Mamassi et al. Agriculture & Food Security (2023) 12:22 Agriculture & Food Security https://doi.org/10.1186/s40066-023-00428-2 REVIEW Open Access Modeling genotype × environment × management interactions for a sustainable intensification under rainfed wheat cropping system in Morocco Achraf Mamassi1,2* , Riad Balaghi3, Krishna Prasad Devkota4, Hamza Bouras5, Mohamed El‑Gharous1 and Bernard Tychon2 Abstract Under the conditions of Moroccan rainfed agricultural areas, wheat cropping systems—the population’s basic staple food—are subject to a set of limitations that seasonally impact crop production and farmers’ incomes, thus national food security. In the last decades, the major constraints were often related to the country’s Mediterranean‑type climate, through the intense recurrence of drought events and high inter‑ and intra‑annual rainfall fluctuations. Similarly, various forms of soil degradation inhibit the potential of this slowly renewable resource to support wheat crop intensification and ensure livelihoods. However, the limitations sometimes surpass the environmental factors to implicate the inappropriate crop management strategies applied by farmers. In Moroccan rainfed areas, production problems linked to crop management practices result principally from a shortage in the provision of knowledge to Moroccan small farmers, or their indigent economic situation that limits farmers’ capacity to adopt, qualitatively and quantitatively, efficient strategies. Advanced technologies (remote sensing or crop modeling) play key roles in assess‑ ing wheat cropping systems in Moroccan rainfed areas. Due to the difficulties of using conventional experience‑based agronomic research to understand Genotype × Environment × Management (G × E × M) interactions, the substantial benefits of crop modeling approaches present a better alternative to provide insights. They allow the provision of simpler, rapid, less expensive, deep, and potentially more accurate predictive knowledge and understanding of the status of cropping systems. In the present study, we highlight the constraints that surround wheat cropping systems in Moroccan rainfed conditions. We emphasize the efficiency of applying crop modelling to analyze and improve wheat cropping systems through three main themes: (i) preserving food security, (ii) supporting general adaptation strategies to face climate change effects and extreme events, and (iii) recommending within‑season and on‑farm crop management advice. Under Moroccan context, crop modeling works have mainly contributed to increase under‑ standing and address the climate change effects on wheat productivity. Likewise, these modeling efforts have played a crucial role in assessing crop management strategies and providing recommendations for general agricultural adaptations specific to Moroccan rainfed wheat. *Correspondence: Achraf Mamassi achraf.mamassi@uliege.be Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Mamassi et al. Agriculture & Food Security (2023) 12:22 Page 2 of 23 Keywords Wheat, Rainfed system, Intensification, Crop modeling, Morocco Introduction Middle East and North Africa [157]. Rainfed croplands Between 720 and 811 million people worldwide are are a future alternative arena for decision-makers and food insecure [47], and 194.6  million pre-school chil- researchers to ensure food security by creating chances dren (under 5 years) are malnourished [144]. The world’s to boost productivity and crop intensification due to the future challenges are to ensure that there is enough food current reported low yield levels and their variability [15]. and to generate adequate income to better feed the poor According to the Fifth Assessment Report from the and hungry people, and thus reduce the number of those IPCC, the average combined land and ocean surface suffering food insecurity [116]. The anticipated growth in temperature has increased globally by 0.85 °C during the the global population, which was projected to reach 8 bil- period 1880 to 2012 [66]. Due to more frequent extreme lion in November 2022, will make this challenge more weather conditions, such as droughts and floods, crop difficult [143], putting even greater pressure on global production has been increasingly impacted, especially in food security, especially in developing countries that have rainfed environments, where crop production and irriga- witnessed a population increase of more than 80% since tion water depend highly on rainfall [53, 122]. Drought 2000. Agriculture plays a key role in economic develop- is the consequence of a “deficiency of precipitation over ment, poverty reduction, and economic growth. Every an extended period of time resulting in water scar- 1% increase in agricultural yield translates to a 0.6–1.2% city”; therefore, rainfed croplands are more vulnerable decrease in the percentage of absolute poor [153]. In to drought in semi-arid and arid areas [67]. In addition, sub-Saharan Africa, for example, agriculture accounts there is a cause-and-effect relationship between drought for more than 35% of the gross domestic product (GDP) and land degradation, which are the primary drivers of many countries (Mali, Niger, Chad, Liberia, etc.) and of poverty in rural areas. [114, 153]. The soil serves as a employs 53% of the population [157] (https:// data. world buffer in rainfed agriculture, storing water during a short bank. org/ indic ator/). drought period and making it available to plants. This Rainfed agriculture plays a crucial role in global food highlights a narrow window of opportunity for increas- production as 85% of agricultural lands are rainfed [113, ing crop productivity and sustainability through effective 116] (Table 1). About 60% of the food needs of the world’s strategies for managing soil and water in rainfed areas population are met by produce from rainfed croplands, [15, 122]. The rainfed agricultural systems in the Medi- and this agriculture employs approximately 60% of the terranean are among the most significant cases of rainfed population, which plays an important role in reducing agriculture [122]. Most of the Mediterranean region falls poverty [15, 114]. While the importance of rainfed agri- within the arid and semi-arid rainfall zones [37]. The agri- culture varies by region, it shares the substantial service culture in this area is regarded as the most water-stressed of providing the majority of food for poor communities in the world with significant inter- and intra-annual vari- in developing countries. Almost all agricultural lands in ation in rainfall distribution, typically concentrated in the sub-Saharan Africa are rain-fed, compared to 70% in the autumn and winter seasons, and dry and hot springs and summers [2]. Table 1 Land area and population distribution according to hydro‑climatic zones and land‑use Rainfed agricultural production systems Region Area (% of total Population (% in Morocco: importance and constraints world land area) of total world Due to the geographic configuration of Morocco, agri- population) culture areas are confined within the borders of the Hydroclimate Arid 23 7.2 mountains and the seas and are highly influenced by cli- Semi‑arid 18 16 matic factors, mainly rainfall [9]. The area of Morocco Dry sub‑humid 9 13 is 710,850  km2, most of which is in an arid to semi-arid Total 50 36 climate (200 to 400  mm). Water supply in Morocco Cropland use Rainfed agricul‑ 11 28 is entirely dependent on precipitation, unlike coun- ture tries of the Middle East and Eastern and Central Africa. Irrigated agricul‑ 2.1 19 Moroccan agriculture is highly dependent on rainfall, ture with rainfed croplands accounting for 81% of the uti- Total 13.1 47 lized agricultural area (UAA), or 7  million  hectares [36, (Data source: Rosegrant et al. [116]; World Bank, [157] 88]. Consequently, crop productivity heavily depends M amassi et al. Agriculture & Food Security (2023) 12:22 Page 3 of 23 on the amount and distribution of rainfall. Any rainfall Table 2 Agro‑climates of Morocco [9] deficiency has an immediate detrimental effect on the Agro-climate Rainfall (mm) % Surface Cereal nation’s water supplies, agriculture, and the economy of production the country. The frequency of dry agricultural seasons (%) has increased fivefold in Morocco, going from one dry Favorable > 400 30 31.1 year out of 15 normal years during the 30  s, 40  s, 50  s, Intermediary 300 to 400 24 16.8 60 s, and 70 s, to one dry year out of three during the last Unfavorable east 200 to 300 12 25.5 two decades (Fig. 1, Balaghi, unpublished). Depending on Unfavorable south 200 to 300 12 9.8 their nature and varied intensity, these droughts have had Mountain 400 to1000 15 10 substantial effects on agriculture and on the economy of Pre‑Saharan and Oasis < 200 7 4.1 the nation. Moreover, Morocco is recognized as a “hot- spot” for anticipated climate change scenarios, and is predicted to have 20% reduced rainfall as well as a tem- perature increase of 2 °C by 2050 [65, 123]. Consequently, production increases are plagued by recurring drought Moroccan rainfed agriculture needs to be given more and stymied by low inputs of fertilizers and machinery, attention because of its greater vulnerability