Ensuring food and nutrition security for all

Analysis of ‘food wedges’ indicates that even if food losses are avoided and food demand is reduced through changing diets, at least 46% of future food demand in 2050 must come from increased food production (Keating et al., Reference Keating, Herrero, Carberry, Gardner and Cole2014). The demand for food in the future will, no doubt, vary across regions, depending on projected population growth rates and improvements in economic well-being. For instance, the population of Africa is expected to grow exponentially until the end of the century whereas the population in Asia is expected to plateau in 2050 and decline thereafter (United Nations, 2019).

Satisfying the additional production needed to meet future food demand requires an understanding of the gap between the potential or water-limited yield and the actual farm yield currently achieved (van Ittersum et al., Reference van Ittersum, Cassman, Grassini, Wolf, Tittonell and Hochman2013). The potential (Y _p) and water-limited yields (Y _w) are ceilings that indicate the maximum yields that can be achieved under irrigated and rainfed conditions, respectively. Mapping these yield ceilings for the main crops and growing regions provides, in effect, a planetary boundary for the maximum amount of food that can be produced. There is increasing evidence that crop yields of smallholders in Africa reach only ca. 20% of Y _w or less (Tittonell and Giller, Reference Tittonell and Giller2013) while in Europe and North America actual yields approach 80% of Y _p or Y _w (Silva, Reference Silva2017; Schils et al., Reference Schils, Olesen, Kersebaum, Rijk, Oberforster, Kalyada, Khitrykau, Gobin, Kirchev, Manolova, Manolov, Trnka, Hlavinka, Palosuo, Peltonen-Sainio, Jauhiainen, Lorgeou, Marrou, Danalatos, Archontoulis, Fodor, Spink, Roggero, Bassu, Pulina, Seehusen, Uhlen, Zylowska, Nierobca, Kozyra, Silva, Maças, Coutinho, Ion, Takac, Mínguez, Eckersten, Levy, Herrera, Hiltbrunner, Kryvobok, Kryvoshein, Sylvester-Bradley, Kindred, Topp, Boogaard, de Groot, Lesschen, van Bussel, Wolf, Zijlstra, van Loon and van Ittersum2018; see also www.yieldgap.org). The causes of these yield gaps differ per region with the lack of inputs being a key determinant for smallholders in Africa (Sanchez, Reference Sanchez2002; Silva et al., Reference Silva, Baudron, Reidsma and Giller2019) and the timeliness and spatial distribution of the inputs applied being more important in Europe (Silva et al., Reference Silva, Reidsma and van Ittersum2017a).

Product quality, health and nutrition are also gaining increasing attention in the analysis of food systems (Brouwer et al., Reference Brouwer, McDermott and Ruben2020). This is important to broaden the food security debate beyond staple (cereal) crops only and to assess the role of nutritional diversity as a key component of agricultural systems. It also helps to contextualize health and nutrition with other macro-economic transformations such as rising incomes and a growing middle class.

Avoiding land use change and biodiversity loss

Closing yield gaps is essential to prevent future food demand being simply met through expansion of existing farmland (Foley et al., Reference Foley, Ramankutty, Brauman, Cassidy, Gerber, Johnston, Mueller, O'Connell, Ray, West, Balzer, Bennett, Carpenter, Hill, Monfreda, Polasky, Rockstrom, Sheehan, Siebert, Tilman and Zaks2011). Farmland expansion is undesirable given the associated biodiversity loss (Zabel et al., Reference Zabel, Delzeit, Schneider, Seppelt, Mauser and Vaclavik2019) and greenhouse gas emissions (van Loon et al., Reference van Loon, Hijbeek, ten Berge, De Sy, ten Broeke, Solomon and van Ittersum2019). In fact, the area of farmland under staple crops at the global level has expanded at the rate of 12.6 Mha/year during the period 2002–14, rates of change never seen before in human history (Cassman and Grassini, Reference Cassman and Grassini2020). More than half of the increase in farmland is attributed to increased areas of rice, maize, wheat and soybean replacing natural ecosystems in Africa, South America and Asia.

