ABSTRACT Nitrous oxide (N2O) emissions from agricultural soils vary due to factors such as soil organic matter, soil moisture, and crop type, leading to short-term variations and

concentrated zones of high emissions, known as “hot moments” and “hotspots.” These peaks, occurring at various scales, contribute significantly to total N2O emissions. This is particularly

relevant for sandy soils, where high porosity and low water-holding capacity promote gas diffusion and create moisture variability, leading to highly heterogeneous N2O emissions. We

investigated N2O fluxes along a transect in six agriculturally used patches (0.52 ha) with varying texture, yield potential and crop rotation. We measured N2O fluxes bi-weekly over 2 years,

On patch level, N2O emissions differed more strongly than on microplot level and were mainly driven by crop-type and management. Our findings highlight the importance of accounting for

intrinsic variability in soil texture, topography, and microclimate within patches. Additionally, broader differences across management-influenced patches must be considered to better

understand the drivers of N2O emissions. This dual-scale approach emphasizes the need for high-resolution soil monitoring for mitigation strategies and to refine models. At the same time, it

guides farmers toward soil-specific fertilization to reduce emissions and maintain yields in diverse agricultural landscapes. SIMILAR CONTENT BEING VIEWED BY OTHERS LEGACY EFFECTS CAUSE

SYSTEMATIC UNDERESTIMATION OF N2O EMISSION FACTORS Article Open access 21 March 2025 A CONCEPTUAL MODEL EXPLAINING SPATIAL VARIATION IN SOIL NITROUS OXIDE EMISSIONS IN AGRICULTURAL FIELDS

Article Open access 21 November 2024 INTERMEDIATE SOIL ACIDIFICATION INDUCES HIGHEST NITROUS OXIDE EMISSIONS Article Open access 27 March 2024 INTRODUCTION Nitrous oxide (N2O) is a potent

greenhouse gas (GHG), and its warming potential is 298 times higher than that of carbon dioxide (CO2)1. With 60%, agriculture is one of the largest contributors to total N2O emissions2,3. A

key driver for N2O emissions is the type and amount of nitrogen (N) fertilizer used. While N fertilizers are essential for achieving high crop yields, they also increase the risk of N

leaching and atmospheric N losses. Studies have shown that there is a linear4,5 or even exponential6 relationship between N fertilization rates and N2O emissions. This is, the fertilizer N

use efficiency of crops ranges around 30–50% and commonly decreases with increased N fertilization rates7. The remaining fertilizer is prone to losses through various pathways, such as N2O

or NH3 emissions, or leaching as nitrate (NO3⁻)8. N2O emissions from agricultural soils are affected by a variety of environmental factors including soil organic matter, pH, soil moisture

and crop type9,10,11. These factors can create short term or small-scale variations in N2O emissions, often leading to periods and concentrated zones of high emissions, commonly referred to

as “hot moments” and “hotspots”12,13,14,15. This is particularly the case for extremely sandy soils, having unique physical and chemical properties that influence nutrient dynamics and gas

emissions. For instance, their coarse texture results in high porosity and generally low water-holding capacity, which promoting gas diffusion and can create localized areas of differing

moisture levels. Previous research found highest N2O emissions occurring in most sandy, freely drained soils, when compared to less sandy, imperfectly drained soils, because increased

nitrification at lower water filled pore space (WFPS) levels is facilitated by improved aeration and oxygen availability in coarse-textured soils16,17. In addition, sandy soils are often

more vulnerable to climate change effects, such as increased precipitation variability and drought18. Understanding how these variations in soil properties and environmental factors affect

N2O emissions can help to reduce GHG emissions and adapt agricultural practices in the face of changing climate conditions. This is especially important given that temporal and spatial peaks

in N2O emission account for a significant portion of total N2O emissions and can occur across different scales. Studies have documented substantial variability in N2O emissions across

scales ranging from microscale to regional scale, with coefficients of variation between 42 and 217%19,20,21. Research also indicates that the factors driving N2O emissions vary depending on

scale. At small-scales (< 1 m2), emissions are primarily linked to the presence of denitrifying microsites within the soil22. In contrast, at larger scales (> 10 m2), emissions are

more strongly associated with the availability of mineral N and topographic influences rather than the physical properties of the soil23. Given this significant variation in driving factors

across different scales, a thorough understanding of the scale dependency on N2O emissions is crucial for effectively managing and mitigating these N losses. Precision agriculture and

site-specific management approaches aim to reduce these N losses by tailoring practices to present spatial and temporal variability24. In aim of this, the “patchCROP” experiment by the