to climate as well as low control of diseases and crop pests. Conse- change compared to irrigated agriculture. quently, cereal yields are still low and stagnant in Moroc- Agriculture plays a vital role in Morocco’s economy can rainfed areas [93]. (12–14% of GDP between 2008 and 2018 and 38% of The implementation of improved varieties and effec- employment in 2018), and any temporal or seasonal tive agronomic management strategies has considerable variation of the climate will immediately affect agricul- potential to reduce the huge cereal yield gap in rain- tural production, particularly for crops that provide the fed areas in Morocco [9, 103, 153], and thus sustainably foundation of the food supply [88]. The most important enhance cereal productivity and approach the potential food resource is cereals, and wheat is the most commonly yield for those climatic conditions [125, 149]. produced cereal in the nation [9, 93]. National wheat production has improved over the years. However, this improvement was insufficient to cover the fast-growing Wheat crop: development processes and abiotic population’s needs. Cereal imports have been consistent stresses since 1980, by representing nearly 48.7% of the national Wheat growth and development processes annual produced amount, and most of the imported food Plant development is the sum of events, whereby tis- products and import costs [9]. sues, organs, and the whole plant are produced [135]. It The Moroccan Ministry of Agriculture has divided the implies three main processes: growth (the effect of cell rainfed croplands into six agro-ecological zones accord- division and enlargement on cell size and plant organs), ing to their production potential: Favorable, Intermedi- morphogenesis (the acquisition of form and structure), ate, Unfavorable South, Unfavorable East, Mountainous, and differentiation (plant cells differentiate to perform and Pre-Saharan and Oasis areas (Table  2). Average specialized functions) [3, 30]. The interactions of the cereal yields have increased from 0.5 to 1.5 t.ha−1 in the environment, the plant genotype, and crop manage- last 20 years. The coefficient of variation in cereal yields ment practices (i.e., G × E × M interactions) determine over this period has been around 40%. As expected, most how the plant develops [86, 126, 155]. Overall, the term of the yield variation is in the less favorable growing “development” refers to all the changes that a plant expe- areas. For example, the coefficient of variation of cereal riences, from seed germination to senescence. The net yields is over 70% in the pre-Saharan and oasis zone CO2 assimilation (i.e. through photosynthesis) at the and around 40–50% in the mountainous and unfavora- tissue level constitutes the basis for plant growth. Pho- ble southern regions. In contrast, yield variation is only tosynthesis processes are affected by different factors about 24% in the favorable region [130]. Overall, cereal that depend on the plant development phase as well as Fig. 1 History of drought seasons in Morocco Mamassi et al. Agriculture & Food Security (2023) 12:22 Page 4 of 23 on environmental characteristics: sunlight and radiation, growth and productivity to fall below optimal levels. water, nutrients, temperature, and CO2 [1]. As a result, abiotic stresses that affect plant growth Wheat growth and development processes are com- and development include environmental factors, such plex. During the life cycle of wheat plants, many of the as heat or cold stress, drought stress, light intensity, development stages overlap, and while one part of the salinity, and nutrient insufficiency [3, 17]. The main plant develops, another part dies [100, 127]. Organ differ- abiotic stress threats to plant development and yield entiation during the cycle defines the wheat development potentials in the Mediterranean-type environment are stages [1, 127]. The following development stages for heat and water deficiency events, notably for grains [4, wheat can be identified based on physiological traits: ger- 161]. Overall, from germination to flowering, the vari- mination, emergence, tillering, stem elongation, booting, ous types of stress have a significant negative impact heading, anthesis, grain filling, and maturity. The dura- on plant growth [3]. In the next section, the effects of tion of each development stage depends on the G × E × M abiotic factors on different periods of wheat growth and interactions: genotype (species, cultivars), environment development are described. (temperature, day length, water, etc.), and management practices (sowing date, fertilization, etc.) [1, 100, 126, a. Focus on water 155]. The most commonly used scale to define cereal growth stages, including wheat, is Zadoks Growth Stage A water deficit occurs when water absorption by the Key [162]: the development of the cereal plant is divided crop is lower than water evapotranspiration, which into 10 general development phases covering 100 indi- reduces the plant water availability and affects the nor- vidual growth stages. Individual growth stages are indi- mal functioning of the plant–soil system [50]. There- cated by the prefix Z (Fig. 2). fore, three processes determine the soil water status: (i) the amount of applied and available water (rainfall Wheat development under abiotic stresses amounts in the case of rainfed areas), (ii) the water For optimal crop growth and development, crops need absorption level by the crop, which is related to the adequate levels of moisture (i.e., accessible water), tem- crop characteristics (species and genotype) and the soil perature, nutrients, and CO2, with variations in crop physical proprieties, and (iii) the evapotranspiration interactions according to the progression of pheno- process that depends on the atmospheric properties logical growth phases [17, 64]. Abiotic stress is defined (temperature, radiation, vapor pressure, etc.), on crop by Cramer et  al. [29] as the reversible and irreversible characteristics (Kc, Kr, stomatal conductance, etc.) and impacts of environmental variables that cause crop on soil physical characteristics. Fig. 2 Zadoks Growth Stage Key of a wheat plant. Modified from Simmons et al. [127] M amassi et al. Agriculture & Food Security (2023) 12:22 Page 5 of 23 Water stress strongly affects several aspects of plant Table 3 Effect of water stress on wheat leaf area index (LAI), growth and development: morphology, physiology, bio- yield components, and water use efficiency (WUE) at various chemistry, and crop productivity [69]. For cereals, a growth stages [55] positive correlation exists between evapotranspiration Parameter Timing of water stress and grain yield [1]. When a wheat plant experiences drought stress, various negative reactions are generated Control Pre-anthesis Anthesis Grain filling depending on which growth stage the plant is in [147]. LAI at booting 5.00 3.30 5.00 5.00 Consequently, it is necessary to investigate wheat plant Fertile tillers.m−2 513 658 434 435 behavior under water stress at various developmental Grains/spike 32.7 13 27.1 31.4 stages: 1000 grain weight (g) 56.3 55.2 53.7 49.2 Grain yield (g.m−2) 779 559 498 658 • Germination and emergence period: plant stability Harvest index 0.52 0.50 0.53 0.53 depends on drought resistance during this period. WUE (kg grain/ha 16.8 14.6 12.4 15.2 Water availability is a determining factor that pre- mmET) vents seed germination [100]. Early drought indices during the growing season affect wheat germina- tion and crop establishment, influencing final ger- b. Focus on temperature mination rates [147]. Water stress during the ger- mination period leads to a 12% decrease in grain Temperature is a substantial meteorological factor con- yield [110]. Furthermore, water deficiency affects sidered in plant development processes. The computation the emergence phase: hard soil, due to low mois- of grain development rates and the illustration of transi- ture, inhibits the coleoptile vigor to grow and per- tions between development stages, which is typically forate the surface, especially for wheat varieties expressed in terms of accumulated growth degree-days with short coleoptile characteristics [100]. (GDD), revealed the significance of temperature variables • Vegetative growth period: water deficit during the (maximum, minimum, and mean temperatures). In addi- crucial development phases, including the veg- tion, the plant responds negatively to excessive changes etative growth period, leads to a significant loss of in temperature, i.e., extreme events (heat or cold), which wheat grain yield [79, 98]. Low soil moisture com- influence the cereal dry matter accumulation rates and bined with heat stress is unfavorable during wheat yield production. Porter and Gawith [108], reviewed the vegetative growth due to their negative impact on effects of climate variability and extreme temperature transpiration and photosynthesis processes. Water events that occurred during the different wheat growth stress slows photosynthesis and leaf area expansion, and development periods: reducing dry matter production. It also limits root growth, thus reducing nutrient uptake [16, 62]. • Germination and