# Monitoring cropland cultivation, abandonment, fallowing and recultivation dynamics with regard to conflict intensity in war-affected Ukraine

Josef Wagner, Shabarinath Nair, Sergii Skakun, Erik Duncan, Fangjie Li, Oleksandra Oliinyk, Françoise Nerry, Jean Rehbinder, Inbal Becker-Reshef

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## To cite this version:

Josef Wagner, Shabarinath Nair, Sergii Skakun, Erik Duncan, Fangjie Li, et al.. Monitoring cropland cultivation, abandonment, fallowing and recultivation dynamics with regard to conflict intensity in war-affected Ukraine. Science of Remote Sensing, 2025, 12, pp.100326. 10.1016/j.srs.2025.100326. hal-05372513

HAL Id: hal-05372513

https://cnrs.hal.science/hal-05372513v1

Submitted on 19 Nov 2025

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Monitoring cropland cultivation, abandonment, fallowing and
recultivation dynamics with regard to conflict intensity in war-affected
Ukraine

Josef Wagner<sup>a1*2+</sup>, Shabarinath S. Nair<sup>a+</sup>, Sergii Skakun<sup>b,c</sup>, Erik C. Duncan<sup>b</sup>, Fangjie Li<sup>a</sup>, Oleksandra Oliinyk<sup>b</sup>, Françoise Nerry<sup>a</sup>, Jean Rehbinder<sup>a</sup> and Inbal Becker-Reshef<sup>b,a</sup>

<sup>a</sup> ICube laboratory, University of Strasbourg, Illkirch-Graffenstaden, FRANCE

<sup>b</sup> Department of Geographical Sciences, University of Maryland, College-Park, 20742, Maryland, USA

<sup>c</sup> College of Information, University of Maryland, College-Park, 20742, Maryland, USA

**Abstract**

Three years of sustained shelling, mining, and active combat have caused major cropland abandonment in Ukraine, particularly in frontline regions. Estimates of abandoned areas vary up to fourfold across studies, largely due to biased estimators and inconsistent definitions. Many assessments misclassify fallow land – temporarily unused but managed and easily recultivated – as abandoned. In contrast, truly abandoned lands are often contaminated by unexploded ordnance, mines, or chemicals, requiring clearance before cultivation. Disentangling fallow from abandoned cropland is therefore crucial for post-war recovery planning and for determining tax relief for farmers unable to access their fields.

We applied a two-level stratified sampling approach and unbiased estimators of area to track cultivated, fallow, and abandoned cropland. Four regions with distinct conflict dynamics were delineated using ACLED conflict-event data, and within each region, stratified random samples were drawn guided by a Planet-based unsupervised cultivation status map. Between 2021 and 2024, total cultivated area declined by just over 2.5 Mha (-8.5%). By 2024, 7% of cropland (2.213 ± 0.256 Mha) was abandoned, of which 1.121 ± 0.148 Mha to 1.671 ± 0.169 Mha may be permanently lost to cultivation, primarily along frontlines. Fallow areas increased nationally, especially in territories reclaimed from occupation.

In 2024 alone, up to 0.472 ± 0.143 Mha were recultivated, by far exceeding official land clearance figures and suggesting widespread reliance on informal or self-organized demining. These results challenge existing assessments and establish a replicable framework for monitoring land-use dynamics in conflict zones, supporting evidence-based recovery, landmine clearance prioritization, and agricultural policy planning in post-war Ukraine.

**Keywords:** Ukraine war; Conflict intensity; Agriculture; Abandoned cropland; Fallow cropland; Unbiased area estimators

<footnote n="1"><sup>1</sup> Corresponding author at ICube laboratory, University of Strasbourg, Illkirch-Graffenstaden, FRANCE
Email address: jwagner@unistra.fr (Josef Wagner)</footnote>

<footnote n="2"><sup>2</sup> These authors equally contributed to this work</footnote>
# Research highlights

* Cultivated, fallow and abandoned area estimation in Ukraine (2021-2024).