Leibniz Centre for Agricultural Landscape Research (ZALF) employs a patch-based design (72 m × 72 m; 0.5 ha) to explore the effects of diverse management practices on agroecosystem

responses, including GHG emissions25. This experimental design is particularly relevant in sandy soils, where even minor heterogeneities in soil texture and environmental conditions strongly

influence N2O dynamics. The 70-ha experimental area represents typical field sizes found in eastern Germany as well as large parts of Eastern Europe, where socialist-era land reforms have

resulted in fields up to 100 ha, contrasting with Western European fields that average 3 to 8 ha due to historical land-use patterns and smaller farm structures. These in average larger

fields naturally tend to exhibit higher heterogeneity, making the patchCROP design especially suited to capturing the interaction of these drivers across spatially extensive areas. While it

is well-established that different crops and management practices result in variability in N2O emissions, our study goes further by linking spatial variability between patches to both

management-specific and environmental drivers. Patch-level heterogeneity provides critical insights into how diverse management strategies interact across spatially extensive fields,

offering a valuable framework for understanding cumulative emissions and identifying potential management and soil heterogeneity driven hotspots for N2O emissions. By bridging the gap

between small-scale variability and broader field-scale dynamics, gained data might help to enhance accuracy of process based models and support the development of adaptive and targeted

mitigation strategies for diversified agricultural systems. To investigate N2O emissions and their small- and larger-scale variability across single fields characterized by pronounced

spatial heterogeneities, our study adopts a multi-driver approach. This approach emphasizes the importance of integrating multiple factors—soil properties, management practices, and weather

variability—into experimental designs. Traditional randomized block experiments often oversimplify real-world complexity, as they are conducted under homogeneous conditions that fail to

account for the complex interactions and small-scale variations present in real agricultural fields, where soil texture, soil moisture, temperature, and other environmental factors are

highly heterogeneous26. By using a transect-based design, our study seeks to address this gap and provide a more comprehensive understanding of N2O emissions in actual agricultural settings.

As highlighted by Thomas & Rayan (2024) such multi-driver experiments minimize prediction error while maximizing the information obtained, making them crucial for understanding the

interplay of environmental and management factors in N2O emissions and their spatial variability. Building on this knowledge, the following hypotheses were tested in our study: * 1. Hot

moments, such as fertilization and heavy rainfall, contribute significantly to N2O emissions, with these events accounting for 30–70% of total seasonal emissions in rain-fed systems. * 2. In

sandy soils, small-scale heterogeneities in the silt-to-sand ratio influence N2Oemissions, with higher sand content amplifying spatial heterogeneity due to higher gas diffusion, drainage

and lower water retention capability. * 3. In heterogeneous fields, N2O emissions exhibit substantial spatial variability, driven by a continuum of interactions between soil texture,

management practices, and weather conditions. RESULTS ENVIRONMENTAL CONDITIONS AND SOIL PROPERTIES With an annual precipitation of 362 mm and 656 mm, respectively, 2022 was rather dry while

2023 was relatively wet compared to the long-term average of 509 mm (ZALF weather station, 2010–202428). Summer months (June – August) of 2023 were exceptionally wet with a total of 210 mm

compared to a total of 106 mm for June to August in 2022. Lowest monthly precipitation in 2022 was recorded for March (0.5 mm) and highest for February (57.7 mm). In 2023 lowest

precipitation was in May (8.4 mm) and highest in December (90.6 mm) (Fig. 1). During the research period a total of five heavy rain events (> 20 mm d−1) occurred on 11/04/2021,

06/06/2022, 06/23/2023, 07/24/2023 and 07/28/2023. With a mean temperature of 19.7 °C, the summer of 2022 was warmer than in 2023 (18.6 °C). Monthly mean air temperature ranged from 1.2 °C

(December) to 20.9 °C (August) in 2022 and from 2.6 °C (February) to 19.3 °C (July) in 2023. Soil properties of the six patches are shown in Table 1. Specific values for each microplot can

be found in the supplementary table S2. Texture as well as C/N varied according to high yield and low yield classification of the respective patch with mean of 69.9% and 80.9% and 11.3 and

10.1, respectively. Highest sand content was found in patch 95 microplot four (94.5%) and lowest in patch 74 microplot five (67.6%). Soil texture was classified as sandy loam (high yield

patches) and loamy sand (low yield patches). Highest total carbon (TC) and total nitrogen (TN) concentrations were found in patch 65 microplot 4 (0.99 and 0.09%, respectively), whereas

lowest concentrations were found in patch 89 microplot 2 (0.59 and 0.06%, respectively). Soil pH was lower in the low yield patches compared to the high yield patches with lowest value for

patch 96 (pH 5.4) and highest for patch 73 (pH 6.4). Bulk density (BD) of the upper soil (0–34 cm) was determined in spring 2021 (patches 65 and 96) and spring 2022 (patches 73, 74, 89 and