emergence period: the seed germi- • Reproductive and grain development period: dur- nation rate is dependent on temperature. The wheat ing this period, wheat is highly sensitive to envi- genotype determines the response to temperature ronmental stresses, particularly nitrogen and water regimes during germination, and each variety has a [160]. The occurrence of water stress and high maximum seed germination and vigor index under temperatures during a critical period of 2–3 weeks a specific temperature, with a required mean of before anthesis can significantly impact the floret 35 degree-days for visible germination to occur [25]. production [55], reducing the number of grains per In addition, the temperature affects wheat emergence spikelet, thus affecting the final yield [100]. Wheat’s and establishment, in which high or low tempera- booting and heading are important reproductive tures perturb the emergence of the coleoptile and stages and are the most sensitive to drought, as heat cause seedling mortality [100]. and water stresses could lead to a drop of 30–90% • Vegetative growth period: wheat vegetative growth, in wheat grain yield [98, 124]. Similarly, the occur- specifically the tillering phase, is sensitive to heat rence of water stress during wheat grain filling stress. An obvious decrease in wheat growth state stages influences various yield components, mainly variables (LAI, plant height, number of tillers, etc.) is grain weight (Table  3) [55]. Moreover, wheat crop observed when plants are exposed to extreme tem- exposure to water stress during this period has a peratures during the vegetative period [1, 59]. Under significant effect on the quality of wheat grain (i.e., the Mediterranean-type climate, temperature acts starch and protein contents) [129]. negatively in two different ways during the wheat vegetative period: i) heat stress impacts the plant Mamassi et al. Agriculture & Food Security (2023) 12:22 Page 6 of 23 water availability through intensifying the evapora- during the vegetative period reduces plant growth, tion process, and ii) high temperatures accelerate the decreasing the tiller number and leads to yellowing of plant development stages by the accumulation of the leaves [163]. However, the early detection of N stress growing degree-days (GDD), without allowing the and the delivery of essential N recovery dosages may plant to achieve the potential growth rates in each reverse the effects on wheat development and pro- independent stage (e.g., tillering and stem elonga- ductivity. On the other hand, while the P-deficiency tion). symptoms could be detected mainly from perturba- • Reproductive and grain development period: due tions of the root system development, it also appears to the direct impact on grain number and dry mat- as dark green spots in leaves, as well as regression of ter accumulation, wheat reproductive and grain fill- plant growth, reduced tiller emergence, and late plant ing stages are the development periods most affected maturity [41, 121, 163]. Potassium (K) also has a sig- by high temperatures. The main mechanisms of heat nificant effect during the plant’s vegetative period, stress include tissue dryness, pollen sterility during specifically on plant height and the tiller number [8, floret development, decreased C O2 assimilation, and 100]. higher photorespiration. These effects decrease pho- • Reproductive and grain development period: the sup- tosynthesis and lower grain yield. High temperatures ply of N-fertilizer between stem elongation and the influence different components of grain yield: reduc- heading period amounts to 60% of the total N uptake ing the grain number per spike, grain dry weight, and and has a significant effect on increasing the number grain protein content [49, 108]. of spikelets and grains per ear, the grain protein con- tent and grain weight, and causes an increase of 40% in grain yield [78, 100, 120]. In addition, an obvious c. Focus on macro-nutrients evolution is seen in different components of grain yield and quality with appropriate P-fertilization [95]. Optimal crop nutrition is necessary for improved plant K-application influences the grain yield and qual- growth and development processes, high-yield produc- ity, resulting in an increase in dry matter and grain tion, and acceptable grain quality. Plant nutrient stress, weight, also allowing the evolution of grain quality specifically deficiency of the primary nutrients (nitrogen by increasing the amount of zinc, iron, and protein in (N), phosphorus (P), and potassium (K)), is one of the the grain [8]. major abiotic stresses that affect cereal growth and grain productivity potential, especially when rainfall amounts are adequate (i.e. in favorable rainfed areas). Moreover, Wheat genotype and yield potential the risks of plant nutrient imbalances are manifested not Yield potential is the maximum yield that a crop cultivar only in a negative impact on crop growth and yield pat- can achieve when cultivated in an ideal physical environ- terns but also affect the notable role of those nutrients ment free of biotic and abiotic stress [46]. The evolution in the activation of several plant mechanisms to mitigate of potential yield in a specific environment depends on other biotic and abiotic stresses [52, 76]. the enhancement of adaptive wheat genotypes (varie- ties) through breeding and aims to improve: (i) specific • Germination and emergence period: wheat is very adapted plant growth and development characteristics, sensitive to insufficient nitrogen (N) and very respon- (ii) grain yield components and quality, and (iii) resist- sive to nitrogen fertilization at sowing. Nitrogen has ance to biotic and abiotic stresses. a significant impact on wheat vigor after sowing, In Morocco, the development of wheat varieties has helping to increase the final germination rates [154]. traditionally been seen as a fast way to increase yields Phosphorus (P) is an essential element in seed ger- to meet the country’s rising production shortfall. The mination and early root development. P-deficiency Moroccan National Institute of Agronomic Research at this early development stage significantly reduces (INRA), in partnership with the International Maize and wheat growth potential later. In additon, P and K Wheat Improvement Center (CIMMYT), created various fertilizer has to be applied close to the seed during wheat genotypes that were well-adapted to the various sowing and cannot be top-dressed due to the relative agro-climates of Morocco in the early 1980s. However, immobility of these nutrients in the soil (as opposed these genotypes were sensitive to some pests, specifically to N) [100]. Septoria fungus and Hessian fly. These wheat genotypes • Vegetative growth period: during this period, scien- were exploited later in a breeding and varietal selec- tists report the most critical wheat crop physiological tion program. They were considered during the follow- responses to N- and P-deficiencies. Overall, N stress ing breeding works: resistance to brown rust in 1980, to M amassi et al. Agriculture & Food Security (2023) 12:22 Page 7 of 23 Septoria in 1987, to Hessian fly in 1989, and to heat and for organic waste decomposition. Because soil is a drought stress in 1992 and 1995. Table 4 presents a list of slowly renewing resource, its loss of quality has a long- important soft wheat (Triticum aestivum) varieties devel- term impact on numerous soil processes. oped between 1980 and 2010 in Morocco. The soil may reduce environmental swings and man- age various biological processes that maintain water Soil: a vital slowly renewable resource and air quality and ensure plant development by inter- for long‑term agricultural production systems acting intimately with water, air, and plants. Therefore, Soil quality and degradation soil quality is defined as the ability of a specific type Soil, as one of the most important natural resources, of soil to function, maintain, or improve the quality of plays a vital role in the production of human food, the water and air and support human health and housing preservation of terrestrial ecosystems, the provision of [83]. The terms “health” and “quality” of soils are fre- an environment for plant growth, the storage of water quently used to define the same concept [128]. and chemical elements, and the biological background Table 4 List of soft wheat varieties released between 1980 and 2010 [68] (Data source: http:// wheat atlas. org/ count ry/ varie ties/ MAR/0? AspxA utoDe tectC ookie Suppo rt=1) Name Years Selector Description MARCHOUCH 1984 Jliben, Bouchoutrouch and Ouassou (INRA, CIMMYT) • Favorable RA and semi‑arid • Sensitive to Hessian fly SAIS 1985 Jliben, Bouchoutrouch and Ouassou (INRA, CIMMYT) • Favorable RA and semi‑arid • Sensitive to Septoria and Hessian fly KANZ 1987 Jliben, Bouchoutrouch and Ouassou (INRA, CIMMYT) • Favorable RA and semi‑arid • Sensitive to Septoria and Hessian fly SABA 1987 Jliben, Bouchoutrouch and Ouassou (INRA, CIMMYT) • Favorable RA • Sensitive to Septoria and Hessian fly ACHTAR 1988 Mergoum and Smith (INRA, CIMMYT) • Favorable RA and irrigated cropland • Favorable response to nutrients KHAIR 1988 Mergoum