Area expansion has indeed been the key pathway to increase production of staple crops in Africa, while in Europe and Asia increases in cereal production occurred through yield gap closure (data not shown). Despite the large land resources considered to be suitable for agriculture in Africa (Chamberlin et al., Reference Chamberlin, Jayne and Headey2014), it is important to spare land for nature and avoid greenhouse gas emissions associated with land clearing. This is particularly true, given that most biodiversity is found outside of protected areas in production landscapes managed by people, where agricultural expansion represents a serious threat (Baudron and Giller, Reference Baudron and Giller2014). Land sparing and land sharing are both realistic options to increase agricultural production while minimizing negative consequences for biodiversity, but the preferred pathway largely depends on local circumstances.

Climate change adaptation and mitigation

Climate change is expected to put global food supply on a razor's edge due to its negative impact on crop yields (Rosenzweig et al., Reference Rosenzweig, Elliott, Deryng, Ruane, Muller, Arneth, Boote, Folberth, Glotter, Khabarov, Neumann, Piontek, Pugh, Schmid, Stehfest, Yang and Jones2014) coupled with a future reduction in the area of land suitable for farming in the tropics (Ramankutty et al., Reference Ramankutty, Foley, Norman and McSweeney2002). The steady increase of CO₂ and other greenhouse gases in the atmosphere over the last few decades is the main driver of the temperature rise and changing rainfall patterns and of the associated increases in climate variability and frequency of extreme weather events. Crop models have been widely used for climate change impact assessments and most evidence indicates rising temperatures are expected to negatively affect future cereal yields (Bassu et al., Reference Bassu, Brisson, Durand, Boote, Lizaso, Jones, Rosenzweig, Ruane, Adam, Baron, Basso, Biernath, Boogaard, Conijn, Corbeels, Deryng, De Sanctis, Gayler, Grassini, Hatfield, Hoek, Izaurralde, Jongschaap, Kemanian, Kersebaum, Kim, Kumar, Makowski, Müller, Nendel, Priesack, Pravia, Sau, Shcherbak, Tao, Teixeira, Timlin and Waha2014; Asseng et al., Reference Asseng, Ewert, Martre, Rötter, Lobell, Cammarano, Kimball, Ottman, Wall, White, Reynolds, Alderman, Prasad, Aggarwal, Anothai, Basso, Biernath, Challinor, Sanctis, Doltra, Fereres, Garcia-Vila, Gayler, Hoogenboom, Hunt, Izaurralde, Jabloun, Jones, Kersebaum, Koehler, Muller, Kumar, Nendel, OĹeary, Olesen, Palosuo, Priesack, Rezaei, Ruane, Semenov, Shcherbak, Stockle, Stratonovitch, Streck, Supit, Tao, Thorburn, Waha, Wang, Wallach, Wolf, Zhao and Zhu2015; Li et al., Reference Li, Hasegawa, Yin, Zhu, Boote, Adam, Bregaglio, Buis, Confalonieri, Fumoto, Gaydon, Marcaida, Nakagawa, Oriol, Ruane, Ruget, Singh, Singh, Tang, Tao, Wilkens, Yoshida, Zhang and Bouman2015) and that this can be offset by a CO₂ ‘fertilization effect’ to a certain extent (Long et al., Reference Long, Ainsworth, Leakey, Nösberger and Ort2006).

Experimental field research through the ‘FACE’ experiments (e.g. Long et al., Reference Long, Ainsworth, Leakey, Nösberger and Ort2006) has been of critical importance to understand the physiological responses of crops to changing temperatures and CO₂ concentrations. Given that experiments cannot reproduce the impacts of future climates with confidence, crop models are a key tool to understand and explore potential impacts of climate change on food production. To be credible tools, crop models must be based on solid physiological understanding of how crops respond to environmental signals – knowledge that can only be gained through detailed experimentation.