95) and ranged from 1.70 g/cm3 in patch 89 to 1.77 g/cm3 in patch 73. TEMPORAL N2O FLUX DYNAMICS In total, more than 2.000 single N2O fluxes were measured during the study period. Figure 2

presents the temporal variation of the N2O fluxes averaged for each patch, between September 2021 and October 2023. Following mineral and organic fertilization events, indicated with purple

and red arrows, respectively, increases in N2O fluxes were observed. The highest peaks in N2O emissions during the study period were observed after organic fertilizer application in patches

65, 73 and 96. Increasing N2O fluxes were also obtained following heavy rain events (blue dotted lines). This was especially the case towards the end of the cropping season (e.g. barley

patch 74; maize patch 95). In general, time periods directly following fertilization and heavy rain events, significantly contributed to total N2O crop emissions, accounting for around 36%

and 43% of the annual crop N2O emissions in 2022 and 2023, respectively (table S3). Highest N2O flux dynamics and crop related N2O emissions were observed for maize on patch 95 (1157 g N2O-N

ha−1 crop−1) in 2022 and on patch 96 (2307 g N2O-N ha−1 crop−1) in 2023. Lowest flux dynamics and crop related N2O emissions were obtained for the non-fertilized lupine on patch 95 and

fertilized summer oat on patch 89 in 2023 (77 and 113 g N2O-N ha−1 crop−1, respectively). In general, fluxes showed higher amplitudes for high yield potential patches when compared to low

yield patches. Mean values for low yield vs. high yield crop emissions were 643 g N2O-N ha−1 crop−1 and 305 g N2O-N ha−1 crop−1, respectively. Based on the Wilcoxon Rank-Sum Test, N2O fluxes

were slightly but not significant (_p_ = 0.51) higher in 2023 compared to 2022 (mean of 640 and 444 g N2O-N ha−1 crop−1, sd = 846 and 371, respectively). SPATIAL HETEROGENEITY OF N2O

EMISSIONS With a R2 value of 0.92 and an explained variance of 67%, the employed RF model was used to examine cumulative, crop related N2O-N emissions, demonstrating a high level of

examination accuracy (Fig. 3a). The model evaluated 102 cumulative emissions, which were gap-filled using the 2.000 measured N2O fluxes. Also, the prediction model (tenfold cross-validation;

Fig. 3b) demonstrated a strong predictive capability by achieving a R2 value of 0.75, a root mean squared error (RMSE) of 2.29, and a mean absolute error (MAE) of 1.58. These results

demonstrate that the model was able to effectively capture and predict the variability of the N2O emissions in our study. According to the variable of importance analysis (Fig. 3c), the most

significant predictors of the RF model are crop type and mean daily CO2 emission, each contributing substantially to the MSE when permuted (> 30%). These key variables are followed by

air temperature, soil texture (SC:S ratio), volumetric soil moisture content, soil total N content and amount of N fertilizer. Variables of least importance were BD and slope, each

contributing less than 10% to the MSE increase. Figure 4 shows the relation between major factors revealed by the RF and the 102 cumulative, crop related N2O-N emissions. First, as indicated

by the RF model, a significant variation of N2O-N emissions was observed across different crop types (Fig. 4a). The highest N2O emission was found for patches planted with maize (average

N2O emission of 8.9 g ha-1 day-1), followed by cover crop (6.4 g ha−1 day−1). Lowest N2O-N was emitted for patches planted with lupine (0.9 g ha−1 day−1). High yield patches emitted on

average 1.7 g N2O-N ha−1 day−1, whereas low yield potential patches emitted 4.3 g N2O-N ha−1 day−1 (Fig. 4b). Second, the strong positive correlation (R2 = 0.63; Fig. 4d) between mean daily

CO2 and N2O emissions indicates that higher CO2 levels are associated with increased N2O emissions. This relationship was consistent across all patches. Highest CO2 emissions were found for

patch 96 (56.9 kg C ha−1 crop−1) and lowest values for patch 95 (7.96 kg C ha−1 crop−1). High yield patches had a mean CO2 level of 19.3 kg ha−1 crop−1, whereas low yield patches had a level

of 28.1 kg ha−1 crop−1. Crop specific CO2 levels were detected with lowest values for fallow (3.6 kg ha−1 crop−1) and highest for maize (45.8 kg ha−1 crop−1). Third, the mean air

temperature during the growing period of the specific crop was evaluated as 3rd most important variable explaining N2O-N emissions. According to the ALE plot (Fig. S1), lower air

temperatures between 4 and 12 °C had a minimal impact on the mean daily N2O emissions. Starting from 12 to 16 °C, we noticed a moderate increase indicating an increase in N2O emissions.