and Smith (INRA, CIMMYT) • Favorable RA and irrigated cropland • Highly sensitive to Septoria and Hessian fly BARAKA 1988 Mergoum and Smith (INRA, CIMMYT) • Favorable RA and semi‑arid • Sensitive to Septoria and Hessian fly TILILA 1989 Jliben, Mergoum and Smith (INRA, CIMMYT) • Favorable RA, irrigated cropland, semi‑arid and mountain • Sensitive to Septoria and Hessian fly MASSIRA 1992 Jliben (INRA) • Favorable RA and irrigated cropland AMAL 1993 Jliben (INRA) • Sub‑humid regions and irrigated cropland • Highly productive RAJAE 1993 Jliben (INRA) • Sub‑humid regions • Favorable response and valorization of nutrients MEHDIA 1993 Jliben (INRA) • Favorable RA and irrigated cropland • Sensitive to Septoria and Hessian fly ARREHANE 1997 Jliben and abdella (INRA) • All RA and irrigated cropland • Sensitive to Septoria • High resistance to Hessian fly • late sowing AGUILAL 1997 Jliben and Amri (INRA) • Favorable RA and semi‑arid • Resistant to Hessian fly WAFIA 2005 Florimond Desprez • Favorable RA and irrigated cropland • Resistant to brown rust and Septoria RADIA 2006 Florimond Desprez • All RA and irrigated cropland • Resistant to brown rust and Septoria BANDERA 2010 Florimond Desprez • Favorable RA and irrigated cropland • Resistant to brown rust and Septoria FAIZA 2010 Florimond Desprez • Favorable RA and irrigated cropland • Resistant to brown rust and Septoria RA rainfed areas Mamassi et al. Agriculture & Food Security (2023) 12:22 Page 8 of 23 Because soil health is still an intrinsic aspect of the Nutrient depletion and yield reduction notion of sustainable agriculture, soil may initially be Several decades of research into the cause–effect rela- deemed in poor health if it is not naturally capable of tionship between crop production and nutrient avail- supporting intensive agriculture [111]. The most practi- ability have revealed that, even in the driest parts of the cal definitions of soil quality are those that are related world, nutrients are among the most limiting factors to their functions. Agronomists often employ a defini- for crop growth, with the macro-elements (N, P, and K) tion that focuses on soil production, i.e., soil in “good being the primary limiting nutrients [31]. Worldwide, it health” produces abundant crops of high quality. was estimated that more than 50% of the increase in crop Agriculture has been perceived differently in the last yields during the twentieth century was due to the adop- 10  years. It is no longer regarded as a closed-circuit tion of chemical fertilizers [77, 85. As a result, without activity, but rather as a component of a much broader adequate replacement of nutrients extracted in agricul- ecological system that interacts with other components tural products, as well as nutrient losses due to soil ero- of the system. This has led to a new definition of soil sion and leaching of chemical or natural fertilizers [150], quality that exceeds productivity and links with the soil nutrients decrease—resulting in poorer crop yields, environment. The National Research Council of Can- as proven in long-term tests [148]. ada (NRC) also recognized the importance of includ- Soil nutrient depletion refers to soil nutrient losses ing environmental perspectives in soil quality. The NRC through natural and human-induced processes (Fig.  3). ruled in 1993 that “[s]oil quality is the ability of a soil to In other words, it is the process by which the soil nutri- promote plant growth, protect watersheds by regulat- ent stock is shrinking because of continuous nutrient ing seepage and dividing precipitation, and preventing mining in the absence of replenishment of the required water and water pollution by cushioning potential pol- nutrients. It can be attributed to the following factors: lutants such as agricultural or industrial chemicals or agriculture intensification, lack or insufficient replenish- organic waste” [145]. ment of nutrients, accelerated soil erosion, inappropri- On the other hand, land degradation refers to weak- ate land uses, poor management practices, unbalanced ening soil quality and capacity through natural per- fertilization, etc. (Fig. 3). Soil nutrient depletion is closely turbations (e.g. extreme climatic events) or human connected to food insecurity in emerging and least- activities [101, 156]. Likewise, soil degradation refers to developed nations due to the expansion of land usage a decline in the soil’s current or potential performance for agriculture without sufficient application of external to ensure livelihoods and the provision of other ecosys- nutrients [54, 90]. Inadequate replenishment of nutrient- tem goods and services, notably, food production [77]. depleted soils exacerbates soil degradation and has an In Morocco, as in other developing countries, the impact on agricultural sustainability and food security. combination of poverty and population growth in fragile environments results in the degradation of non-renewable or slowly renewable resources, particu- larly forests, soils, and water. Overexploitation of soils through increasingly intensive crop rotations, unsus- tainable soil cultivation, and export of crop residues from farmed and grazed fields all contribute to carbon loss and aggregate instability [119]. Three main forms of soil degradation require atten- tion [19, 77]: • Biological degradation: loss of organic matter and reduction in the activity of microorganisms and species diversity; • Physical degradation: soil erosion and sedimenta- tion, decline in soil structure, crusting and compac- tion; • Chemical degradation and nutrient depletion: preva- lent nutrient depletion and salinization of agricul- tural soils are primary causes of decreasing yields, Fig. 3 Soil nutrient depletion and its impacts on soil quality, crop low on-site water productivity, and off-site water pol- production, and the environment. Modified from Deckelbaum et al. lution. [35] M amassi et al. Agriculture & Food Security (2023) 12:22 Page 9 of 23 Studies on soil degradation and soil fertility assessments conducted in different countries play a major role in improving crop production and optimizing fertilizer use efficiency, maximizing the farmers’ revenues, specifically those with low incomes. For cereal crops, the main efforts should be focused on identifying nutrient constraints in the field, mainly nitrogen and phosphorus. Nitrogen and phosphorus fertilization Nitrogen (N) is a critical element in worldwide food cropping systems due to its essential function as a cereal yield-determining nutrient. N-fertilizer inputs ensure Fig. 4 World land area affected by nutrient depletion (Data source food for half of the world’s population [45, 80] through from Tan et al. [140] the reduction of staple crop yield gaps, specifically, where yields are less limited by water availability [122]. Prior to the 1960s, crop N-intake was mostly dependent on Table 5 Average level of NPK balance 1993–1995 [117] manure inputs, biological N-fixation, and indigenous N-supply through mineralization of soil organic mat- High (≥ 60) Medium (− 60 to − 30) Moderate/low (≤ 30) ter [131]. Afterward, N-fertilizer consumption increased Kg of N–P–K ha−1  year−1 worldwide. Since the 1990s, N consumption has Burkina Faso Cape Verde Algeria decreased in developed countries, whereas the increase Burundi Congo Egypt continues for developing countries (Fig.  5). This new Cameroon Chad Morocco trend in N-fertilizer consumption was primarily caused Kenya Sudan Tunisia by developing countries’ high population growth rates Nigeria Niger South Africa in comparison with developed countries, as well as an Senegal increase in nitrogen use efficiency in developed countries Ivory Coast due to the adoption of new technologies and insights into crop management practices [14]. Many studies in the lit- erature have highlighted the influence of N-fertilizers on global food security [75, 80, 131]. Low-input and inappropriate fertilization are a threat to Phosphorus is a significant nutrient for plant growth food security in many regions of the world [140], both and production and has a limiting effect at different directly by lowering crop yields and crop nutritional val- stages of wheat crop development (see Sect.  