The keywords ‘climate change’, ‘adaptation’ and ‘impact assessment’ occurred 482, 383 and 204 times in the book of abstracts of the iCropM2020 (Fig. 1a). The aforementioned numbers were considerably larger than those for food security (n = 75), insurance (n = 67), climate variability (n = 38) or policy (n = 47). The data do indeed confirm the large number of crop model applications dealing with climate change adaptation and show an imbalance with other topics which would also benefit from the insights provided by crop models. Examples of the latter include exploratory studies mapping the suitability of a given region to introduce new crops (Nendel et al., Reference Nendel, Reckling, Stella, Berg-Mohnicke, Constantin, Debaeke, Jaksic, Klimek-Kopyra, Schoving, Webber and Battisti2020) or resource-use efficiency assessments at regional scale (Silva et al., Reference Silva, Tenreiro, Spätjens, Anten, van Ittersum and Reidsma2020). We also note that most crop model applications currently target cropping systems (n = 276) at the field scale (n = 405; Table 1), with findings being often directly extrapolated to the regional level. Extrapolations from field to region are simple and attractive but they do not account for explanatory factors at the farm level, the most important decision-making level. To overcome this limitation, spatially-explicit crop models have been incorporated with farming systems modelling to evaluate trade-offs in management options while considering heterogeneity of farming systems (Capalbo et al., Reference Capalbo, Antle and Seavert2017; Antle et al., Reference Antle, Homann-KeeTui, Descheemaeker, Masikati, Valdivia, Lipper, McCarthy, Zilberman, Asfaw and Branca2018; Descheemaeker et al., Reference Descheemaeker, Reidsma, Giller and Deryng2020).

Current agricultural systems need to adapt and contribute to mitigate future climate change, and farmers are the people making key management decisions that drive that process. As such, it is critical to contextualize adaptation and mitigation options at a ‘systems level’ and within the broader livelihoods of farmers (e.g. Descheemaeker et al., Reference Descheemaeker, Reidsma, Giller and Deryng2020). Our keyword search indicated a relatively high frequency of words associated with the term ‘farm scale’ (n = 314), but we note that more than two-thirds of these records refer to words such as ‘farmer(s)’ or ‘farming’ (data not shown), while the term ‘farming system’ received considerably less attention (n = 18, Table 1). Case studies in the Netherlands and Zimbabwe clearly indicate the need for farm and farming system level analysis (Descheemaeker et al., Reference Descheemaeker, Reidsma, Giller and Deryng2020). The findings indicate that large farms are more likely to benefit from climate-smart agriculture technologies than small farms, and price variability or poor soil fertility are as important as climate change in explaining future farm performance (Descheemaeker et al., Reference Descheemaeker, Reidsma, Giller and Deryng2020).

Diverging paradigms for the future of agriculture

The fourth grand challenge for agricultural scientists is to arrive at a consensus on what should be produced where, and how (Foran et al., Reference Foran, Butler, Williams, Wanjura, Hall, Carter and Carberry2014). Different paradigms have been proposed to address these issues, all with their pros and cons (Wezel et al., Reference Wezel, Casagrande, Celette, Vian, Ferrer and Peigne2014). We are confronted by a plethora of approaches and definitions – ecological intensification, sustainable intensification, agroecology, agroecological intensification, organic agriculture, conservation agriculture, circular agriculture, regenerative agriculture – each with its own protagonists and flag-bearers. ‘Alternative’ approaches are often juxtaposed against an undefined ‘conventional’ or industrial agriculture claiming the high ground. We argue that although this polarization may help in scoring political points it helps little in addressing the problems facing humanity – dogma has no place in agronomy nor science at large (Giller et al., Reference Giller, Andersson, Sumberg, Thompson and Sumberg2017).

Diverging paradigms remain a contentious point and put considerable pressure on the prioritization of the research agenda needed to address the grand challenges introduced earlier, for which there is a general consensus. A solid, prioritized research agenda for the agricultural sector is needed to ensure that scarce research funds are used effectively and to avoid that humanity is pushed towards short-term solutions in the medium- and long-term that may prove unsustainable.

Crop models to generate and test hypotheses

Approaches to crop modelling build upon three fundamental concepts: system, model and simulation (de Wit, Reference de Wit and Leffelaar1993). A ‘system’ is defined as a limited part of reality that contains interrelated elements, and the totality of the relations within a system is known as ‘system structure’. A ‘model’ is a simplified representation of a system, and both models and systems have a structure. Explanatory models are of particular interest as they build upon different levels of organization and knowledge. Explanatory models may be defined as dynamic or static, depending on whether or not they represent systems that change with time. Simulation models are a genre of dynamic models which consider changes in states and rates over time, whereas optimization models or statistical models are examples of static models. Finally, ‘simulation’ refers to the building of mathematical models and the study of model behaviour in reference to that of the system it represents.