Temperature values above 16 °C resulted in a steep increase, suggesting a strong positive relationship between mean daily N2O and air temperature at this higher temperature range. Fourth,

the RF model evaluated the mean volumetric soil moisture content as the 5th most important variable explaining N2O-N emissions. In Fig. 4c, the calculated mean daily WFPS % is plotted

against the mean daily N2O emissions. Patch and yield specific trends were observed with high yield patches showing tendentially higher mean WFPS than low yield patches (67.6% and 58.6%,

respectively). The WFPS favoring N2O-N emissions within our patches was located between a WFPS of 40–60%. Higher WFPS > 60% values did not lead to an increase in N2O emissions. At the

microplot scale N2O emissions exhibited light to moderate correlations with soil environmental parameters like texture, soil moisture, and soil temperature. The strongest relationships were

observed with silt and sand content (r = -0.36 and 0.35, respectively; _p_ < 0.05), while the weakest correlation was found with WFPS (r = -0.22; _p_ < 0.05) (Fig. S2). Spatial

heterogeneity in N2O emissions was high and present across the microplots of each patch and strongly pronounced between patches (Fig. 5a, b). The CV within patches showed a positive

correlation with increasing sand content, achieving an R2 value of 0.7 (Fig. 5a). Levene’s test for homogeneity of variances revealed that this variation was significant (_p_ < 0.05)

within patches 73, 89, and 96. In contrast, patches 65, 74, and 95 did not exhibit significant differences in variability when compared to the remaining patches. Highest CV was found for

patch 95 (low yield) with a value of 42.9%, whereas lowest CV was found for patch 74 (high yield) with a value of 14.0%. The average CV within all high yield patches was 21.1%, compared to

39.3% for low yield patches. DISCUSSION In our first hypothesis, we proposed that hot moments, such as fertilization and heavy rainfall, contribute significantly to N2O emissions from

agricultural fields, accounting for 30–70% of total crop emissions in rain-fed systems. Previous research has reported that the first three months after fertilization events can account for

74–96% of total annual N2O emissions29 and that the fertilization event alone can account for up to 70% of total annual N2O emissions14. However, Krichels & Yang, (2019) showed that the

spatiotemporal heterogeneity in soil N2O emissions is not always characterized by predictable hot moments and that controlling factors change depending on environmental conditions, which

underlines the importance of understanding the driving factors for effectively mitigating N2O emissions. In our study, hot moments were responsible for between 6 and 71% of the total crop

specific N2O emissions (tab. S3), confirming our first hypothesis. Overall, hot moments were found to contribute to a larger share of total crop N2O emissions in “high-emission” crops like

maize and barley compared to “low-emission” crops such as summer oat or lupine. While in some cases, hot moments accounted for as little as 6% of emissions, this was typically when baseline

emissions were low as well. However, when substantial emissions occurred (> 500 g ha−1 crop−1), trigger events were responsible for 50 to 70% of the total crop related emissions. In

addition, we found that higher shares of hot moments appeared for summer crops like maize and soy (71 and 52%, respectively), compared to winter crops like winter oat and rapeseed (6 and

18%, respectively). This can likely be explained by higher air temperatures during trigger events for summer cropping periods. Previous studies found that warm and wet climate conditions

foster N2O from soil, which, in general, are more favorable conditions for nitrification11,31. Additionally, hot spots in N2O emissions are particularly likely to occur following wetting

events after dry periods32,33. The soil drying and rewetting is inducing N mineralization and denitrification, increasing N2O emission. This was pronounced in our study, as most heavy rain

induced hot moments occurred in summer months between May and August (Fig. 2) with a corresponding WFPS of e.g. 45 and 28% before and 65 and 94% after the rain event, respectively (patch 96,

maize season 2023). However, it needs to be considered that, we focused on sandy soils in a continental climate in our study, representing large areas of the northern hemisphere. When

comparing sandy soils across different climates, such as continental versus maritime climates, the patterns and timing of hot moments and trigger events may differ, resulting in varying N2O

emission dynamics. In summary, we found that hot moments played a disproportionately large role when baseline N2O emissions were high compared to periods of overall low emissions.

