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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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Cloud cover and environmental datasetsThe monthly mean MODIS cloud fraction at 0.05° used in this study was computed from the daily cloud mask data (“cloudy” label for the bits 0–1 of “state_1 km” band) included in the MODIS Surface Reflectance product (MYD09GA.006, overpass at local time of 13:30) of Aqua from 2002 to 2018, using the reduceResolution function with “mean” aggregation method on Google Earth Engine (https://earthengine.google.com/). The 1-km cloud mask was produced based on the MOD35_L2 cloud mask product, which had been extensively validated71,72. Before computing cloud fractions, a snow/ice flag (the bit 12 of “state_1km” band) was used to remove snow or ice pixels in the cloud record because the high reflectivity of snow/ice degrades the accuracy of cloud detection, especially during winter in the northern hemisphere. Therefore, the estimated cloud effect would have larger uncertainty in boreal winter than in summer.To complement MODIS-based cloud analyses, we used the Meteosat Second Generation (MSG) hourly cloud fraction data of 2004–2013 at a spatial resolution of 0.05°. The Coordinated Universal Time (UTC) of the raw MSG hourly cloud cover data was converted to local time before being used for analysis.The cloud fraction from Sentinel-5P Near Real-Time (NRTI) data product was used in this analysis. This dataset is available from 2018-07-05 at a spatial resolution of 0.01° and it has an overpass time of 13:30 similar to MODIS. The Sentinel-5P cloud data, although having a short period of 2 years, allows for the separation of cloud effects into different cloud types, with the help of a cloud classification scheme based on cloud top pressure and cloud optical depth information30.Environmental variables include evapotranspiration (ET, MOD16A2 V6), land surface temperature (LST, MYD11A1 V6) from MODIS, and soil moisture (SM) from the TerraClimate dataset. All these environmental variables were averaged into monthly means at 0.05° resolution.Elevation data are from SRTM Digital Elevation Data at 0.05° resolution. Land cover data include MODIS (MOD12C1) and European Space Agency (ESA) global land cover products, which were aggregated to 0.05°.Defining forest cover changeTo define forest/non-forest and forest cover change, we used the Global forest cover (GFC) product which provides global tree cover for the year 2000 (baseline), yearly forest loss from 2001 to 2018, and forest gain from 2000–2012 at 30 m resolution53. The GFC data were aggregated to fractions at 0.05°. Net forest cover change was calculated as the sum of the loss and gain accumulated throughout the study period. Pixels with net forest cover change fractions smaller than 0.05 are considered to be “unchanged” and greater than 0.05 are considered to be “changed”. Unchanged forests and unchanged non-forest were defined as pixels with baseline tree cover fraction greater or less than 0.5 and with net forest change 0.15. Forest loss defined this way is expected to pose a stronger signal on clouds than that with a lower threshold, and thus improves the detectability of cloud impact against natural variability of cloud cover.Estimating potential and actual impacts of forest loss on cloud coverThe potential effect of forest on cloud (ΔCloud) was quantified as the mean cloud difference between unchanged forests and nearby non-forest as:$$Delta {{{{{rm{Cloud}}}}}}={{{{{{rm{Cloud}}}}}}}_{{{{{{rm{forest}}}}}}}-{{{{{{rm{Cloud}}}}}}}_{{{{{{rm{nonforest}}}}}}}$$
(1)
where Cloudforest and Cloudnonforest are multiyear or yearly mean cloud fractions averaged over unchanged forest and unchanged non-forest pixels, respectively. ΔCloud defined this way, with the reversed sign, represents the potential impact of forest loss on cloud cover at a given location. The methodology is designed to isolate the cloud effects of land surface conditions from those caused by meteorological conditions. It refers to local cloud impact (caused by land surface conditions) because effects from synoptic conditions and large-scale circulation changes/climate changes (meteorological conditions) are shared by both forest and non-forest and are therefore minimized through subtraction. If there is no effect of forests on cloud cover, the resulting ΔCloud would show random patterns with mixed positive and negative values instead of a systematic pattern, which indicates a cloud preference over forests or