* 7% of Ukraine's cropland abandoned in 2024.

* Recultivation outpaced official demining, indicating informal land clearance.

* Framework supports policy, tax relief and demining prioritization in conflict zones.

## 1 Introduction

Conflict disrupts agricultural systems and reporting, reduces market transparency and ultimately harms local and/or global food security. Remote sensing is a perfect alternative for objective, safe and large-scale assessments of disruptions, and offers significant advantages over traditional field data collection methods, which are often limited by accessibility and safety concerns in conflict zones.

In affected countries, armed conflicts do not uniformly disrupt agricultural production. In Ethiopia's Tigray region, cropland abandonment increased near Armed Conflict Location and Event Dataset events Raleigh et al. [1], but most of the land remained cultivated [2]. In Burkina Faso, terrorist violence was linked to lower agricultural output and a shift toward cash crops based on fixed-effect panel regression [3]. In Nigeria, pooled ordinary least squares regression was used to demonstrate Boko Haram attacks reduced staple crop production without significantly affecting total cropped area [4]. In Sudan, proximity to conflict was defined as a causal driver of cropland abandonment using propensity score matching [5]. In more traditional and static warfare, with sustained utilization of artillery and marked frontlines, cropland abandonment is far more common. During the two Chechen wars, proximity to violence and conflict intensity were key predictors of cropland abandonment and post-war recultivation likelihood, based on logistic regression, fixed- and random-effects panel regression models [6]. Similarly, in Eastern Ukraine, four years after the 2014 Donbas war, 46% of cropland abandoned in 2014 remained uncultivated near the frontline, while recultivation was more common farther away [7]. Across these cases, although rarely assessed in causal models, proximity to frontlines and conflict duration are consistently linked to cropland abandonment and recultivation dynamics.

Ukraine, one of the world's largest exporter of agricultural commodities, presents a particularly complex and urgent case study. In 2021, the country accounted for 41% of global sunflower oil exports, 13% of corn, 18% of wheat, and 10% of rapeseed [8]. Just months after the 2021 harvest, Russia launched a full-scale invasion on February 24, 2022, anticipating a swift surrender of Kyiv and other major cities [9]. Instead, Ukrainian forces' resistance turned the invasion into a protracted war of attrition, concentrated in the East and South [10]. Some of Ukraine's most productive farmland is now exposed to daily shelling - up to 20,000 rounds - leading to widespread cropland abandonment, civilian casualties, and, in 2022, sharp increases in global food and fertilizer prices [11-13].

In Eurostat's glossary [14] which Ukraine must adhere to as part of its European Union accession process, clear distinctions exist between fallow lands and unused agricultural lands. Fallow land is intentionally left unplanted for one crop year as part of a planned rotation. Farmers may intervene in the field (e.g. ploughing, cover cropping), but cannot harvest a crop in the fallow year. Unused agricultural lands, on the other hand, refers to arable land that is no longer used and is not considered part of the rotation, though it can be brought back into cultivation using on-farm resources. In wartime, however, the definition of unused agricultural lands must be revised to reflect ground realities: sustained active combat leads to forced cropland abandonment, where the recultivation of arable land post-conflict is uncertain at best. After three years of war, Ukraine is now one of the most heavily mined country in the world [15]. Along 31,000 km² of frontlines, 3.8 ± 0.6 million artillery craters (± standard error) have been detected in
agricultural fields [16]. Due to age and storage conditions, many shells fail to explode and become
unexploded ordnance (UXO) [17]. Therefore, abandoned croplands are plausibly affected by explosive
material and chemical contamination [18]. In such a situation, making a proper distinction between fallow
and abandoned lands is critical: while fallow land can typically be recultivated in the following season,
abandoned land may be unsuitable for cultivation without prior remediation. The distinction is also
policy-relevant, as farmers forced to abandon land are eligible for tax exemptions [19], those cultivating
in combat zones are may receive subsidies [20] and humanitarian demining costs are fully covered by the
government [21]. Consistent estimates of fallow and abandoned cropland areas across conflict years are
therefore required for informed policymaking and for planning post-war rehabilitation in Ukraine.

