotation maize–grass ley (Ml). Five fertilization management systems were adopted: 0N control, and bovine slurry and farmyard manure supplied at two levels, ranging from 215 to 385 kg ha⁻¹ of total N.

The dry-matter production of Mr was significantly higher than those of the other systems. The response of maize to fertilization was similar in all the cropping systems, except for Mr, for which the crop showed a high reactivity to N input at both fertilizer levels. Soil reserves were rapidly consumed in the unfertilized treatment of Mr, whereas the high productivity potential of this cropping system was exerted in fertilized plots. The introduction of a ley in rotation with maize reduced the system's DM production, due to the low yield potential of grass compared to that of maize, reduced the system response to fertilization, and diminished the exploitation of organic N at high fertilization rates. Cumulated N surplus caused an enrichment of the soil N pool size: 43% of excess N was retained by the soil. The relationship between the cumulated C surplus and the soil C pool size indicated that 26–27% was retained by the soil. Crop residues of the Mg system were less effective in building up the soil C pool than other C sources. Both slurry and farmyard manure exerted a positive effect on the soil C and N retention. When farmyard manure was used, 18% of C and 45% of surplus N were incorporated into the soil organic matter (SOM). Slurry also built up the SOM content, resulting in 9% of C and 24% of N surplus.     

Designing crop management systems by simulation

To help agricultural advisors to propose innovative crop management systems, simulation models can be a complementary tool to field experiments and prototyping. Crop management systems can be modelled either by using a vector representing dates and quantities used as input parameters in crop models or by developing specific decision models linked with biophysical models. The general design process of crop management systems by simulation follows a four-step loop (GSEC): (i) generation; (ii) simulation; (iii) evaluation; (iv) comparison and choice. The Generation step can follow different approaches: from blind generation before simulation to optimization procedures using artificial intelligence algorithms during the loop process. Simulation is mainly an engineering problem. Evaluation process means assigning a vector of indicators to the simulated crop management systems. A three-point evaluation can be carried out on the simulated crop management systems: global, agronomic and analytical. Comparison and choice of different simulated crop management systems raise the question of “monetary” versus “non-monetary” comparison and how to aggregate different quantities such as drainage, nitrogen fertilisers, labour, etc. Different examples are given to illustrate the GSEC loop on the basis of research programs conducted in France. Methodological advances and challenges are then discussed.     

覆盖作物及其作用的研究进展

在农作物种植系统中,田间杂草、土壤因素对作物的生长发育、产量和品质的影响一直都是农业领域关注的热点。大量使用化肥和除草剂可以达到作物增产、除草的目的,但其对土壤和环境造成的负面影响,严重制约了农业生产的可持续发展。种植覆盖作物是一种实现农业可持续发展的新策略,可以达到控制杂草、减少氮肥施用、改善土壤质量等目的。本文主要从覆盖作物的起源与发展过程、主要种类和作用及其种植制度等方面,总结了目前覆盖作物的研究进展及其在作物种植中的应用,以期为覆盖作物在我国农业生产中的研究与应用提供理论基础。     

A methodology for multi-objective cropping system design based on simulations. Application to weed management

Weeds are harmful for crop production but important for biodiversity. In order to design cropping systems that reconcile crop production and biodiversity, we need tools and methods to help farmers to deal with this issue. Here, we developed a novel method for multi-objective cropping system design aimed at scientists and technical institutes, combining a cropping system database, decision trees, the “virtual field” model FlorSys and indicators translating simulated weed floras into scores in terms of weed harmfulness (e.g. crop yield loss, weed-borne parasite risk, field infestation), weed-mediated biodiversity (e.g. food offer for bees) and herbicide use intensity. 255 existing cropping systems were simulated with FlorSys, individual indicator values were aggregated into a multi-performance score, and decision trees were built to identify combinations of management practices and probabilities for reaching performance goals. These trees are used to identify the characteristics of existing cropping systems that must be changed to achieve the chosen performance goals, depending on the user's risk strategy. Alternative systems are built and simulated with FlorSys to evaluate their multi-criteria performance. The method was applied to an existing oilseed rape/wheat/barley rotation with yearly mouldboard ploughing from Burgundy which was improved to reconcile weed harmfulness control, reduced herbicide use and biodiversity promotion, based on a risk-minimizing strategy. The best alternative replaced a herbicide entering plants via shoot tips (during emergence) and roots after barley sowing by a spring herbicide entering via leaves, introduced crop residue shredding before cereals and rolled the soil at sowing, which reduced the risk of unacceptable performance from 90% to 40%. When attempting to reconcile harmfulness control and reduced herbicide use, the best alternative changed the rotation to oilseed rape/wheat/spring pea/wheat, replaced one herbicide in oilseed rape by mechanical weeding, delayed tillage before rape and applied the PRE herbicide before oilseed rape closer to sowing. This option reduced the risk of unacceptable performance to 30%. None of the initial or alternative cropping systems succeeded in optimal performance, indicating that more diverse cropping systems with innovative management techniques and innovative combinations of techniques are needed to build the decision trees. This approach can be used in workshops with extension services and farmers in order to design cropping systems. Compared to expert-based design, it has the advantage to go beyond well-known options (e.g. plough before risky crops) to identify unconventional options, with a particular focus on interactions between cultural techniques.     