Models can only be used to solve practical problems once they have been tested for their usefulness and once their errors and uncertainties have been quantified. Disagreements between model outputs and reality are to be expected as the conceptualization of the studied system and the development of a model to represent it involves simplifications and assumptions (de Wit, Reference de Wit and Leffelaar1993). Such disagreements and model failures are the starting point for model improvement. Models are essentially complex hypotheses, and model testing and improvement involves the identification of the explanatory processes in the model responsible for an unacceptable representation of reality, and their modification. Experiments with both the model and the system are crucial in this regard as a means to generate new information that can be used to test and improve elements of the model. Model development and improvement are thus a continuous cycle of simulation and experimentation as new questions and hypotheses are generated and tested (see Rötter et al., Reference Rötter, Appiah, Fichtler, Kersebaum, Trnka and Hoffmann2018, for an example on weather extremes).

‘No simulation without experimentation’ is a mantra often attributed to C.T. de Wit. This proposition lacks a formal reference, but it is known to have been used by C.T. de Wit with ‘characteristic conviction’ (van Keulen, Reference van Keulen2008). An extended version was later reformulated by Leffelaar (Reference Leffelaar1987) as ‘no simulation without experimentation’ and ‘no experimentation without simulation’. Rötter et al. (Reference Rötter, Appiah, Fichtler, Kersebaum, Trnka and Hoffmann2018) recast the proposition as ‘no modelling without experimentation and no experimentation without modelling’; yet ‘simulation’ is a much broader concept than ‘modelling’ alone (de Wit, Reference de Wit and Leffelaar1993). During the iCropM2020, the propositions ‘if you don't understand it you can't model it, if you don't model it you can't understand it’ (Hammer, Reference Hammer2020) and ‘we learn most when the models don't work’ (Giller, Reference Giller2020) were also proposed to highlight the role of crop models to generate and test hypotheses (Loomis et al., Reference Loomis, Rabbinge and Ng1979).

C.T. de Wit's proposition remains highly relevant for the agricultural research community to remind us that simulation is as important as experimentation and that these two should go hand-in-hand at the core of a research cycle. Yet, this appears not to be reflected in the book of abstracts of the iCropM2020 (Figs 1b and 1c). Firstly, the number of records with the keyword ‘simulation’ (n = 1143) far outweighs those with ‘field trials’ (n = 93) or ‘field experiments’ (n = 303, Fig. 1b). Secondly, the number of records with terms like ‘model parametrization’ (n = 469) or ‘model calibration’ (n = 439) also far outweigh those with ‘model evaluation’ (n = 284), ‘model validation’ (n = 124) or ‘model improvement’ (n = 18, Fig. 1c). Thirdly, ‘model prediction’ (n = 251) is more common than ‘model exploration’ (n = 12, Fig. 1c). Field studies are expensive, laborious and unable to test multiple genotype × environment × management interactions (G × E × M), which crop models can do in a very cost-effective way. Yet, despite the aforementioned limitations, experiments provide the bedrock on which models are developed and remain crucial to re-calibrate models or test model improvements.

‘Forgotten’ crops, growth factors and traits

Many crop models have been developed specifically for cereal crops and most modelling exercises devoted their attention to cereal crops. This is justified given the large area share of cereals and their importance as a staple food in most regions. The iCropM2020 was no exception (Fig. 1d). Crops such as ‘wheat’ (n = 498), ‘maize’ (n = 379) and ‘rice’ (n = 121) featured much more prominently than ‘potato’ (n = 46), ‘banana’ (n = 19) or ‘cassava’ (n = 16, Fig. 1d). Highland banana, root and tuber crops are important staples for smallholders in Africa (Tittonell and Giller, Reference Tittonell and Giller2013) which should not be neglected in food security assessments. This remains challenging because the crop models available for root and tuber crops lack proper field testing (Raymundo et al., Reference Raymundo, Asseng, Cammarano and Quiroz2014).