Since the start of the war, several studies have attempted to estimate cropland abandonment across
Ukraine. Covering parts or all of Ukraine in their studies, He et al. [22], Chen et al. [23], and Dai et al.
[24] estimated 1.189 million hectares (Mha), $1.696 \pm 0.23$ Mha (± standard error), and between 2.34–2.4
Mha of cropland remained bare in the 2022 growing season, which was therefore considered abandoned.
Zhang et al. [25] expanded this definition with unattended cropland, referring to land that was sown but
never harvested or ploughed. They reported 1.72 Mha and 1.407 Mha of abandoned cropland in 2022 and
2023, respectively. In contrast, MkrtchianMüller [26] manually delineated 1.39 Mha of uncultivated
cropland along the frontlines, in 2023, based on the presence of natural vegetation, following
grassland-like dynamics. Kussul et al. [19] defined abandoned cropland as fields cultivated at least four
out of six years pre-war, subsequently left uncultivated in 2022, 2023, or 2024. They estimated 4.5, 5.3,
and 5.1 Mha of cropland were abandoned yearly from 2022 to 2024. Hartman et al. [27] defined full
abandonment as five consecutive years without management, with intermediate levels after 1–4 years. In
seven frontline oblasts, they found 3 Mha abandoned in 2023, about half first uncultivated that year. None
of these studies explicitly estimate fallow cropland areas, and definitions of abandoned cropland vary
substantially. As a result, area estimates for the same growing season differ by as much as a factor of four
between papers. This underscores the need for harmonized, policy-relevant definitions of fallow and
abandoned croplands in Ukraine that are also detectable via remote sensing imagery.

Beyond differences in definitions, area estimation methods in previous studies may also impact the
accuracy and precision of area estimates. The widely used “pixel-counting” estimator (pixel area * number of pixels in a class of interest) integrates map omission and commission errors and mixed pixels
on class boundaries into the area estimate [28, 29]. Instead, statistically unbiased estimators should be
used for area estimation [30, 31]. When compared to simple random sampling, using a geoinformation
product and a stratified estimator increases sampling efficiency: fewer sample units are required to
achieve the same standard error [32-34]. While reference data is ideally collected during field campaigns
[35-37], conflict often prevents ground access, making remotely sensed imagery interpretation the only
globally available, timely, and repeatable alternative [38, 39]. For instance, Kerner et al. [2], Li et al. [40],
Olsen et al. [5], Qadir et al. [41], [42], and Skakun et al. [7] used maps for stratification in stratified
random sample designs [31], and collected reference data through satellite imagery interpretation, for
estimating sunflower planted-, abandoned- or cultivated cropland- areas, in conflicted Tigray (Ethiopia),
Sudan, Syria, and Ukraine regions. Annotation, however, requires domain knowledge and results
ultimately depend on annotators expertise, with for instance Chen et al. [23], overestimating Ukraine’s
winter wheat, rapeseed, sunflower and maize planted areas in 2020 by respectively 35%, 209%, 14% and
72% relative to official statistics [43]. In Ukraine, achieving consistent and policy-relevant estimates of
cropland abandonment and fallowing throughout the war, depends on moving beyond pixel-counting,
toward statistically sound estimators of area and uncertainties.
Given the strategic importance of Ukraine's agricultural sector, the severe disruptions caused by the war, and inconsistencies across previous studies, there is an urgent need for Ukraine-scale, consistent estimates of planted, fallow, and abandoned cropland areas from 2021 to 2024. To address this, in this paper we generated a cultivation status map used for stratification in a stratified random sampling design and collected reference data with expert interpretation of high-resolution satellite imagery. This method allowed us to distinguish land-use states reliably across seasons and conflict years. By relying on statistically rigorous, unbiased area estimators, we not only improved the accuracy of our estimates but also quantified their uncertainty. This approach ensures transparent, policy-relevant monitoring of cropland abandonment and fallowing dynamics in Ukraine, enabling robust assessment of war-related agricultural impacts and informing post-war recovery planning.