Wheat ues and indirectly by lowering resource use efficiency (i.e. development under abiotic stresses: focus on macro- land, water, fertilizer, etc.) and farmers’ marginal rev- nutrients). The agricultural practice of applying P-fertiliz- enues [77]. These effects are intensified under the climate ers has existed since the middle of the nineteenth century change context and the recurrence of extreme events (e.g. and developed in the twentieth century to exceed the drought), which exacerbates food insecurity. Overall, amount of phosphorus applied as manure. Likewise, the continuous nutrient depletion perturbs socio-economic consumption of P-fertilizers in developing countries has security and degrades soil resource sustainability and increased from less than 1 million tons of P per year (MT environmental quality. P.year−1) during the 1960s to 13 MT of P.year−1 in 2010 Around 135 million hectares of soil were considered [118]. In parallel to this evolution, studies have stated a to be prone to nutrient depletion worldwide at the turn two to three-times increase in P-uptake in crops [28, of the twentieth century, with 97% occurring in devel- 136]. Most of the phosphate rock mined (around 80%) oping and least-developed countries. In Africa, over- is used in fertilizer production [136]. Syers et  al. [139] cultivation and insufficient replacement of nutrients and reported critical challenges to the improvement of P-fer- management techniques have impacted about 45 million tilizer use efficiency, and they justified its importance by hectares of soil (Fig. 4). Furthermore, nutrient depletion two substantial reasons: (i) phosphate rock as the origin rates in Africa demonstrate a clear negative balance for of phosphorus fertilizer is a non-renewable resource, and the majority of countries [117] (Table 5). (ii) under a non-limiting N supply in terrestrial systems, In conclusion, adopting appropriate crop manage- P is usually the limiting nutrient for biomass develop- ment practices, including adapted fertilizer strategies, ment. As a result, enhancing the P status of many soils will contribute to a substantial crop yield improvement. Mamassi et al. Agriculture & Food Security (2023) 12:22 Page 10 of 23 Fig. 5 Developing and developed countries’ total nitrogen (N) and phosphate (P) fertilizer consumption (by million tons per year) from 1960 to 2020. [63] (data source: https:// www. ifast at. org/ datab ases/ plant‑ nutri tion) worldwide is critical to maintain optimal crop develop- There is an apparent high interest in newly developed ment and production, hence ensuring global food secu- strategies and tools that aim to optimally formulate N- rity [28]. and P-fertilization for farmers by applying adequate N In Morocco, N and P fertilizer recommendations are and P amounts at appropriate times during the grow- formulated to relevant farmers by agronomists and soil ing seasons. Furthermore, using agro-meteorology to experts using two main conventional approaches: (i) enhance fertilization recommendations in rainfed areas based on the average crop yield response to fertilization might be a valuable tool for adjusting fertilizing methods data collected in the long-term over large geographic to climate change and reducing economic and food inse- areas, or (ii) based on simple statistical equations (linear curity concerns in Mediterranean environments. or multiple regression equations) relating the amount of applied fertilizer to soil fertility proprieties, yield, and Advances in modeling of wheat cropping system crop type. In conventional N- and P-fertilizer timing management advice for cereal crops, while P application is automati- Introduction cally added at sowing (P applied as deep fertilizer), N Cropping systems are unstable ecosystems, and their application is prescribed before sowing, with three con- establishment and management are surrounded by ventional applications: at sowing as deep fertilizer, at uncertainty and gaps [89]. The complex interac- tillering stage (Z20 to Z25), and the start of the heading tions of agricultural systems, determined previously stage (Z50) as N-recovery fertilizer. Originally, two fac- through the G × E × M factors, impact crop growth, tors explained those fertilizer application dates: (i) the development, and productivity. Studies of the causal farmers’ usual calendar and (ii) the market availability of relationships between crop management and real N-fertilizer, i.e., when appropriate amounts of conven- crop production functions (i.e., measurements and tional N-recovery fertilizer are available. observations) are conventionally conducted through The conventional N- and P-fertilizer recommendations experience-based agronomic research [40]. However, are designed to achieve a target yield defined before sow- since the need for a more complex understanding of ing and possibly based on actual soil conditions, average crop responses in field trials and environments has climate of the location, and on weather conditions of the increased, such traditional approaches have shown current growing season. Thus, there is a large probabil- many limitations [74, 102]. First, this type of research ity of applying excessive or too-low amounts of N- and study provides information and results that not only P-fertilizer in several farmers’ fields, specifically in rain- are site-specific, and therefore, their re-exploitation fed croplands. This situation impacts not only crop depends on the environmental conditions of new sites, production and fertilizer use efficiencies but also the but also limited in time (season-specific or decade-spe- farmers’ incomes and increases the risk of environmental cific) due to the severity of climate change effects [56]. degradation through exaggerating greenhouse-gas emis- Second, demand is rising for extensive data sets and sions and groundwater pollution. M amassi et al. Agriculture & Food Security (2023) 12:22 Page 11 of 23 databases to ensure reasonable and accurate guidance The two types of models are described individually in of sensitive decision-making and policy processes, such the following sections, and the pertinent uses of crop as the food security issue. Finally, these trials are inten- modeling to improve crop management strategies are sively labor-, time-, and cost-consuming. Thus, a major investigated. difficulty is presented in conducting experiments across multiple years and sites during the season, as well as monitoring a large number of plant–soil state variables Type of models [27]. Over time, it became clear that farmers, specialists, a. Empirical models and decision-makers urgently require tools and advisory systems for crop scenario assessments [74]. Worldwide, Also called “statistical” or “descriptive” models. Unlike the newly discovered tools had to provide more simple, mechanistic models, empirical models focus on describ- rapid, less expensive, and deeper research findings for an ing and interpreting data, with a few assumptions speci- alternative exploration of the plant–soil–climate interac- fied to develop a knowledge-based model from the data tion effects on the environment and crop productivity. set [6, 102]. Sample data of the studied population are Mathematics may now be used to represent complicated used to build empirical models [11]. Thus, the accuracy biological processes due to technological breakthroughs of an empirical model is highly dependent on the qual- [102]. The modeling process uses sets of equations to ity of the sample data, the equations and parameters used create representations of the behavior of real systems in the model. For modelers, the preferred characteristics (whether they are complicated or simple), and then uses of empirical models are represented in their simple struc- those representations to replicate the behavior of those ture with limited requirements for input parameters and systems [71, 97]. To combine the key processes involved reduced time to run the models [104]. Therefore, statis- in the understanding of crop growth and development, tical models were the first empirical models to be used, quantitative methodologies were created in crop mod- specifically on a large scale [138]. eling studies, taking into consideration the interconnec- Statistical crop models are often based on machine tions of several disciplines (plant physiology, agronomy, learning techniques or simple or multiple regression soil science, agro-meteorology, etc.) [94, 107]. Agricul- models. Statistical crop models are often used in practice tural modeling has enabled crop research studies for vari- to explain the variation of a dependent variable, mostly ous aims to be undertaken, imitated, and pre-evaluated crop yield, using a collection of predictors (independent in a few minutes of computer effort [70, 133, 134], such as variables), most commonly represented by meteorologi- for crop yield prediction, climate change, crop responses cal parameters, satellite indices, soil, and crop manage- to different environmental and management factors, etc. ment factors [84]. Consequently, those types of models However, it has been explicitly stated that the use of tra- are mainly used for crop yield forecasting and yield gap ditional field experiments in tandem with crop modeling studies. studies is essential for calibrating and validating the mod- el’s effectiveness [115, 152]. b. Mechanistic models To simulate crop growth, development, and produc- tion, two main crop modeling techniques have been pro- Mechanistic models, also known as biophysical, crop posed [73, 109]: (i) empirical models and (ii) mechanistic simulation, or process-based models, are computerized models. Empirical models are the first models used pri- representations of crop growth, development, and pro- marily for crop yield modeling; they are calibrated using ductivity that use biophysical equations to provide an historical data, and their structure requires few parame- understanding of the biological, chemical, and physical ters. These models focus on data interactions and require processes that interact with the soil–plant–atmosphere less direct information from the plant [84]. Mechanis- continuum [58, 107]. There has been a lot of literature tic models, on the other hand, are more complex and published on mechanistic models since the 1980s. Devel- include a core framework of equations that reflect the opments in computer science, as well as the increasing physical and biological interactions of crop–soil–atmos- complexity of the challenges faced in agricultural sys- phere systems to mechanistically mimic crop growth and tems, have resulted in the development of more compli- development [5]. cated models [12]. Previous reviews have reported the creation and evolution of mechanistic crop models [71, Mamassi et al. Agriculture & Food Security (2023) 12:22 Page 12 of 23 106], while Wallach et al. [151] provided further data on zation and calibration are sometimes used inter- crop model methodologies and applications. changeably. These models compute the plant growth rates based • Validation: the model’s ability to model crop on information about crop management and environ- growth, development, and productivity outputs mental factors (soil, weather), and they predict the bio- against independent measured or observed data mass output from resources, such as carbon dioxide, sets is the ultimate test of the calibrated model’s water, solar radiation, and nutrients that are captured correctness (data sets of new experiments, loca- [7]. Hence, three types of process-based crop models tions, or years). This step is referred to as verify- were distinguished by Steduto [132]: (i) crop models ing the model’s truthfulness following the cali- based on carbon uptake through the photosynthetic bration and parameterization processes. The process (De Wit school of models), an approach initially validation process involves comparing independent developed by de Wit et  al. [33, 34] which includes the field measurements to model results [96]. “WOFOST” model (“the WOrld FOod STudies”) [18, 26, 39, 141], (ii) water-driven models based on the pro- portional linear relation of biomass accumulation rates c. Model sensitivity and uncertainty to transpiration, through a water productivity param- eter, included in this group are the two crop models Input parameters, calibration coefficients, and model “CropSyst” [137] and “AquaCrop” [133, 134]; and (iii) structure (or equations) are all prone to variation or radiation-driven models that quantify the accumulation uncertainty. A quantitative examination of the uncer- of biomass from intercepted solar radiation through a tainty and variability of a model’s various parts is known unique conversion coefficient called Radiation Use Effi- as an uncertainty analysis, and it enables the determina- ciency (RUE) [91, 141], and the main examples of these tion of a range of uncertainty for each output variable models are APSIM [57] and DSSAT–CERES [112]. as opposed to a single misleading estimate [152]. Sensi- Mechanistic models have been developed for a wide tivity analysis, on the other hand, is used to assess how range of crop species for many applications connected sensitive the output of a crop model is to model compo- to the world’s urgent problems, such as food security, nents that are unknown or variable. These studies help climate change, crop management adaptations, and researchers to understand the model’s behavior during so on. The more complex the models, the better they crop growth, development, and yield simulations [72]. will explain the soil–plant–climate systems, with a high demand for input parameters. Mechanistic mod- Major insights on crop modeling applications for food els need a three-step approach to ensure better model security and crop management adaptations design before applying such models to various goals: a. Crop growth monitoring and yield forecasting • Parameterization: this process is a higher level adjustment of specific model parameters than Sustainable crop intensification and effective natu- calibration [48]. It refers to supplying the model ral resource use are major challenges for ensuring food with local and time-specific input parameters that security. Crop forecasting models are key instruments were directly measured or recorded (climate input for farmers, agronomists, and policymakers addressing parameters, soil physical and chemical characteris- food security concerns in the context of climate change. tics, management information, etc.). Model forecasting has been developed to anticipate crop • Calibration: while some model parameters, such yield as early as possible, based on a conceptual logical as crop cultivars and soil coefficients, cannot be relationship between crop yield and within-season exter- directly measured or have higher levels of uncer- nal factors (environmental conditions and management tainty, iterative modification or calibration is highly techniques), internal factors (crop genotype), or environ- recommended. Calibration is the process of adjust- mental indices (ex: NDVI). ing particular model coefficients (parameters), so Empirical or mechanistic models are two of the most that the model simulates output, primarily crop used yield prediction techniques in literature; both are growth and productivity outputs, in agreement integrated with agricultural yield forecasting models. with the observed values in a given environment. Pre-harvest opportunities (pre-harvest crop produc- In case of discrepancies, the relevant parameters tion forecasts) are provided by global or national sys- are revised within acceptable boundaries, and the tems of crop growth monitoring and yield forecasting procedure is repeated until the model accuracy is to plan for any potential shortages in crop production. acceptable. In the literature, the terms parameteri- In other words, it enables the planning of preventative M amassi et al. Agriculture & Food Security (2023) 12:22 Page 13 of 23 interventions (such as farmer assistance and cereal (for parameterization of mechanistic models), whereas imports) by estimating the consequences of severe events CGMS-Maroc does not consider those variables. on crop output, hence reducing climate risk sensitivity. Aside from the Moroccan system (CGMS-Maroc), Morocco has experienced a significant decrease in additional recent investigations modeling agricultural terms of water availability (from 1016  m3 per capita in production predictions for Moroccan rainfed areas have 2000 to 799  m3 per capita in 2019) [157] because of the been conducted using other methodologies and under increasing pressure on water demand and rainfall fluc- varied climate conditions. Epule et al. [44] cited some of tuations caused by climate change, which has had a nega- those research studies in their literature review and gave tive impact on the agriculture sector and, consequently, insights on the following modeling approaches, model on the country’s food security [9], [158]). Water scar- structure, required input data set, and their perfor- city, inter- and intra-annual variance in rainfall, and the mances in estimating crop yield. recurrence of drought episodes all contribute as abiotic stressors and to disruptions in agricultural production, b. Between broad adaptations and within-season crop particularly for wheat crops in Moroccan rainfed regions management advice, the transition from crop model- (See Sect. “Introduction”). Hence, in the framework of an ling to crop management institutional consortium between the National Institute of Agronomic Research (INRA-Morocco), Agronomic Agricultural modeling studies have made a significant and Veterinary Institute Hassan II (IAV Hassan II), the contribution to food security worldwide by evaluating National Weather Service (DMN), and the Strategy the productivity of national or global crop systems (see and Statistical Service (DSS) of the Ministry of Agricul- Sect.  “Nitrogen and phosphorus fertilization”), as well ture, and using a combined agrometeorological mode- as local assessments of various crop management tech- ling approach, a national system for cereal crop growth niques in current and future climatic conditions. By sug- monitoring and agrometeorological prediction of yields gesting optimal and sustainable management techniques was developed: The Crop Growth Monitoring System and assisting adjustments to the variations in market of Morocco “CGMS-Maroc” [9, 32]. CGMS-Maroc is inputs, model-assisted decision-making in the local agri- a national system that allows in-season monitoring of cultural sector might reduce farmers’ vulnerability to cli- cereal crops and agrometeorological prediction of cereal matic and economic hazards. yields using climatic variables (ex: temperatures and rain- Crop simulation models (mechanistic models) play fall) and remote sensing vegetation indices (ex: NDVI). important roles as decision support systems because of In Table 6, we detail the main characteristics of CGMS- their complex structure that integrates plant–soil–cli- Maroc. Moreover, we compared the Moroccan system mate system interactions. They help to identify the best with well-known agricultural monitoring and yield fore- adaptation strategies for farmers to combat and control casting systems currently in operation: (i) China’s global the impact of climate fluctuations