Tropical perennials such as coffee and cocoa are important cash crops that also have received remarkably little attention when compared with recent advances in experimentation and modelling of the major cereals (Rozendaal et al., Reference Rozendaal, Slingerland, Zuidema, Giller and Anten2020). Predicted impacts of climate change on future suitability regions for production of coffee (Ovalle-Rivera et al., Reference Ovalle-Rivera, Laderach, Bunn, Obersteiner and Schroth2015) and cocoa (Schroth et al., Reference Schroth, Laderach, Martinez-Valle, Bunn and Jassogne2016) were initially based on agroecological characterization of current production areas and superimposing these on maps of projected future climates. Such approaches are highly relevant in the absence of robust crop models, and in the case of both crops indicated major geographic shifts, and reductions in land suitability due to climate change. Indeed, major reductions in yields of Arabica coffee have already been observed in East Africa due to rising minimum temperatures resulting from climate heating (Craparo et al., Reference Craparo, van Asten, Laderach, Jassogne and Grab2015). Development of a coffee crop model allows the interacting effects of drought, rising temperatures and CO₂ fertilization to be taken into account (Rahn et al., Reference Rahn, Vaast, Laderach, van Asten, Jassogne and Ghazoul2018), which should give greater confidence in projections of climate change impacts. Recent experiments in Brazil indicate that yield-enhancing effects of CO₂ fertilization may dampen and compensate for the negative impacts of rising temperatures and drought on coffee yield (DaMatta et al., Reference DaMatta, Rahn, Laderach, Ghini and Ramalho2019). Current research in Wageningen University is focusing on improving an existing cocoa simulation model (Zuidema et al., Reference Zuidema, Leffelaar, Gerritsma, Mommer and Anten2005) for use in climate change research.

The threats of climate change on future yields and production areas of coffee and cocoa are very real and of commercial interest to producers, traders and processors of these commodities. Such companies also have considerable experience and observations on which research can build, and which seem to defy our current physiological understanding of these crops. Research under controlled conditions suggests that the flowers of Arabica coffee are sterile at temperatures above 33°C (Drinnan and Menzel, Reference Drinnan and Menzel1995), yet it is currently grown with irrigation in Bahia, Brazil, producing excellent yields in areas where maximum temperatures exceed 35°C during flowering, with peaks observed up to 39°C (Piet van Asten, personal communication, 2020). Similarly, cocoa produces very high yields (ca. 2 kg/tree/year)Footnote ¹ with irrigation in Andhra Pradesh, southern India, where temperatures often exceed 46°C and sometimes even reach 50°C (Nicholas Cryer, personal communication, 2020). These are conditions where, based on current physiological understanding of coffee and cocoa, we would predict the crops should not be grown. Such observations in farmers' fields provide a perfect basis for collaboration between public institutions and the private sector to extend our knowledge of the climate responses of these crops. Such cooperation, if properly established to ensure open sharing of data and results, will be key to understanding options for adaptation to climate change.

Concepts of production ecology are useful to identify the relative contribution of growth-defining, -limiting and -reducing factors to actual yields (van Ittersum and Rabbinge, Reference van Ittersum and Rabbinge1997). Growth-defining factors are essential to simulate the potential yield and their effects on crop production are captured well in crop models. The importance of defining factors was also reflected in the iCropM2020 as indicated by the number of records for ‘radiation’ (n = 84), ‘temperature’ (n = 284), ‘variety’ (n = 446) and ‘sowing’ (n = 172, Table 1). Growth-limiting factors are required to simulate attainable yields and crop models can simulate the effects of water and nitrogen limitation on crop production fairly well. Limiting factors received by far the most attention in the contributions to the iCropM2020, with terms associated with ‘water’ and ‘nutrients’ recorded 1226 and 406 times in the book of abstracts, respectively (Table 1). Growth-defining and -limiting factors (especially water) are the focus of most climate change impact assessments (Fig. 1a). By contrast, most crop models are unable to handle growth-reducing factors, the pests, diseases and weeds that are responsible for the gap between attainable and actual yields (Donatelli et al., Reference Donatelli, Magarey, Bregaglio, Willocquet, Whish and Savary2017). This was also noticeable in the iCropM2020 with relatively few records for terms such as ‘pests’ (n = 77), ‘diseases’ (n = 63) and ‘weeds’ (n = 90, Table 1). Regarding the traits investigated, by far the most focus goes to crop yield (n = 1787) followed by aboveground-biomass (n = 398), leaf dynamics (n = 383) and crop phenology (n = 328, Fig. 1e). Partitioning coefficients received almost no attention (n = 33) despite requiring re-calibration for recent varieties (e.g. ten Den et al., Reference ten Den, van de Wiel, de Wit, van Evert, van Ittersum and Reidsma2020).