Importantly, our findings provide novel insights by demonstrating how the magnitude of hot moments shifts based on both crop type and small-scale spatial differences. For example, a single

heavy rainfall event contributed more significantly to N2O emissions in maize compared to lupine, soy, or rapeseed (Fig. 2, June 2023), highlighting the interaction between crop-specific

management practices and environmental conditions. While previous studies have emphasized the importance of event-based N2O measurements12,13,15, our results extend this understanding by

showing that these peak events are not static but vary significantly in their contribution across spatial scales and crop types. This nuanced understanding underscores that peak events not

only drive short-term N2O flux variability but also exert a greater impact under conditions of elevated baseline emissions. By focusing on these shifting emission peaks, we can refine

strategies for GHG mitigation, tailoring them to specific crops, spatial characteristics, and dynamic emission patterns in arable fields. Our second hypothesis stated that in sandy soils,

small-scale differences in the silt-to-sand ratio influence N2O emissions, with higher sand content amplifying spatial heterogeneity due to higher gas diffusion, drainage and lower water

retention capability18. This hypothesis was confirmed by our study. We found that N2O emissions displayed high heterogeneities on a small-scale between microplots and between patches, which

were more pronounced in more sandy soil (R2 of 0.7) (Fig. 5a, b). According to e.g. Li et al. (2022) the coarse texture can create localized areas of differing moisture levels, which can

lead to a higher heterogeneity in N2O emissions. Previous research has also shown that soils with higher sand content and good drainage tend to exhibit elevated N2O emissions16,17, which is

supporting our study. Here, we observed the greatest N2O-N losses in sandy soils with highest sand content, and an optimal WFPS range of 40–60% (Fig. 4b). This can be attributed to increased

nitrification at lower WFPS (%) due to better aeration and oxygen availability in coarse-textured soils. These results emphasize the importance of adjusting fertilization to address the

spatial variability in soil texture and moisture levels to mitigate N2O emissions. Additionally, the high macro porosity of sandy soils promotes gas diffusion, creating an optimal balance

for microbial activities and gas exchange. Previous research assumed that little N2O emissions result from denitrification in coarse textured soils35,36. However, we observed N2O emissions

occurring up to a WFPS of 60%. Thus, it is likely that while oxygen started to become limited, denitrification was initiated but the denitrification process was incomplete resulting in high

N2O emissions. A higher WFPS of > 60% did not lead to an increase in N2O emissions, which can be attributed to anaerobic conditions, which presumably led to a complete denitrification

process and subsequent emission as N2. Recent research has shown that at small scales (up to 10 m2), N2O emissions exhibit considerable heterogeneity, with CV reaching as high as 217%21.

However, studies have shown that this variability tends to increase as the spatial scale expands19,22. Our findings are consistent with these results, as we observed a significantly lower

heterogeneity within individual patches compared to between patches. This outcome was likely due to the intense variations in crops and management practices across the patches. Additionally,

the comparable lower texture variability within individual patches may also explain the lower N2O variability observed within patches compared to between patches (table S2). For our third

hypothesis, we proposed that in larger fields, N2O emissions exhibit substantial spatial variability, driven by a continuum of interactions between soil texture, management practices, and

weather conditions. This hypothesis was supported by our RF analysis which identified crop type, CO2 emissions and air temperature as the most significant predictors of mean daily N2O

emissions (Fig. 3c). Tallec et al. (2022) emphasized the role of cropping systems for N2O emissions, attributing observed differences to variations in N fertilization and management

practices. They observed lower N2O emissions during winter cropping due to the split-application of smaller N amounts during well-developed vegetation growth throughout the growing season.

Contrarily, higher N2O emissions occurred during summer cropping, driven by larger N inputs during low vegetation development in May and medium development in June. This is partially

consistent with our study. Although N fertilizer amounts were site-specifically adjusted, suggesting that their contribution to the crop type aspect was less dominant, we observed the

highest N2O peaks in summer crops during periods of low vegetation growth—both at the start of the crop season and at the start of plant dormancy when plant N-demand was minimal (Fig. 2).

During these phases, excess mineral N becomes more prone to leaching or atmospheric losses. When combined with rainfall events, nitrification and denitrification is triggered, leading

increased N2O emissions. These findings highlight the critical importance of fertilizer timing, as applying fertilizer during these low-demand phases can exacerbate N2O emissions. This is

further supported by the RF analysis, where N fertilizer ranked relatively low in importance for explaining variations in N2O emissions. Similarly, a study by Ruser et al. (2001) found that

although crop specific fertilization reduced N2O emissions, differences in soil nitrate and moisture content across crops were the main drivers of N2O emission variability. We analyzed soil

CO2 emissions during chamber closure as a proxy for belowground biomass activity and microbial activity, both contributing to soil respiration as autotrophic (belowground) and heterotrophic

respiration. In our study, higher soil respiration was associated with increased N2O emissions, likely due to enhanced microbial denitrification activity in the soil, which was also found by