and global warming crop-monitoring system (CropWatch) [159], (ii) Fore- on crop growth and production [94, 107]. As a result, casting Agricultural output using Space, Agro-meteoro- by modeling crop responses to various management logical and Land-based observations (FASAL) [105], and techniques for local environmental circumstances, such (iii) the Crop Explorer service of The Foreign Agricultural models may provide a better understanding of the inter- Service of the United State Department of Agriculture connections between the key systems of the soil–plant– (USDA–FSA) [146]. The comparison of the systems’ yield climate continuum and its worldwide functioning at a prediction performances is not considered due to differ- daily time step. ences in climatic parameters, temporal and geographical Using empirical models for this purpose, on the other scales, and crop species importance for the countries’ hand, is based on linking crop biomass or yield (depend- food security (staple foods). Instead, the comparison was ent variable) to agronomic and environmental param- centered on the systems’ key components, which may eters. Through regressions and correlation analyses, provide insight when considering adaptations to the sys- the statistical technique facilitates a straightforward tems’ structure for possible improvement of robustness. qualitative understanding of the relationships between When CGMS-Maroc was compared to the other three biomass or grain production measurements and envi- systems, two significant differences were identified. First, ronmental factors (see Sect.  “Nutrient depletion and CGMS-Maroc is based on empirical models and machine yield reduction”a). As a result, it might be challenging to learning algorithms, whereas other systems use empirical draw agronomic conclusions from a model of this kind and mechanistic models. Second, the other systems inte- that, on one hand, ignores the biological, physical, and grate soil data sets and crop management information chemical processes of the system, and crop function- ing under management approaches on the other. Using Mamassi et al. Agriculture & Food Security (2023) 12:22 Page 14 of 23 Table 6 Comparison between four crop yield forecasting systems: CGMS‑Maroc, CropWatch, USDA–FAS, and FASAL systems CGMS-Maroc CropWatch USDA-FAS (Crop explorer) FASAL References [9] [159] [146] [105] Modeling approach Empirical models (multiple Empirical models (multiple Empirical and mechanistic models regression, machine learning and regression and similarity analysis) similarity analysis) Crop Cereal crops Cereal crops and soybean Cereal crops, soybean, cotton, etc Cereal crops, cotton, sugarcane, rapeseed, mustard, etc Mission ‑ Yield forecasting ‑ Monitoring agro‑climatic indica‑ ‑ Monitoring crop, soil, and Monitoring crop growth and ‑ Monitoring crop growth and tors, climate indicators, development indicators, climate indicators ‑ Crop mapping ‑ Crop mapping, ‑ Crop mapping, ‑ Yield forecasting, ‑ Yield forecasting, ‑ Yield forecasting, ‑ Analysis of phyto‑sanitary ‑Drought risk assessment ‑Water balance conditions Yield predictors Meteorological input parameters ‑Gridded data sets from ground Satellite‑based: temperatures, rainfall, photosynthetically active radia‑ ‑Station‑based: temperatures and stations: temperatures, rainfall, tion, and land surface temperature rainfall evapo‑transpiration + Snow coverage in Crop Explorer ‑Satellite‑based: insolation and land surface temperature Other remote sensing products Vegetation indices (NDVI, LAI, Vegetation indices (NDVI, LAI etc.) + Soil moisture FAPAR, FCOVER), rainfall estimates Auxiliary parameters Land cover map, crop calendar Systems use some common auxiliary parameters: crop type (or land cover maps), crop calendar, crop development scales, soil information, DEM, etc Systems’ scales ‑ Administrative level, ‑ Administrative level, ‑ Administrative level, ‑ National level ‑ National level, ‑ National level ‑ Global level Schedule to release system findings Every 10 days, during the growing Quarterly (every 4 months) and Monthly and numerous param‑ Depends on crop species. Overall, season annually eter maps and charts updated in early season, mid‑season, and every 10 days pre‑harvest Area covered by Morocco ‑ Global for Agro‑climatology, Global India the system ‑ Crop production and yield estimation for 31 countries FPAR fraction of photosynthetically active radiation, DEM digital elevation model M amassi et al. Agriculture & Food Security (2023) 12:22 Page 15 of 23 remote sensing-based variables (such as crop growth and contributions for developing crop decision support tools vegetation indices, soil moisture and organic matter indi- have been developed, such as the OCP-Al Moutmir ces, and plant stress indices) as well as combining soil, “NPK Engine”, which is a soil-based method for defining climate, and crop management information are practical fertilization recommendations based on empirical mod- aspects of implementing empirical methodologies [11, els that include actual soil analyses, previous field history, 61]. Furthermore, complex empirical models (machine current cereal species, and an expected yield value deter- learning techniques) may be preferable for managing and mined based on the agricultural potential of each area. analyzing a huge collection of variables (large data set) In addition, many crop management decision-making during model extraction [81]. services and tools are being developed by the AgriEdge Hundreds of crop modeling studies have recently been program (an innovation structure at the Mohamed VI conducted to address climatic and economic risks by Polytechnic University UM6P), in which researchers recommending general adaptation strategies to be fol- propose within-season and field-level optimal and sus- lowed in cropping systems under various environmental tainable crop management tools: FertiEdge, AquaEdge, conditions, such as: evaluating the effects of adopting the PhytoEdge, YieldEdge, etc. Overall, those described deci- three axes of conservation agriculture, improving plant sion support tools share the main characteristic of utiliz- breeding works, assessing general adaptive management ing empirical modelling approaches (multiple regression practices (in irrigation, weed control, or fertilization), etc. or machine learning). In addition, when the tool is recent, Crop modeling methodologies, however, have rarely been it substantially integrates more remote sensing-based incorporated into decision support systems to offer opti- indices, independently or in combination with the actual mal within-season and on-farm tactics for farmers, such environmental conditions. The absence of mechanistic as the Yield Prophet tool [60] (https:// www. yield proph models in such contributions, on the other hand, could et. com. au/ yp/ Home. aspx) that provides advice to farm- be attributed primarily to: (i) the nature of some business ers on nitrogen and irrigation applications, sowing dates, research projects that require commercial licenses to crop varieties, etc., to match crop management practices use the majority of crop simulation models, (ii) the high with the fields’ crop yield potential. requirement for field input parameters to run those mod- Various mechanistic and empirical models have been els in a large number of farmers’ fields (i.e. labor-, time-, used in Morocco to analyze general adaptation tech- and cost-consuming), and (iii) the need for expert knowl- niques for soil degradation and climate change, as well as edge to combine agronomic and modeling to exploit such to provide recommendations for optimal within-season models. cropping management guidelines. In this latter context, a Recently, in the only published review article (a sys- number of mostly unreported contributions were imple- tematic review) about crop modeling studies under spe- mented as part of agronomic advice and development cific Moroccan conditions, Epule et  al. [44] collected initiatives for small farmers (e.g. the OCP-AL Moutmir the major crop modeling achievements over the last two program) or for commercial gain. One of the famous, decades that concern the evaluation of yield gaps under older Moroccan realizations is the Fertimap project. The Moroccan cropping systems. However, during this litera- OCP group in collaboration with the Moroccan Minis- ture review, they concentrated on crop modeling meth- try of Agriculture and Maritime Fisheries and Moroccan odologies, specifically the type, methods, and structure institutes of agronomic research (INRA, IAV, ENA, etc.) of the applied crop models, as well as the input variables developed the Moroccan soil fertility map “Fertimap”, used, without any focus on the contribution of the type which was constructed from more than 32,000 soil sam- of studies (yield forecasting, general adaptation strate- ples collected from different agro-climatic zones during gies, or within-season management tools), the concerned the last decade and analyzed for different soil fertility management practices (fertilization, irrigation, weed parameters. Afterward, a decision support tool for rec- control, etc.), cropping systems (rainfed or