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Experimental site, location and climateFive years’ field experimentation on ICM was started in 2014–15 at the ICAR-Indian Agricultural Research Institute (28°35′ N latitude, 77°12′ E longitude, 229 m MSL), New Delhi, India. The study site comes under the ‘Trans IGPs’, being semi-arid with an average annual rainfall of 650 mm, of which ~ 80% occurs in July–September (south-west monsoon). The mean max. / min. air temperature ranges between 20-40ºC and 4-28ºC, respectively. The five years (2014–2019) weather data were recorded from the observatory adjoining to the experimental field, and presented in Supplementary Table 1. Before start of the experiment, a rainy season Sesbania was grown in 2014 to ensure the uniform fertility across the blocks. Initial soil samples (0.0–0.15 m depth) were collected in October 2014 after incorporating the Sesbania residues in soil. The soil samples were processed for the chemical analysis. The study site had a pH of 7.9 (1:2.5 soil and water ratio)68, 3.8 g kg−1 soil organic-C69, 94.1 kg ha−1 KMnO4 oxidizable N70, 97 µg g−1 soil microbial biomass carbon71, 51.3 μg PNP g−1 soil h−1 alkaline phosphatase72, 53.0 μg TPF g−1 soil d−1 dehydrogenase73, and 13.5 μg NH4-N g−1 soil h−1urease74.Description of different ICM modulesThe eight ICM modules were tested, comprising of four conventional tillage (CT)-based (ICM1-4) and four conservation agriculture (CA)-based (ICM5-8) modules, replicated thrice in a complete randomized block design with the plot size of 60 m2 (15 m × 4.5 m) (Table 4). The crop residues were completely removed in the CT-based modules (ICM1-4), while in the ICM5-8 modules, in-situ wheat (~ 3 Mg ha−1 on dry weight basis)) and maize (~ 5 Mg ha−1, on dry weight basis) residues were retained on the soil surface during all the seasons of crops cultivation (Footnote Table 4, Fig. 6a,b).Table 4 Description of integrated crop management (ICM) modules adopted in maize and wheat crops during the five yearsˈ fixed plot experimentation.Full size tableIn the ICM1-4 modules, the field preparation was carried out by sequential tillage operations, such as, deep ploughing using the disc harrow, cultivator/rotavator twice (0.15–0.20 m), followed by levelling in each season. In the ICM3-4, the raised beds of 0.70 m bed width (bed top 0.40 m and furrow 0.30 m) were formed during each cropping cycle using the tractor mounted bed planter, and simultaneously wheat sowing was done (Fig. 6c). In the case of maize, ridges (0.67 m length) were prepared using the ridge maker. In the CA-based ICM5-8 modules, the tillage operations, such as, seed and fertilizer placement were restricted to the crop row-zone in maize and wheat both. In the ICM7&8, the permanent raised beds (0.67 m mid-furrow to mid-furrow, 0.37 m wide flat tops, and 0.15 m furrow depth), were prepared (Fig. 6d). However, these beds were reshaped using the disc coulter at the end of each cropping cycle without disturbing the surface residues. The sowing was accomplished using the raised bed multi-crop planter.Cultural operations and the fertilizer applicationDuring every season, the maize (cv. PMH 1) was sown in the first week of July using 20 kg seed ha−1. The wheat (cv. HD 2967) crop was sown in the first fortnight of November using the seed-cum fertilizer drill (ICM1-2), bed planter (ICM3-4) and zero-till seed drill (ICM5-8) at 100 kg seed ha−1. The chemical fertilizers (N, P and K) were applied as per the modules described in the footnote of Table 4. At sowing, the full doses of phosphorous (P) and potassium (K) were applied using the di-ammonium phosphate (DAP) and muriate of potash (MOP), and the nitrogen (N) supplied through DAP. The remaining N was top-dressed through urea in two equal splits after the first irrigation and tasseling / silking stages in maize, and crown root initiation and tillering stages of wheat. In the modules receiving ¾ fertilizers (ICM2,4,6,8), the seeds were treated with the NPK liquid bio-fertilizer (LBFs) (diluted 250 ml formulation 2.5 L of water ha−1), and an arbuscular mycorrhiza (AMF) was broadcasted at 12 kg ha−1 as has been described by75. This LBFs had the microbial consortia of N-fixer (Azotobacter chroococcum), P (Pseudomonas) and K (Bacillus decolorationis) solubilizers, procured from the commercial biofertilizer production unit of the Microbiology Division, ICAR-Indian Agricultural Research Institute, New Delhi (Patentee: ICAR, Govt. of India). Weeds were managed by integrating the pre- and post-emergence herbicides, and their combinations along with the hand weeding-mulching, as mentioned in the concerned modules (Footnote Table 4). However, in the CA-based modules (ICM5-8), the non-selective herbicide glyphosate (1 kg ha−1) was used 10 days before the sowing. The need-based integrated insect-pests and disease management practices were followed uniformly across the modules.Soil sampling and analysisBefore start of the experiment, the soil sampling was done