We tested the hypothesis that conflict intensity, measured (i) by the duration of exposure and proximity to frontlines and ACLED events, and (ii) by the density of shelling around sample units, is linked to cropland abandonment and recultivation. Ultimately, we aimed to address the question: *How much cropland may be permanently abandoned as a consequence of the war in Ukraine?*, based on the hypothesis that the longer cropland has been abandoned across conflict years, the more likely it is to stay permanently abandoned.

## 2 Data and Methods

In this study, we applied a sample-based approach to quantify, with associated uncertainties, the areas of cultivated, fallow, abandoned, and recultivated cropland in Ukraine from 2021 to 2024. We then examined how cultivation patterns relate to conflict intensity metrics, given potential confounding variables.

The workflow included six key steps:

i. Defining the targeted land use classes.

ii. Generating two stratification layers: a baseline 2023 cultivation status map and a spatio-temporal map of conflict dynamics.

iii. Using these stratification layers to inform the sampling design and to draw samples.

iv. Annotating stratified sample units through remote sensing imagery interpretation, once per season from 2021 to 2024.

v. Estimating cultivated, fallow, abandoned, and recultivated areas based on both seasonal and multi-season aggregated annotations.

vi. Analyzing relationships between cultivation status, conflict indicators, and environmental or accessibility-related confounders.

### 2.1 Definitions and remote sensing-based annotation of cultivated, fallow, abandoned and recultivated croplands in Ukraine

Table 1 summarizes the commonly used terminology and definitions for different cropland cultivation statuses in institutions and academia.

<table><caption>Table 1. Terminology and definitions of cultivation statuses for croplands used in institutions and academia</caption><thead><tr><td>Definitions (may be direct citations or abstracted from the full resource)</td><td>Reference</td></tr></thead><tbody><tr><td><b>Cropland</b> includes arable land, tillage land, and agro-forestry systems below forest thresholds, but excludes fallow land.<br/><b>Arable land</b> is land that is regularly ploughed or tilled for crop production, including cereals, pulses, root crops, vegetables, and other annual crops, and may include temporarily fallow land.<br/><b>Fallow land</b> is arable land left to recover for a crop year or more, whether worked or not, with no agricultural production. It may be bare, have spontaneous vegetation, or be sown for green manure. Land fallow &gt;5 years for ecological purposes remains arable.</td><td>[14]</td></tr></tbody></table>
<table><tr><td><strong>Unutilized Agricultural Area</strong> is land previously used for agriculture that is no longer worked and not part of the crop rotation, with no intended agricultural use. It excludes fallow land but could be brought back into cultivation with means available on the farm.</td><td></td></tr><tr><td><strong>Cropland</strong> is land used for the production of cultivated crops for harvest.<br><strong>Arable land</strong> is used mostly for temporary crops. It includes land devoted to temporary crops during a 12-month reference period, as well as land that would normally be used for this purpose but has been left fallow or not sown due to unforeseen circumstances. Arable land does not include land under permanent crops, nor potentially cultivable land that is not normally cultivated.<br><strong>Fallow land</strong> is the condition of a plot between the harvest of one crop and the establishment of the next, a rest period during which cultivation practices may still be carried out.<br><strong>Abandoned Agricultural Lands</strong> are not defined specifically although mentioned.</td><td>[44]</td></tr><tr><td><strong>Cultivated land</strong> has been cultivated at least once during the 12-month growing season.<br><strong>Uncultivated land</strong> is equivalent to <strong>abandoned land</strong> and shares spectral similarities with grassland. They are identified as all lands that were cultivated at least four out of six pre-war years, that are not cultivated in 2022, 2023 or 2024.<br>The authors acknowledge the existence of <strong>intentional fallowing</strong> as part of the crop rotation but do not map this class, nor estimate its area.</td><td>[19]</td></tr><tr><td><strong>Abandoned cropland</strong> was cultivated any year in 2020 or 2021 and subsequently not cultivated in 2022, where non cultivated croplands are consistently bare for the full growing season.</td><td>[22]</td></tr><tr><td><strong>Planted pixels</strong> were sown during war-time in 2022 and <strong>unplanted pixels</strong> were not. <strong>Unplanted pixels</strong> are identified based on substantially lower NDVI than in years preceding the war (implicitly, bare soils).</td><td>[23]</td></tr><tr><td><strong>Abandoned croplands</strong> are identified based on a lower average Normalized Difference Vegetation Index than in years preceding the conflict (implicitly, bare soils).</td><td>[24]</td></tr><tr><td><strong>Abandoned lands</strong> are either <strong>unused</strong> or <strong>unattended</strong>. Unused refers to cropland with no sowing activities, while "unattended" indicates cropland that has been sown but lacks or has no management.</td><td>[25]</td></tr><tr><td><strong>Cultivated cropland</strong> is characterized by a tessellated pattern of fields with different crop types at different growing stages; by contrast, abandoned cropland becomes overgrown with natural vegetation and typically displays a more uniform pattern. With sufficiently high summer precipitation, <strong>abandoned croplands</strong> appear greener than cultivated fields throughout the growing season, exemplified by the clearly visible abandoned plots.</td><td>[26]</td></tr><tr><td><strong>Abandoned land</strong> is land that has not been managed for at least five consecutive years. Early stages of abandonment are defined: <em>emerging</em> (three to four years), <em>potential</em> (two years), or expected (historically cultivated but uncultivated in the most recent year).<br><strong>Fallow land</strong> is land that is deliberately left uncultivated for a limited period, typically less than five years, as part of normal crop rotation or soil recovery practices. Authors do not map or estimate area for this class.</td><td>[27]</td></tr></table>