irrigated), and ommending N–P–K fertilization at the field level was the agro-climatic conditions. Consequently, and to pro- derived from regression relations between the created vide more extensive information, we tried to highlight Fertimap soil fertility database, within-season applied various peer-reviewed crop modeling studies for wheat fertilizer rates, and recorded yield in farmers’ fields. The cropping systems in Morocco, based on those ignored Fertimap tool is available on an open access platform neglected characteristics (Table 7). (http:// www. ferti map. ma/ map. html), and fertilization recommendations can be extracted by directly select- Conclusions ing the retained fields on the map (or inserting the field In Morocco, a number of constraints and limitations coordinates) and defining a target yield value to extract encourage policymakers, agronomists, and scientists the fertilization recommendations. Other unpublished to closely monitor in-season and post-season wheat Mamassi et al. Agriculture & Food Security (2023) 12:22 Page 16 of 23 Table 7 Crop modeling studies on wheat cropping systems under Moroccan conditions Objective of crop References Title of publication Type of model Type of cropping Concerned crop Contribution’s primary Concerned Agro- modeling study system management practice scale climatic zone (contribution) Model calibration and [22] New multi‑model WOFOST and CROPSYST Irrigated and rainfed Monitoring crop growth 25 km × 25 km elemen‑ Unfavorable, intermedi‑ evaluation independent approach gives good systems and final yield predic‑ tary simulation unit ate, and favorable areas studies: estimations of wheat tion ‑ Crop growth, develop‑ yield under semi‑arid ment, and yield predic‑ climate in Morocco tion [10] Empirical regression Regression models Rainfed systems Final yield prediction Administrative level All Moroccan agro‑ ‑ Estimation of soil– models using NDVI, (provinces) and national climatic areas water and soil‑fertility rainfall and temperature level variables data for the early predic‑ tion of wheat grain yields in Morocco [21] Cereal yield forecasting Machine learning Rainfed systems Final yield prediction Agricultural province Unfavorable, intermedi‑ with satellite drought‑ unit ate, and favorable areas based indices, weather data and regional climate indices using machine learning in Morocco [87] Relevance of soil fertility APSIM Rainfed systems Monitoring crop Field level Unfavorable, intermedi‑ spatial databases for growth and final yield, ate, and favorable areas parameterizing APSIM‑ and assessment of soil wheat crop model in fertility Moroccan rainfed areas [82] GEPIC–modelling wheat EPIC Rainfed and irrigated Final yield prediction 50 km × 50 km elemen‑ Unfavorable, intermedi‑ yield and crop water systems and assessment of crop tary simulation unit ate, and favorable areas productivity with high water productivity resolution on a global scale M amassi et al. Agriculture & Food Security (2023) 12:22 Page 17 of 23 Table 7 (continued) Objective of crop References Title of publication Type of model Type of cropping Concerned crop Contribution’s primary Concerned Agro- modeling study system management practice scale climatic zone (contribution) General strategies of [38] Explaining yield and Regression model Irrigated and rainfed Assessment of different Field level Unfavorable, intermedi‑ crop management gross margin gaps for systems factors responsible for ate, and favorable areas adaptation sustainable intensifica‑ yield gaps tion of the wheat‑based systems in a Mediterra‑ nean climate [99] Adaptation of Moroccan Regression model and Irrigated and rainfed Cultivar choice and Field level Unfavorable, intermedi‑ durum wheat varieties the additive main effects systems breeding recommenda‑ ate, and favorable areas from different breeding and multiplicative inter‑ tions eras action model (AMMI) [92] Modeling the impact of APSIM Rainfed system Evaluation of con‑ Field level Favorable area conservation agriculture servation agriculture on crop production and practices soil properties in Medi‑ terranean climate [42] Towards precision Machine learning Irrigated and rainfed Recommending optimal ‑ Unfavorable, intermedi‑ agriculture in Morocco: systems crop species to grow ate, and favorable areas a machine learning and forecasting of approach for recom‑ the hourly average air mending crops and temperature forecasting weather [24] Modeling sustainable Soil and Water Assess‑ Rainfed system Improve crops’ water Watershed level Favorable area adaptation strategies ment Tool (SWAT) model productivities using toward a climate‑smart sowing dates and no‑ agriculture in a Mediter‑ tillage adaptations ranean watershed under projected climate change scenarios [23] Wheat (Triticum APSIM Rainfed system Sowing date adapta‑ Field level Unfavorable area aestivum) adaptability tions and cultivar choice evaluation in a semi‑ arid region of Central Morocco using APSIM model Mamassi et al. Agriculture & Food Security (2023) 12:22 Page 18 of 23 Table 7 (continued) Objective of crop References Title of publication Type of model Type of cropping Concerned crop Contribution’s primary Concerned Agro- modeling study system management practice scale climatic zone (contribution) Crop modeling for [43] Weather‑based predic‑ Threshold‑based Rainfed system Disease control Field level Favorable area within‑season manage‑ tive modeling of wheat weather model ment advice stripe rust infection in Morocco [142] Performance assessment AQUACROP Irrigated system Sowing date adapta‑ Field level Favorable area of AquaCrop model for tions and irrigation estimating evapotran‑ scheduling spiration, soil water content and grain yield of winter wheat in Ten‑ sift Al Haouz (Morocco): application to irrigation management [13] Assessment of vegeta‑ Regression models Irrigated system Irrigation scheduling Field level Intermediate area tion water content in (satellite‑based indices) wheat using near and shortwave infrared SPOT‑5 data in an irrigated area [20] Linkages between Regression models Rainfed system Monitoring and assess‑ Agricultural province Unfavorable, intermedi‑ rainfed cereal produc‑ ment of drought events unit ate, and favorable areas tion and agricultural drought through remote sensing indices and a land data assimilation system: a case study in Morocco [51] Calibration and valida‑ STICS Irrigated system Irrigation scheduling Field level Unfavorable area tion of the STICS crop model for managing wheat irrigation in the semi‑arid Marrakech/Al Haouz plain M amassi et al. Agriculture & Food Security (2023) 12:22 Page 19 of 23 cropping systems in rainfed circumstances. Among Declarations these are: (i) the importance of wheat for the country’s food security, (ii) the aridity of the climate, (iii) the Ethics approval and consent to participate Not applicable. impacts of inter- and intra-annual variability of rainfall and increased recurrence of extreme events, particu- Consent for publication larly drought, and (iv) the adoption of inefficient crop Not applicable. management strategies due to a shortage of knowledge Competing interests provision to Moroccan small farmers, or their indigent The authors declare no competing interests. economic situation. Hence, critical efforts were estab- Author details lished in development programs and scientific research 1 Agricultural Innovation and Technology Transfer Center (AITTC), Moham‑ to deal with the relevant constraints and limited condi- med VI Polytechnic University (UM6P), 43150 Ben‑Guerir, Morocco. 2 Spheres tions of Moroccan rainfed areas, such as increasing the Research Unit, University of Liege, 6700 Arlon, Belgium. 3 National Institute for Agronomic Research (INRA), 10101 Rabat, Morocco. 4 International Center reliance on efficient and sustainable adaptive strategies for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco. 5 Depart‑ for cereal system management, such as the three axes of ment of Crop Production, Protection and Biotechnology, Hassan II Institute conservation agriculture, rational fertilization, and pre- of Agronomy and Veterinary Medicine, 10101 Rabat, Morocco. cision agriculture, among others. During the present Received: 16 February 2023 Accepted: 6 June 2023 work, we have highlighted the effectiveness of applying crop models for the assessment and enhancement of wheat cropping systems under Moroccan rainfed con- ditions. As described in the previous section, various References types of models were adopted in past research studies 1. Acevedo E, Paola S, Herman S. Growth and wheat physiology. In: Uni‑ versity of Chile editor. Laboratory of soil‑plant‑water relations. faculty of and integrated into sophisticated systems to support agronomy and, forestry sciences. FAO Plant Production and Protection the country’s food security through monitoring wheat Series. FAO: Santiago. 2002. pp. 39–70. growth and yield forecasting. Moreover, several crop 2. Acevedo EH, Silva PC, Silva HR, Solar BR. Wheat production in Mediterra‑ nean environments. Wheat Ecol Physiol Yield Determ. 1999. p. 295–331. modeling works have contributed to increase under- 3. Ali S, Baloch AM. 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