from 0.0–0.15 m depth. Afterwards, five random samples from each module from 0.0–0.30 m soil depth were collected at the flowering stage of 5th season wheat. These samples were taken from the three soil depths (0.0 to 0.05, 0.05–0.15 and 0.150–0.30 m) using the core sampler. The ground, air-dried soil samples, passed through a 0.2 mm sieve were used for the determination of the Walkley and Black organic carbon (SOC), as described by76. For the soil biological properties, the soil samples were processed, and stored at 5ºC for 18–24 h, then analyzed the soil microbial biomass carbon (SMBC), dehydrogenase (SDH), alkaline phosphate (SAP) and the urease (URE) activities.The soil microbial biomass carbon (SMBC)The SMBC was measured using the fumigation extraction method as proposed by71. The pre-weighed samples from the respective soil depths were fumigated with the ethanol-free chloroform for the 24 h. Separately, a non–fumigated set was also maintained. Further, 0.5 M K2SO4 (soil: extractant 1:4) was added, and kept on a reciprocal shaker for 30 min. and then filtered through a Whatman No. 42 filter paper. OC of the filtrate was measured through the dichromate digestion, followed by the back titration with 0.05 N ferrous ammonium sulphate. The SMBC was then calculated using the equation:$${text{S}}_{{{text{MBC}}}} = {text{EC }} times { 2}.{64}$$where, EC = (Corg in fumigated soil – Corg in non-fumigated soil), and expressed in µg C g−1 soil.The dehydrogenase activity (SDH)The SDH activity (μg TPF g−1 soil d−1) was assessed using the method of73. The soil sample (~ 6 g) was saturated with 1.0 ml freshly prepared 3% triphenyltetrazolium chloride (TTC), and then incubated for 24 h under the dark. Later on, the methanol was added to stop the enzyme activity, and the absorbance of the filtered aliquot was read at 485 nm.The alkaline phosphatase activity (SAP)The APA activity was estimated in 1.0 g soil saturated with 4 ml of the modified universal buffer (MUB) along with 1 ml of p-nitrophenol phosphate followed by incubation at 37 °C for 1 h. After incubation, 1 ml of 0.5 M CaCl2 and 4 mL of NaOH were added and the contents filtered through Whatman No. 1 filter paper. The amount of p-nitrophenol in the sample was determined at 400 nm72 and the enzyme activity was expressed as µg p-NP g−1 soil h−1.The urease activityUrease activity was measured using 10 g soil suspended in 2.5 ml of urea solution (0.5%). After incubating for a day at 37 °C, 50 ml of 1 M KCl solution was added. This was kept on a shaker for 30 min and the aliquot was filtered through Whatman No. 1 filter paper. To the filtrate (10 ml), 5 ml of sodium salicylate and 2 ml of 0.1% sodium dichloro-isocyanide solution were added and the green color developed was measured at 690 nm74. These values are reported as µg NH4-N g−1 soil h−1.Water application and productivityIn experimental modules, water was given through the controlled border irrigation method. The current meter was fixed in the main lined rectangular channel, and the water velocity was measured. To get the flow discharge, then multiplied with area of cross section of the channel. The following formulae were used to calculate the applied irrigation water quantity and depth3:$${text{Irrigation water applied }}left( {text{L}} right) , = {text{ F }} times {text{ t (i)}}$$$${text{Depth }}left( {{text{mm}}} right) , = {text{ L}} div {text{A}}/{ 1}000$$where, F is flow rate (m3 s−1), t is time (s) taken in each irrigation in each module and A is area (m2).The effective precipitation (EP, difference between total rainfall and the actual evapotranspiration) was calculated, and then EP was added to the irrigation water applied to calculate the total water applied in each module. Across the maize and wheat modules (ICM1-8), irrigations were given at the critical growth stages, such as, knee high and silking / tasseling (maize) and crown root formation, maximum tillering, flowering, heading / milking (wheat) stages, and after long dry spell (≥ 10-days).On the basis of the soil water depletion pattern (at the depth of 0.60 m), in each season, 3–6 irrigations were given to maize, while wheat received 5–8 irrigations per season or crop including the pre-sowing irrigation. The rainfall data were obtained from the meteorological observatory located in the adjoining field. The water productivity (kg grains ha−1 mm−1 of water) was measured as per the equation given below:$${text{Water productivity }} = {text{ economic yield }}left( {{text{kg ha}}^{{ – {1}}} } right)/{text{ total water applied }}left( {{text{mm}}} right)$$Additionally, the systems water productivity (SWP) was also estimated by adding the water productivity (WP) of both maize and wheat crops grown under the MWR.Yield measurementsIn each season, the maize and wheat crops were harvested during the months of October and April, respectively, leaving 0.75 m border rows from all the corners of each module. The crops were harvested from the net sampling area (6 m × 3 m, 18 