The capacity of crop models to simulate improved management practices increases with their capacity to simulate the effects of growth-limiting and -reducing factors on crop growth (cf. Tittonell and Giller, Reference Tittonell and Giller2013). Although most crop models are able to simulate water- and nitrogen-limited yields under different management practices (e.g. amount, time and efficiency of application), little progress has been made in simulating phosphorus and potassium limitations and the interactions between these factors. An exception is found for cassava in West Africa, an example where simulation and experimentation were combined. The amounts of nitrogen, phosphorus and potassium required to achieve the potential yield of cassava (32 t DM/ha) were calculated using the QUEFTS model (Ezui, Reference Ezui2017). These nutrient requirements were used to design field experiments that were in turn used to parametrize, evaluate and improve the performance of the crop model LINTUL in simulating cassava yields (Adiele et al., Reference Adiele, van den Beuken, Ezui, Pypers, Ano, Egesi and Giller2021). When balanced nutrient requirements were provided, yields of 35 t DM/ha were achieved, the largest yields ever recorded in West Africa to the best of our knowledge. Indeed, the yield response to potassium was still linear at 300 kg K/ha suggesting that potential yields of cassava have been underestimated (Adiele et al., Reference Adiele, Schut, van den Beuken, Ezui, Pypers, Ano, Egesi and Giller2020). Despite these efforts, interactions between potassium and water stress remain poorly understood. This is a clear example of model improvement where simulation was combined with experimentation and vice-versa with an application to a crop often neglected in food security assessments.

Hierarchical levels and farm(ing) systems research

Agricultural systems involve a large number of components and interactions among these components. It is thus useful to partition agricultural systems into nested levels to better understand their complexity (Ewert et al., Reference Ewert, van Ittersum, Heckelei, Therond, Bezlepkina and Andersen2011). For such an analysis, it is assumed that each nested level shares the same time and spatial dimension so that components in a given level are nested into the components above them. Interactions between the different system components occur between hierarchical levels (e.g. between field and farm or farm and region) and between components in each level (e.g. multiple fields within a farm). Navigating through the different hierarchical levels requires updating space and time dimensions and involves different degrees of complexity.

Another key feature of agricultural systems is the importance of biophysical and socio-economic dimensions (van Ittersum et al., Reference van Ittersum, Ewert, Heckelei, Wery, Olsson, Andersen, Bezlepkina, Brouwer, Donatelli, Flichman, Olsson, Rizzoli, van der Wal, Wien and Wolf2008). In the case of a farm, the biophysical dimension plays a role at the field level where water and nutrients are used for production and may leak to the environment while the socio-economic dimension controls the decision-making process on how resources are allocated across the farm (i.e. crop and farm management). The latter includes strategic, tactical or operational decisions (de Koeijer et al., Reference de Koeijer, Wossink, Smit, Janssens, Renkema and Struik2003), which are subjected to farmers' objectives and resource constraints due to limited availability of land, labour and capital. Up-scaling between different nested levels requires information transfers within each dimension and between different dimensions (Ewert et al., Reference Ewert, van Ittersum, Heckelei, Therond, Bezlepkina and Andersen2011). For instance, up-scaling from the field to the regional level requires up-scaling the biophysical processes dealing with crop growth at the field level and the socio-economic aspects controlling the decision-making process at the farm level. Missing the latter is not advisable as it implies missing the context where farmers operate.

Crop models are the engines most commonly used to up-scale biophysical processes in agricultural systems and thus provide the building blocks for integrated assessments at multiple scales. This was true in earlier ex-ante policy assessments at the farm and regional levels in Europe where crop models were embedded within the multi-layered and multi-dimensional approaches described above (van Ittersum et al., Reference van Ittersum, Ewert, Heckelei, Wery, Olsson, Andersen, Bezlepkina, Brouwer, Donatelli, Flichman, Olsson, Rizzoli, van der Wal, Wien and Wolf2008). The same applies to earlier efforts on modelling the effects of farmer decision-making under resource constraints in sub-Saharan Africa (van Wijk et al., Reference van Wijk, Tittonell, Rufino, Herrero, Pacini, de Ridder and Giller2009), where a summary crop model was deployed to simulate attainable yields (Tittonell et al., Reference Tittonell, Corbeels, van Wijk and Giller2010). More recently, crop models have been combined with breeding and experimentation to explore whole-farm benefits of an early sowing system with slower-developing wheat cultivars in Australia (Hunt et al., Reference Hunt, Lilley, Trevaskis, Flohr, Peake, Fletcher, Zwart, Gobbett and Kirkegaard2019). Rather than simply assuming the full yield benefits of the adapted crop management could be implemented across the whole area of wheat production, as it is often done, the authors took into account the time of sowing required across the huge farms which cover several thousand hectares. By including these operational limitations, the overall yield advantage across the farm could be calculated, taking yield advantage of early-sown slow-developing cultivars and the larger area of fast-maturing cultivars sown in a timely fashion. These adaptation strategies increased wheat yields by 0.54 t/ha, equivalent to 7.1 Mt annually, at national scale and are the result of a close collaboration between farmers, breeders, field agronomists and crop modellers over more than a decade of research: ‘An example of ‘systems agronomy’ at its best’ (Giller and Ewert, Reference Giller and Ewert2019).