Saha et al. (2021). Additionally, organic residues in the soil can provide a labile C source that may be responsible for the increase in soil respiration and increased N2O emissions40. The

elevated N2O emissions detected towards the end of the growing season (e.g. patch 89, Sunflower 2022; Fig. 2) could be attributed to the decomposition of crop residues during that period,

which is additionally enhanced by respective rain events, increasing soil moisture contents. Other studies41,42,43 employing RF to analyze the factors influencing agricultural N2O emissions

identified soil (i.e. volumetric soil moisture, average daily soil temperature) and environmental conditions (i.e. rainfall, air temperature) as the most dominant predictors, which partially

contrasts with our study (Fig. 3c). However, these studies either focused on fields cultivated with a single crop, were limited to a single year, or did not include crop type as a factor in

the RF analysis. In contrast, our study expands on this by including multiple crops, a multi-year approach, and a comprehensive assessment of spatial variability at both the small and

larger scales across different management practices. Consistent with Philibert et al. (2013), who identified crop type as one of the most important factors explaining N2O emissions, our

results further substantiate the importance of explicitly accounting for crop diversity in N2O emission models. CONCLUSION We investigated N2O fluxes along a transect in six agriculturally

managed patches, differing in soil texture, yield potential, and crop rotation, over a 2-year period that spanned multiple crop cycles and varying weather conditions. By employing a distinct

research design, we were able to analyze both fine-scale and patch-level heterogeneities in N2O emissions. Our results demonstrate that N2O emissions are shaped by the combined effects of

small-scale soil texture variations and broader patch-level factors such as crop type and management. Sandy soils exhibited pronounced microplot-scale variability in N2O fluxes, driven by

high macro porosity and low water retention, while patch-level emissions displayed even greater heterogeneity (CV of 87%) due to differences in crop type and management practices. Hot

moments, such as those triggered by fertilization and heavy rainfall, contributed significantly to total crop N2O balances, with shares as high as 71%. The timing and magnitude of these

events varied depending on site-specific soil conditions and management factors, emphasizing the critical role of spatial and temporal dynamics in driving N2O emissions. Our study provides

valuable insights by linking baseline emission levels, crop-specific dynamics, and the contribution of hot moments. These relationships highlight the need for integrating spatial

heterogeneity and temporal variability into predictive process-based models. By accounting for key drivers such as WFPS (%), soil respiration, and crop type, such models can better represent

the multifaceted nature of N2O fluxes and improve the accuracy of emission estimates. As sandy soils account for approximately 13% of global agricultural land, these findings are

particularly relevant in the context of climate change, which is expected to exacerbate the frequency and intensity of droughts and heavy rainfall events45. By bridging the gap between

small-scale variability, driven by intrinsic field variability in soil texture and other environmental conditions, and patch-level heterogeneity, driven by differences in management, our

results advance the understanding of N2O emissions and their entire variability spectrum in diversified agricultural systems. This knowledge supports the development of adaptive,

site-specific mitigation strategies, such as optimizing fertilization timing and selecting crops better suited to specific soil conditions to minimize N2O emissions. At the small scale,

adaptive management based on real-time environmental monitoring could reduce emissions, while at the field scale, optimized fertilization practices are crucial. Agricultural stakeholders

should consider precision farming approaches to minimize emissions while maintaining productivity by using crops with high N use efficiency. We urge researchers, policymakers, and

agricultural stakeholders to integrate these insights into targeted mitigation strategies that balance productivity with environmental sustainability. MATERIALS AND METHODS STUDY AREA AND

EXPERIMENTAL SETUP The study area was part of the “patchCROP” experimental field (52°44′ 86.2″ N, 14° 14′ 05.1″ E)25 initiated by the Leibniz Centre for Agricultural Landscape Research and

consisted of six patches: three high yield potential patches (patch IDs 65, 73 and 74) and three low yield potential patches (patch IDs 89, 95 and 96) with an individual size of 0.52 ha (72 

× 72 m) (Fig. 6). Yield potential zones were based on an automated cluster analysis using yield maps, soil value number, soil organic matter content and electrical resistance in the topsoil

layer (0–25 cm; Donat et al., 2022). Each patch included six approximately equidistant measurement points, also referred to as microplots, which were arranged along a ~ 30 m transect within

each patch, covering the maximum range of soil texture. Patch 74 (high yield) and 89 (low yield) were installed with bent transects, as the soil gradient changed in the respective direction.

The temperate climate of the study area is characterized by a mean annual air temperature of 9.9 °C and mean annual precipitation of 509 mm (ZALF weather station, 2010–202428). Soils were

formed during the last glacial period and are part of a hummocky ground moraine landscape and can be classified as Luvisol, according to IUSS Working group WRB, 202247. Due to a combination

of glacial and interglacial processes, forming with erosion, soils in this area are highly heterogeneous48. Patches were conventionally managed, including soil tillage and pesticide

applications. The specific crop rotation and patch management for each of the six patches throughout the study period from September 2021 until October 2023 is displayed in table S1. N2O

FLUX MEASUREMENTS N2O fluxes were measured using a non-flow-through non-steady-state (NFT-NSS) manual closed chamber system49. Two sets of six chambers (vol: 0.076/0.064 m3; area:

0.193/0.204 m2) consisting of white or light grey PVC, equipped with a pressure vent50 and one port at the top to enable air sampling with pre-evacuated glass bottles (vol: 60 ml) were used.