m2) located at the center of each plot. Maize crop was harvested manually and the wheat by using the plot combine harvester. All the harvested produce was sun dried before threshing and the grain and straw / stover yields were weighed separately. The stover/straw yields were measured by subtracting the grain weight from the total biomass. To compare the total (system) productivity of the different ICM modules, the system yield was computed, taking maize as the base crop, i.e., the maize equivalent yield (MGEY) using the equation20:$${text{M}}_{{{text{GEY}}}} left( {{text{Mg ha}}^{{ – {1}}} } right) , = {text{ Ym }} + , left{ {left( {{text{Yw }} times {text{ Pw}}} right) , div {text{ Pm}}} right}$$where, Ym = maize grain yield (Mg ha−1), Yw = wheat grain yield (Mg ha−1), Pm = price of maize grain (US$ Mg−1) and Pw = price of wheat grain (US$ Mg−1).Farm economicsUnder different ICM modules, the variable production costs and economic returns were worked out based on the prevailing market prices for the respective years. The production costs included the cost of various inputs, such as, rental value of land, seeds, pesticides, LBFs / consortia, AMF, labor, and machinery; tillage / sowing operations, irrigation, mineral fertilizers, plant protection, harvesting, and threshing etc. The costs for the crops’ residues were also considered. The system total returns were computed by adding the economic worth of the individual crop, however, the net returns were the differences between the total returns to the variable production costs of the respective module. The Govt. of India’s minimum support prices (MSP) were considered for the conversion of grain yield to the economic returns (profits) during the respective years. Further, the system net returns (SNR) were worked out by summing the net income from both maize and the wheat in Indian rupees (INR), and then converted to the US$, based on the exchange rates for different years.Sustainable yield index (SYI)77,78described the SYI as a quantitative measure of the sustainability of agricultural rotation/practice. The sustainability could be interpreted using the standard deviation (σ) values, where the lower values of the σ indicate the greater sustainability and vice-versa. Total crop productivity of maize and wheat under the different ICM modules was computed based on the five years’ mean yield data. SYI was calculated using equation78.$${text{S}}_{{{text{YI}}}} = , left( {{-}{overline{text{Y}}}_{{{text{a }}{-}}} sigma_{{text{n}}} {-}_{{1}} } right) , /{text{ Y}}^{{{-}{1}}}_{{text{m}}}$$where, –ȳa is the average yield of the crops across the years under the specific management practice, σn–1 is the standard deviation and Y–1 m is the maximum yield obtained under the set of an ICM module.Statistical analysisThe GLM procedure of the SAS 9.4 (SAS Institute, 2003, Cary, NC) was used for the statistical analysis of all the data obtained from different ICM modules to analyze the variance (ANOVA) under the randomized block design79. Tukey’s honest significant difference test was employed to compare the mean effect of the treatments at p = 0.05.Authors have confirmed that all the plant studies were carried out in accordance with relevant national, international or institutional guidelines. More

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Biological damage from water deficit in foragesReports on the tolerance to water deficit damage in the forage cultivars under study are scarce, especially in relation to N and C accumulation, Si effects, and physiological attributes.Pastures grown under water restriction with and without silicon showed a decreased cumulative amount of the beneficial element. However, pastures grown with or without water restriction that had received silicon had an increase in the cumulative amount of silicon (Fig. 2a,d). Carbon content decreased in pastures that had received silicon, regardless of water availability (Fig. 2b,e). Water restriction increased N content in both treatments with and without Si for both forages. Silicon fertigation only in plants with water restriction increased N content in cultivar Massai but decreased it in cultivar BRS Zuri (Fig. 2c,f).Figure 2Silicon (Si) content (a, d), carbon (C) content (b, e) and nitrogen (N) content (c, f) in the aerial part of forage plants cultivated in soil with different soil water retention capacity (WRC) (70 and 40%) absence (− Si) and in the presence of silicon fertigation (+ Si). *Significant to 5% probability by the F test. Lowercase letters show differences in relation to Si and uppercase in relation to WRC. The bars represent the standard error of the mean, n = 6.Full size imageThe present study evidenced, especially with Si addition to the crop, that water deficit in the P. maximum pasture, regardless of cultivar, significantly impairs plant growth by changing homeostasis, i.e., decreasing the C:N ratio by reducing plant C content. This induces instability in the metabolism of the crop, especially in terms