The philosophy underlying the use of crop models to understand farm level problems, as illustrated in the three examples above for contrasting farm(ing) systems, is not present in most climate change impact assessments presented in the iCropM2020 (Table 1 and Fig. 1c). Can crop models help us understand the farm level implications of adapting to, and contributing to mitigate, the negative impacts climate change? And how can these models be contextualized in concrete farm(ing) systems applications?

Core disciplines and emerging technologies

Most crop model applications are within the realm of agronomy and crop improvement, followed by plant biology and physiology (Fig. 1f). These were indeed the disciplines within which models were developed and have proven to be useful research tools (Jones et al., Reference Jones, Antle, Basso, Boote, Conant, Foster, Godfray, Herrero, Howitt, Janssen, Keating, Munoz-Carpena, Porter, Rosenzweig and Wheeler2017). Combining crop models with technologies such as remote sensing, machine learning and opportunities of big data for model parametrization were also reported during the iCropM2020 (Fig. 1f). Such applications often require new types of models or the re-packaging of old models in new programming languages that facilitate their integration with new types, and large amounts, of data (e.g. de Wit et al., Reference de Wit, Boogaard, Fumagalli, Janssen, Knapen, van Kraalingen, Supit, van der Wijngaart and van Diepen2019). Emerging technologies will contribute to increase the efficiency and accuracy of crop models in the future to design sustainable agricultural systems at farm and landscape scales (Basso and Antle, Reference Basso and Antle2020).

New sources and large amounts of data are becoming available for crop modelling, allowing for the application of crop models at greater spatial and temporal resolutions. For example, combining spatial data from remote-sensing imagery with crop models allows for better estimates of crop yield, water use and N uptake across large scales (Huang et al., Reference Huang, Gomez-Dans, Huang, Ma, Wu, Lewis, Liang, Chen, Xue, Wu, Zhao, Wang and Xie2019), which can be helpful for planning logistics and analysing markets of crops across large farms or regions. Machine learning has already proved helpful to calibrate crop models for specific genotypes based on large amounts of phenotyping data (Chapman et al., Reference Chapman, Potgieter, George-Jaegli, Hammer, Jordan, Wei Yang and Tuinstra2020). At another, finer scale, detailed models of plant components dealing with, for example, root morphology in relation to nutrient uptake and of leaves to gas exchange and photosynthesis can play a major role in understanding plant plasticity in relation to environmental changes. The same is true for functional-structural plant models (Vos et al., Reference Vos, Evers, Buck-Sorlin, Andrieu, Chelle and de Visser2009) which further allow studying resource competition between crops or devising management options for operations that manipulate plant structure such as pruning (Tosto et al., Reference Tosto, Zuidema, Anten and Evers2020). Crop modelling is also now being used in CGIAR research programmes to inform and accelerate breeding programmes (Ramirez-Villegas et al., Reference Ramirez-Villegas, Milan, Alexandrov, Asseng, Challinor, Crossa, van Eeuwijk, Ghanem, Grenier, Heinemann, Wang, Juliana, Kehel, Kholova, Koo, Pequeno, Quiroz, Rebolledo, Sukumaran, Vadez, White and Reynolds2020) and to analyse socio-economic factors to improve policy recommendations (Kruseman et al., Reference Kruseman, Bairagi, Komarek, Molero Milan, Nedumaran, Petsakos, Prager and Yigezu2020).