棉花生产管理模拟系统CPMSS/CGSM

棉花生产是一个复杂的大系统,为了使其系统输入输出最优化,以便达到高产、优质、低耗、高效益的目的,我们研制了适应于每公顷产1500公斤皮棉的棉花生产管理决策支持系统CPMSS和棉花生长发育动态模拟模型CGSM。目前,CPMSS/CGSM系统可以完成一熟春播棉田的播前决策、生长发育期间的调控决策以及生育后期的估产。     

西部黄土高原苜蓿终止时间对苜蓿-小麦轮作系统生产力及土壤水分的影响

旱作春小麦 (Triticum aestivum L.)是西部黄土高原最重要的禾谷类作物,该区苜蓿(Medicago sativa L.)分布也非常广泛。持续的作物连作和多年苜蓿种植系统都存在很多问题。雨养农业系统发展的关键是最佳水分利用策略的应用。发展合理的苜蓿-小麦轮作系统对该区农业的发展有十分重要的意义。由于苜蓿终止时间严重影响土壤水分,所以在适宜的时间终止苜蓿就显得十分重要。然而,关于苜蓿-小麦轮作中老苜蓿在一年中适宜终止时间的研究鲜见报道。本研究利用黄土高原西部典型的半干旱雨养农业区30年老苜蓿布设田间试验,旨在探索老苜蓿地土壤水分状况、苜蓿终止时间和少量氮肥施用对系统生产力及土壤水分的影响。结果表明,长期种植苜蓿后0~3 m土壤水分很少,即便遇到丰水年(2003年),3年的时间都不足以恢复土壤水分。30年苜蓿在一年中春季还是秋季终止对土壤水分状况无显著影响。种植苜蓿30年后杂草竞争力增强,苜蓿干物质和产量水平都相当低,且对1 kg hm^-2的氮肥使用无明显响应。由于土壤水分含量太低,后茬春小麦对1 kg hm^-2的氮肥使用和苜蓿终止时间也无明显响应。因此,苜蓿持续种植时间太长会耗竭土壤水分,使后茬春小麦对苜蓿在一年中的终止时间及少量的氮肥使用无响应,需要3年以上时间才有可能恢复土壤含水量。     

Winter barley performance under different cropping and tillage systems in semiarid Aragon (NE Spain)

Winter barley is the major crop on semiarid drylands in central Aragon (NE Spain). In this study we compared, under both continuous cropping (BC) (5–6-month fallow) and a crop–fallow rotation (BF) (16–18-month fallow), the effects of three fallow management treatments (conventional tillage, CT; reduced tillage, RT; no-tillage, NT) on the growth, yield and water use efficiency (WUE) of winter barley during three consecutive growing seasons in the 1999–2002 period. Daily precipitation measurements and monthly measurements of soil water storage to a depth of 0.7 m were used to calculate crop water use (ET) and its components. The average growing season precipitation was 195 mm. Above-ground dry matter (DM) and corresponding WUE were high in years with high effective rainfalls (>10 mm day⁻¹) either in autumn or spring. However, the highest values of WUE for grain yield were mainly produced by effective rainfalls during the time from stem elongation to harvest. Despite the similarity in ET for the three tillage treatments, NT provided the lowest DM production, corresponding to a higher soil water loss by evaporation and lower crop transpiration (T), indicated by the lowest T/ET ratio values found under this treatment. No clear differences in crop yield were observed among the tillage treatments in the study period. On average, and regardless of the type of tillage, BF provided the highest values of DM and WUE and yielded 49% more grain than BC. These differences between cropping systems increased when water-limiting conditions occurred in the early stages of crop growth, probably due to the additional soil water storage under BF at sowing. Although no significant differences in precipitation use efficiency (PUE) were observed between BC and BF, PUE was higher under the BC system, which yielded 34% more grain than the BF rotation when yields were adjusted to an annual basis including the length of the fallow. The crop yield under BF was not dependent on the increase in soil water storage at the end of the long fallow. In conclusion, this study has shown that, although conventional tillage can be substituted by reduced or no-tillage systems for fallow management in semiarid dryland cereal production areas in central Aragon, the practice of long-fallowing to increase the cereal crop yields is not longer sustainable.     

Factors of winter wheat yield robustness in France under unfavourable weather conditions

To face increasing uncertainties, future farming systems must be sustainable not only under average conditions but also in extreme climatic and economic situations. Various concepts such as stability, robustness, vulnerability or resilience have been proposed to analyze the ability of agricultural systems to adapt to changing production conditions. The operational effectiveness of these concepts remains nevertheless limited. In this paper, we developed an original analytical framework allowing characterizing and quantifying crop yield robustness, as well as identifying agricultural practices linked to cropping systems differentiated according to their robustness pattern. This framework was applied to 2300 bread wheat plots belonging to 145 cropping systems in various regions of France over the period 2011–2014. The analysis was performed at the scale of the cropping system. In a first step, we defined a regression statistical model allowing us to link wheat yield variability to an index of abiotic perturbations constructed using the STICS agronomic model; the cropping systems were taken into account through the use of dummy variables. In a second step, the different cropping systems were positioned within four quadrants using the regional average wheat yield in conditions of average abiotic perturbations and the regional average estimated robustness to abiotic perturbations as cut-offs for the quadrants. In a third step, the cropping systems of the different spaces defined by the four-quadrant approach were compared on the basis on three types of agronomic practices, i.e., management intensification, rotation and heterogeneity practices. Empirical results show that abiotic perturbations had an impact on wheat yield variability. This impact differed from one system to another which means that there is a ”cropping system effect” of abiotic perturbations on wheat yield robustness. Several agronomic practices allowed differentiating high versus low wheat yield cropping systems. High yield cropping systems relied more intensively on chemical inputs (fertilizers and pesticides) and used more diversified rotations, with more frequently legumes as preceding crops and a lower frequency of cereals. Fewer agronomic practices allowed differentiating robust versus sensitive wheat cropping systems. In addition to the sowing date (later for robust systems) and the sowing density (greater), these practices were essentially linked to spatial adjustments of the sowing date, total pesticide use, variety earliness at heading stage and variety disease resistance.