of physiological processes31,53. Thus, it was clear that water deficit aggravated physiological stress in the pastures due to an increase in electrolyte leakage, followed by a decrease in Fv/Fm. In other words, photosynthetic efficiency decreased in association with lower relative water content in the plant, which reduced the growth of both P. maximum cultivars.Water deficit in both pastures with and without silicon supply decreased the C:N ratio, except in cultivar Massai, in which the omission of silicon increased this ratio. In an adequate condition of water availability, there was no difference between the absence and presence of Si in the pastures (Fig. 3a,d). Other authors report the same results for different forages, such as sugarcane53. Water deficit in the pastures did not change the C:Si ratio, regardless of Si. In pastures with or without water deficit, silicon fertigation decreased the C:Si ratio (Fig. 3b,e).Figure 3Ratio C:N (a, d), ratio C:Si (b, e) and carbon use efficiency (c, f) in the aerial part of forage plants cultivated in soil with different soil water retention capacities (WRC) (70 and 40%) %) absence (− Si) and in the presence of silicon fertigation (+ Si). *Significant at 5% probability. ns: not significant by the test F. Lowercase letters show differences in relation to Si and capitalization in relation to WRC. The bars represent the standard error of the mean, n = 6.Full size imageAlthough this species has a high capacity for dry matter accumulation because it has a high protein content54, it is sensitive to drought55. Drought damage to plant growth, is due to the loss of stoichiometric stability of nutrients56, which balances the mass of various elements between plants and their environments57.A promising alternative to mitigate water deficit damage in the pasture is the use of Si. This element plays a vital role in the physiological, metabolic, and/or functional processes of plants58 when properly absorbed by the crop. The present study evidences the high capacity of the pastures under study to absorb Si when under water restriction. This is because P. maximum is a Si-accumulating species (leaf Si content > 10 g kg−1), which means that these plants might have specific efficient carriers in the process of Si absorption (monosilicic acid)37,59.Biological benefits of silicon in mitigating water deficit in forageThe high Si absorption by the pastures was important because it was enough to change C and N contents in the pastures under water deficit, and consequently the C:N ratio. However, Si absorption varied depending on the cultivar. In cultivar Massai, the absorption of this element decreased due to an increase in N content, while the opposite occurred in cultivar BRS Zuri. This may have occurred because cultivar Massai has higher N absorption efficiency than BRS Zuri. One cultivar or species may have greater absorption efficiency than another because it has a more efficient nitrogen transporter. In other words, it has better kinetic indexes, such as low KM and minimum concentration, which is governed by genetics31.The decrease in the C:Si ratio in plants grown under water restriction is a result of Si supply, which increased the absorption of this element and decreased C content in both pastures. Long et al.28 also reported the importance of silicon in elementary stoichiometry in a study with banana trees under water deficit.The benefit of stoichiometric homeostasis reflected the high metabolic efficiency of C, that is, Si significantly increased C use efficiency in P. maximum pastures under water restriction (Fig. 3b,e). Other authors report this effect in Brachiaria spp. pastures under drought25 and in sugarcane plants without water stress60.Carbon use efficiency (CUE) decreased in pastures with water restriction without silicon application. However, this variable increased in pastures where this element had been applied. In pastures under adequate water availability, silicon fertigation also increased CUE (Fig. 3c,f). Sugarcane plants under water deficit also showed decreased carbon use efficiency53. This increase in C use efficiency (Fig. 3c,f) by Si may have occurred in both pastures because there was a clear decrease in C content in plants grown under water restriction (Fig. 2b,e).Hao et al.29 reported similar results in native grass species, in which high Si content correlated with low levels of C. This decrease in C content may have occurred because when absorbing the beneficial element, the plant applies an “exchange strategy” to C, particularly in cell wall components such as cellulose. This is because the energy cost of including Si in the carbon chain is lower than that of including C itself61. This strategy thus improves the homeostasis of resistance to water deficiency in pastures. Reports indicate that the increase in Si in plant tissues may decrease lignin synthesis in the cell wall, which has a high energy cost62; The plant uses a “low cost strategy” when occupying binding sites