Gas measurements were performed for six microplots per patch by setting the chambers on a PVC frame, which was inserted 5 cm deep into the soil at the beginning of each crop growth period.

Chambers were sealed using a rubber band and additional elastic belts which were fixed to the frame to assure airtight closure and prevent leakage51. Gas samples were taken every 20 min to a

total deployment time of 60 min, resulting in a four-point flux measurement. Gas samples were analyzed for N2O and CO2 concentrations using a gas chromatograph (GC-14A and GC-14B, Shimadzu

Scientific Instruments, Japan), coupled with an electron capture detector (Loftpatch et al., 1997). Over each crop period, N2O flux measurements were carried out every 2–3 weeks. To minimize

the effects of diurnal variation, measurements were consistently conducted in the morning between 8:00 AM and 12:00 PM whenever logistically feasible53. After fertilization and heavy rain

events, measurement frequency was increased up to seven days after fertilization to cover those events more accurately, prioritizing accurate event coverage over maintaining a consistent

time of day54,55. AUXILIARY MEASUREMENTS In addition to CO2 and N2O flux measurements, environmental variables such as volumetric soil moisture and soil and air temperature were recorded in

a 15 min interval throughout the measurement period for each microplot using microclimate loggers (TMS-4, TOMST, Czech Republic), placed next to each microplot (n = 36). Soil texture

analysis for every measurement point (n = 36) was carried out by the soil laboratory of the FAU Erlangen-Nürnberg. Sand fractions were determined according to DIN 19683: 1973, while the

smaller particles were analyzed with a Sedigraph 5100 (Micromeritics). Soil total carbon (TC) and total nitrogen (TN) were analyzed by the central laboratory of ZALF according to ISO 10694

with a multiphase determinator (RC 612, Leco Instruments GmbH, Mönchengladbach, Germany). Soil pH was determined according to DIN ISO 10390 with an 855 Robotic Titrosampler (Deutsche Metrohm

GmbH &Co. KG). Air temperature in 2 m height, wind speed and direction, relative humidity, air pressure, global radiation, and precipitation were recorded in an hourly interval by a

weather station of ZALF, near the experimental field. DATA PROCESSING AND STATISTICAL ANALYSES The reliability of N2O concentration measurements during opaque chamber closure was verified

prior to N2O flux calculation using the anticipated increase in CO2 concentration (from ecosystem respiration) within an opaque chamber as a quality control criterion56. If CO2

concentrations decreased over the measurement period, those measurements were deemed biased and excluded from further analysis. Outliers in the concentration data were handled using a

multiple of the inter-quartile range (6 × IQR) of the regression residues as a threshold criterion. N2O fluxes were interpolated linearly between campaigns to obtain cropping period N2O

emission estimates. Calculation of N2O and CO2 fluxes (F; µmol m−2 s−1) was done according to the ideal gas law (Eq. 1) and based on the assumption of a linear concentration increase during

chamber closure57,58. $$F = \frac{pV}{{RTA}}* \Delta c/\Delta t$$ (1) where p represents ambient air pressure (Pa), V denotes chamber volume (m3), R the gas constant (8.314 m3 Pa/K mol−1), T

the air temperature (K), A the chamber basal area and dc/dt denotes the N2O or CO2 concentration change in the chamber headspace over the measurement time. An adaptation of the modular R

script for GHG flux calculation by Vaidya et al. (2023) was used. We identified hot moments as time periods after fertilization (14 days) and rain events (7 days). To investigate the

importance of variables explaining the mean daily N2O fluxes we used the random forest (RF) machine learning technique in R (V. 4.2.2 (2022-10-31); R Core Team, 2021) with the package