non-forest.To implement Eq. 1, we used a moving window approach to search for comparison samples between forest and nearby non-forest pixels at locations that underwent “forest change” (i.e., net forest change >0.05) across the globe73. Each moving window was sized at 9 × 9 pixels (0.45° × 0.45°) and two adjacent windows were half-overlapped with a distance of 5 pixels (i.e., the centers of two windows were 5 pixels apart along latitudinal and longitudinal direction). To avoid cloud inhibition effects from water bodies74, water pixels and their one-pixel buffer zone were masked out in the window searching strategy for ΔCloud. Therefore, ΔCloud can be calculated using unchanged forest and non-forest pixels within each moving window. This window searching strategy ensures the proximity of the forest and non-forest pixels to pixels that underwent forest change, making the estimated potential effect more representative of the actual forest change impact. To test the sensitivity of ΔCloud to window size and time period, ΔCloud was also estimated using alternative window sizes: 11 × 11 (0.55° × 0.55°), 21 × 21 (1.05° × 1.05°), 51 × 51 (2.55° × 2.55°) pixels and different periods (2002–2007, 2008–2013, and 2014–2018). The resulting ΔCloud was similar to results with the window size of 9 × 9 (0.45° × 0.45°) and among split time periods (Supplementary Figs. 2, 3). Unlike using direct comparison in cloud cover (and other biophysical variables) between forest and non-forest, an alternative method is to utilize the regression coefficients of cloud cover (dependent variable) to land cover fraction (independent variable) and estimate cloud effects assuming 100% land conversion, as adopted by ref. 58. The alternative regression-based approach is mathematically more complicated, and its implementation involves non-trivial post-processing compared with our method while producing qualitatively similar results.A similar window searching strategy was applied to estimate the differences between forests and non-forest in LST (ΔLST), ET (ΔET), and soil moisture (ΔSM) (Supplementary Fig. 10).The cloud impact estimated as the cloud differences between forest and non-forest could be confounded by their differences in topography, which is known to be an important factor for cloud formation. To minimize the topographic influence, we calculated the standard deviation (s.d.) of elevation within each moving window and removed samples with s.d. >100 m from the analysis. This filtering effectively excluded comparison samples over complex terrain such as mountainous regions so that the retained samples came from relatively flat areas.The actual effect of forest loss on cloud (ΔCloudloss) was quantified as the cloud cover difference between forest loss (Cloudloss) and nearby unchanged forest pixels (Cloudforest) using the same window searching strategy as the potential effect (Eq. 2).$$Delta {{{{{{rm{Cloud}}}}}}}_{{{{{{rm{loss}}}}}}}={{{{{{rm{Cloud}}}}}}}_{{{{{{rm{loss}}}}}}}-{{{{{{rm{Cloud}}}}}}}_{{{{{{rm{forest}}}}}}}$$
(2)
where ΔCloudloss is the actual impact of forest loss on cloud cover, Cloudloss and Cloudforest are the multiyear or yearly mean cloud cover averaged over forest loss and unchanged forest pixels, respectively. The actual impact (deforested vs. forests) shows good spatial resemblance to the potential effect (non-forest vs. forests, ΔCloud with the reversed sign), suggesting that the potential effect can provide a priori prediction of possible cloud change induced by forest loss (the correlation of the spatial pattern is 0.44, p  200 W/m2).Scale-dependency of potential cloud effect of forestTo investigate how the potential cloud effect varies with spatial scale, we reprocessed the MODIS cloud cover and GFC data into different spatial resolutions to emulate the scale change (using “mean” for cloud cover and “major” method for forest cover). Specifically, the 0.05° cloud and GFC data used in the main analysis were aggregated to coarser resolutions (0.1°, 0.25°, 0.5°, and 1°) and ΔCloud was re-estimated with the window searching strategy of slightly different configurations to accommodate the resolution change (Supplementary Fig. 12). The specific parameters of the window searching strategy under different resolutions are provided in Supplementary Table 2, including raw data resolution, window size, window distance, and display resolution. For a given resolution, ΔCloud was estimated with two-parameter combinations to ensure the robustness of the results. More

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