between cell wall components, providing similar structural resistance to that of lignin63.These findings may support the promising role of Si in pasture management. This was evidenced from the effect of Si on elemental stoichiometry homeostasis in both forages grown under water restriction, which favored vital physiological processes by increasing the relative water content of the plant by approximately 14% (Fig. 4a,d). However, the effect of Si on the stoichiometric homeostasis of C might have induced energy savings in the plant, which is critical under water deficit conditions. Plants under water deficit have a limitation in the CO2 assimilation rate accompanied by an increase in the activity of another sink of absorbed energy, for example, photorespiration30. Studies on other crops confirm this finding, indicating a benefit of Si on stoichiometric homeostasis in plants under water deficit. Some examples are the studies of Rocha et al.25 on pasture, and Oliveira Filho et al.26 and Teixeira et al.64 on sugarcane.Figure 4Relative water content (a, d), electrolyte leakage index (b, e) and Total phenolic content (c, f) of forage plants cultivated in soil with different soil water retention capacities (WRC) (70 and 40%) absence (− Si) and in the presence of silicon fertigation (+ Si). *Significant at 5% probability. ns: not significant by the test F. Lowercase letters show differences with respect to Si and uppercase in relation to WRC. The bars represent the standard error of the mean, n = 6.Full size imagePastures under water deficit without silicon fertigation showed decreased relative water content in the plants. On the other hand, silicon fertigation increased the relative water content of forages under water deficit (Fig. 4a,d). Wang et al.65 performed a review to elucidate the effect of silicon on plant water transport processes. The authors indicated that silica deposition on leaf cuticle and stomata decreases water loss from transpiration under water deficit stress. However, accumulating evidence suggest that silicon maintains leaf water content not by reducing water loss, but rather through osmotic adjustments, enhancing water transport and uptake. According to the same authors, enhancement of stem water transport efficiency by silicon is due to silica depositing in the cell wall of vessel tubes, avoiding collapse and embolism.The physiological improvement promoted by Si in attenuating water deficit in pastures probably correlates with the reduction of oxidative stress. In this sense, cell electrolyte leakage decreased (Fig. 4b,e), from the increase of the non-enzymatic antioxidant compound in both forages (Fig. 4c,f) or from the activity of antioxidant enzymes66. This reduces reactive oxygen species, which are common in plants under water deficit67.Water deficiency affected the production of phenolic compounds depending on the cultivar. In Massai, this variable only increased with Si supply; in BRS Zuri, however, it decreased regardless of Si. Plants with silicon fertigation had increased phenolic compound content in pastures under both water availability conditions (Fig. 4c,f). Other authors have reported this effect of Si in increasing phenolic compounds in crops such as faba bean68 and sugar beet69. This supports the hypothesis that Si can attenuate the oxidative stress caused by water deficit by increasing the non-enzymatic antioxidant compound.Exogenous application of Si protects the photosynthetic pigments from oxidative damage by reducing membrane lipid peroxidation. In peanut, this type of application either maintained or reduced H2O268. Another effect of Si that demonstrates the attenuation of oxidative stress in pastures under water deficit was the increase in Fv/Fm; in other words, it favored photosynthetic efficiency. In both pastures, the condition of water restriction without silicon supply decreased the quantum efficiency of PSII (Fv/Fm). However, the supply of silicon in pastures, regardless of water condition, increased the photochemical efficiency of PSII (Fig. 5a,c).Figure 5Quantum efficiency of photosystem II (Fv/Fm) (a, c) and total chlorophyll index (Chl a + b) (b, d) of forage plants grown in soil with different soil water retention capacities (WRC) (70 and 40%) absence (− Si) and in the presence of silicon fertigation (+ Si). *Significant at 5% probability. ns: not significant by the test F. Lowercase letters show differences in relation to Si and capitalization in relation to WRC. The bars represent the standard error of the mean, n = 6.Full size imageThe protection of photosynthetic pigments by Si is also indicative of decreased oxidative stress58. The present study evidenced this situation, as the beneficial element increased the total chlorophyll index in both forages under water deficit (Fig. 5b,d). Wang et al.69 reported that Si delays the degradation of chlorophyll–protein complexes, as the element alters the protein components of the thylakoid, thus optimizing the light collection and stability