‘randomForest’61. The RF constructs an ensemble of classification or regression trees62, which does not require precise information about the form of the relationship between response and

input variables. Autocorrelation of the data was checked with the Durbin-Watson-Test (DW) (RF model: DW = 2.15, _p_ value = 0.76) to ensure reasonable results of the RF model. The DW test

calculates the residues of the regression model (RF-model) and compares the sum of the squared differences of successive residuals to the sum of the squared residuals. Specific soil

variables that did not improve the model were first identified using the Boruta algorithm and excluded through further refinement with the VSURF package in R. Homogeneity of variance was

checked using Levene’s test for each patch, to verify variations in N2O emissions within and between the patches. To analyze the interaction between the N2O emission and variables in the RF

model, Accumulated Local Effects (ALE) plots were created. We opted for ALE over partial dependence plots (PDP) because ALE accounts for feature correlations, reducing bias. The ALE describe

how features influence the prediction of a machine learning model on average63. Coefficient of variation (CV) was calculated to investigate the spatial variability in-between and between

the microplot of each patch. It is calculated as the ratio of the standard deviation to the mean. Higher values indicate greater variation. $$CV = \frac{sd}{{\overline{x}}}*100$$ (2) where

sd is the standard deviation and \(\overline{\text{x} }\) the mean of each flux calculated from the chamber concentrations. We calculated the SC:S ratio, defined as the ratio of silt plus

clay to sand content (%) of the specific soil sample to simplify the texture variable for the RF model. This provides a comprehensive soil texture metric without separating each texture

component. The slope for individual transects was calculated by subtracting the lowest elevation position (meter above sea level) from the highest and dividing by the distance (m) between

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standard porosity value of 2.65 g/m3 with the following equation: $$WFPS\,(\% ) = \frac{\theta{v}}{1 - BD/2.65}* 100$$ (3) where θv is the volumetric water content of the specific soil

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references ACKNOWLEDGEMENTS The maintenance of the patchCROP experimental infrastructure is ensured by the Leibniz Centre for Agricultural Landscape Research. We thank Dr. Ute Schmitt from

the soil laboratory of the FAU Erlangen Nürnberg for soil texture analysis, and the Central Laboratory of ZALF. We thank all student assistance and DAAD interns as well as Eva Mundschenk and

Sara Hoferer, for their help during intense N2O measurement field campaigns. Lastly, we thank the reviews for their valuable and helpful comments. FUNDING Open Access funding enabled and

organized by Projekt DEAL. This research was funded by the “IPP Optimal-N” project by the Leibniz Centre of Agricultural Landscape Research (ZALF) e.V. KG acknowledges support from BMBF for

the Junior Research Group SoilRob, project ID 031B1391. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Working Group of Isotope Biogeochemistry and Gas Fluxes, Leibniz Centre for Agricultural

Landscape Research (ZALF) e.V., Eberswalder Straße 84, 15374, Müncheberg, Germany Isabel Zentgraf, Oscar Rodrigo Monzón Díaz, Matthias Lück, Katja Kramp, Valerie Pusch & Mathias

Hoffmann * Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt-Universität zu Berlin , Invalidenstraße 42, 10099, Berlin, Germany Isabel Zentgraf & Oscar Rodrigo Monzón

Díaz * Working Group of Root Soil Interaction, Leibniz Centre for Agricultural Landscape Research (ZALF) e.V., Eberswalder Sraße 84, 15374, Müncheberg, Germany Maire Holz * Experimental

Station Müncheberg, Leibniz Centre for Agricultural Landscape Research (ZALF) e.V., Eberswalder Straße 84, 15374, Müncheberg, Germany Katja Kramp * Working Group of Resource-Efficient

Cropping Systems, Leibniz Centre for Agricultural Landscape Research (ZALF) e.V., Eberswalder Straße 84, 15374, Müncheberg, Germany Kathrin Grahmann Authors * Isabel Zentgraf View author

publications You can also search for this author inPubMed Google Scholar * Maire Holz View author publications You can also search for this author inPubMed Google Scholar * Oscar Rodrigo

Monzón Díaz View author publications You can also search for this author inPubMed Google Scholar * Matthias Lück View author publications You can also search for this author inPubMed Google

Scholar * Katja Kramp View author publications You can also search for this author inPubMed Google Scholar * Valerie Pusch View author publications You can also search for this author

inPubMed Google Scholar * Kathrin Grahmann View author publications You can also search for this author inPubMed Google Scholar * Mathias Hoffmann View author publications You can also

search for this author inPubMed Google Scholar CONTRIBUTIONS I.Z. wrote, reviewed and edited the manuscript, collected and analyzed the data, prepared all figures. M.H. reviewed and edited

the manuscript, supervised the experiment, funding acquisition. O.R.M.D. reviewed and edited the manuscript, collected the data. M.L. cured the data. K.K. reviewed and edited the manuscript,

collected the data. V.P. collected the data. K.G. reviewed and edited the manuscript, collected the data. M.H. wrote, reviewed and edited the manuscript, methodology and conceptualization

of the manuscript, supervised the experiment. CORRESPONDING AUTHOR Correspondence to Isabel Zentgraf. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests.

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KEYWORDS * Soil heterogeneity * N2O emission * Coarse-textured soils * Crop rotation