of PSI. Another benefit of Si would be an increase in osmoprotection as a result of the greater accumulation of metabolites, mainly sugars and sugar alcohols (talose, mannose, fructose, sucrose, cellobiose, trehalose, pinitol, and myo-inositol) and amino acids (glutamic acid, serine, histidine, threonine, tyrosine, valine, isoleucine, and leucine), as seen in peanut plants68.Si benefit on forage productivity under water deficitWater restriction with or without silicon supply decreased the height of both pastures, and silicon application in both water regimes increased plant height (Fig. 6a,d). Water restriction with or without silicon supply decreased the number of tillers in both pastures, except for the cultivar BRS Zuri that had received Si. Silicon application increased the number of tillers in both pastures in both water regimes, except for the cultivar Massai without water restriction (Fig. 6b,e). The dry weight of both pastures decreased under water deficit, regardless of silicon. However, the dry matter of the pastures increased after Si application, with or without water restriction (Fig. 6c,f).Figure 6Plant height (a, d), number of tillers (b, e) and dry matter mass (c, f) of forage plants grown in soil with different soil water retention capacity (WRC) (70 and 40%) absence (− Si) and in the presence of silicon fertigation (+ Si). ns: not significant by the test F. Lowercase letters show differences in relation to Si and capitalization in relation to WRC. The bars represent the standard error of the mean, n = 6.Full size imageThus, the mitigating effects of Si on the physiological processes of both pastures grown under water deficit were responsible for increasing forage growth by promoting an increase of 12% in plant height and 31% in the number of tillers, which is one of the main components of pasture production. This resulted in a 25% increase in dry matter accumulation in relation to the pasture without Si (Fig. 7). Other authors have also reported the mitigating effect of Si on water deficit with a view to increasing plant growth in forage crops70 and other crops like wheat71 and rice72.Figure 7Figure of a forage plant in the condition of water deficit in the absence (− Si) and in the presence of silicon fertigation (+ Si) and a summary of its beneficial in the effects of the plant growth.Full size imageThe present study showed that the effect of Si on the attenuation of drought is not restricted only to physiological aspects involving increased plant water content and photosynthetic or biochemical efficiency. It also regulates elemental stoichiometric homeostasis as discussed above, confirming the biological strategy reported by Hao et al.29 in other forage grasses. Our study indicates that the line of research on the relationship between water deficit and Si in elementary stoichiometry is promising and should advance towards a better understanding of the multiple effects of this beneficial element on the plant.Animal production depends on the amount of biomass produced for grazing. The report of Habermann et al.73 has indicated that climate changes, such as droughts, are threatening pasture production and have a negative impact on animal and protein production. To solve this, the present research serves as a reference for Si fertigation management during the growth of P. maximum. This management consists of a sustainable alternative to improve production with greater nutritional balance even under soil water restriction, favoring water use efficiency in cultivation (Fig. 8). Moreover, Si has long-term potential to reduce the occurrence of droughts, favoring the sustainability of ecosystems. This is because the use of the beneficial element in the soil does not produce greenhouse gases, without negative impacts on the production environment74,75.Figure 8Benefits of Si in elementary stoichiometry and its relationship with physiological and biochemical aspects.Full size imageFuture perspectivesPeatlands and other terrestrial ecosystems represent large reservoirs and filters for Si, controlling Si transfer to the oceans. Land use change during the last 250 years has decreased soil Si availability by increasing export and decreasing Si storage due to higher erosion and a decrease in potentially Si-accumulating plants. Moreover, it has led to a twofold to threefold decrease of the base flow delivery of Si76. This raises concern over forage crops, reinforcing the need for silicate fertilization to explain the response of these species to the application of this element. Future perspectives would focuse on the benefits of Si in elementary stoichiometry and its relationship with physiological and biochemical aspects.Studies should use, other forage species, especially dicotyledons sensitive to water deficit, which have different mechanisms for Si absorption. This will allow a better understanding of whether the Si mechanisms that attenuate drought in monocotyledons also occur in dicotyledons. More

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Crop dataCrop area (in hectare, ha, for sown areas) and production (in kg) statistics on departmental level from 1900 until 1988 were collected from books of national agricultural statistics (‘Statistique agricole annuelle’ or ‘Annuaire de statistique agricole’) compiled by the French Ministry of Agriculture; detailed references are provided in the supplementary information. Numbers were manually digitized from photocopied versions of the original paper documents. Data from 1989 to 2018 were derived from digital statistics from the Agreste database (‘Statistique agricole annuelle’ compiled by the Service de la Statistique et de la Prospective (SSP), Secrétariat Général du Ministère de l’Agriculture, de l’Agroalimentaire et de la Forêt (MAAF), France); details are provided in the supplementary information. Yields were calculated from total production and sown area for each department to avoid apparently often incorrect yield values printed in the old statistics books. Yields are given in kilogram per hectare (kg/ha, for sown area) for dry mass with 10–16% moisture content, depending on the crop.Data are available for ten crops: soft wheat (spring and winter separately), durum wheat, maize, oats (spring and winter), rapeseed (spring and winter), barley (spring and winter), potatoes, sugarbeet, sunflower and wine. The split into spring and winter crops eventually results in 18 distinct crop-cultivar types. Time frames with available data and the correspondence between French and English names are provided in Table 1.Table 1 Data set description for yields on department level.Full size tableThe shapes of French departments have changed over time. We use the 96 mainland (Metropolitan France) departments in their current form and subsume historical values to modern departments as follows. Corsica was one single department until 1975 but then split into Corse-du-Sud and Haute-Corse. Data for Corsica until 1975 were split equally (area, production) or copied (yield) to both new departments. Seine and Seine-et-Oise were two departments until 1967, but then subdivided into seven new departments on 1 January 1968. To account for this, we consider the values of the seven new departments (Essonne, Hauts-de-Seine, Paris, Seine-Saint-Denis, Val-de-Marne, Val-d’Oise, Yvelines) only from 1968 on and unite the two old departments into one counter-factual (“Seine_SeineOise” in the data tables) until 1967.Multiple cropping per year within this set of crops is accounted for by separate area data, but is practically nonexistent in France6.Quality filtersSome yield values had to be considered as outliers, also after checking for digitizing errors. There were four criteria for defining an outlier. First, absolute yield values larger than a physiologically currently unreachable threshold were removed; threshold values were 15 t/ha for barley and durum wheat, 200 t/ha for sugarbeet and potatoes, 20 t/ha for maize, oats and wheat, 10 t/ha for rape and sunflower and 200 hl/ha for wine. These thresholds were chosen to eliminate visually obvious outliers likely due to mismatches between area and production records. The values are set slightly above current maximum attained yields, thus remaining permissive and removing only obvious errors in this first step. Additionally, all yield values for winter rape in 1944, spring rape in 1968 and spring barley in 1980 were removed due to wrongly reported values in the yearbooks. This first step removed in total 167 yield data points. Second, the top 1% of yield values across all departments per decade were removed. Third, values above or below the mean +/− four times the standard deviation of each crop-department time series (for yield, area and production separately) were removed. Fourth, and finally, a similar variance filter as in the third step was applied within each decade of a single time series, filtering values above or below decadal mean +/− two (for yield) or three (area, production) decadal standard deviations. The latter three filters removed, on average, 3.6% of the yield and 0.2% of the area or production data, respectively (Table 1). There were, as a median, 43 yield outliers per department (out of 1,260 data points on average), with a range of 4 (department Hauts de Seine) and 255 (Nord) and an interquartile range of 35–50 outliers. Outliers were masked as missing values to avoid introducing a bias from any correction. In the accompanying data sets we provide two version of the full data set, one without any corrections (“RAW”) and one where the filters described above have been applied (“FILTERED”).ValidationNationally aggregated area, production and yield data from our data set were validated with national data from 1961 to 2018 provided by the FAO (http://faostat3.fao.org/home/E). Area and production data for crops with separate spring and winter data were summed on department level to test agreement with area and production data digitized for the ‘total’ crop. More