In Fig. 1, to support our definitions of cultivated, fallow and abandoned cropland, we present average and standard deviation Normalized Difference Vegetation Index (NDVI) time series for five major cropland signals in Ukraine. We extracted the time series over sample units annotated in a previous work [45] for winter and summer crops and over sample units annotated for this paper for abandoned and fallow croplands. For reference, we displayed a single time series for a spring crop, for which we did not have a large sample.
<figure><img src="image_3.png" /><figcaption>**Figure 1. Typical cropland NDVI time series in Ukraine**
Average and standard deviations were computed from sample units annotated in season 2023.</figcaption></figure>

In line with Kussul et al. [19] and the United Nations [44], we defined cultivated cropland as all croplands intended for annual crops that were sown and harvested, regardless of the timing of harvest. We excluded fallow lands, grasslands, agroforestry systems, and orchards. Cultivated croplands exhibit three main temporal signals: (i) Winter crops (e.g., winter wheat, rapeseed) are sown in the preceding fall, begin greening in February, peak in May, and are harvested by July (Blue line, Fig. 1); (ii) summer crops are planted up to late May, green in June, peak in July, and are harvested from mid-September onward (Dark green line, Fig. 1) and (iii) spring crops (e.g., spring barley) emerge in April, peak slightly later than winter crops, and are harvested in July–August (light green line, Fig. 1). This definition matches policy expectations as it integrates all croplands that actively contribute to national production.

*We defined non-cultivated croplands as fields where no production occurred during a full 12-month crop cycle, and we further divided them into fallow and abandoned lands. We defined managed croplands as fields that show signs of farmer intervention, being either cultivated or fallow.*

Fallow land is intentionally left uncultivated for one or more annual crop cycles [14, 19, 27, 44]. In Ukraine, idle land rapidly overgrows with weeds due to generally rainy springs [26]. Therefore, bare fields for a full season usually result from intentional farmer interventions, such as tillage or herbicide application. We therefore considered croplands as fallow if fields show signs of management but are not harvested (e.g. ploughing for weed management). This often translates to soils remaining bare for an entire crop cycle, which may be shorter than 12 months (for example, if a summer crop is harvested in October of year *n*–1 and a winter crop is planted in September of year *n*) (Brown line, Fig. 1). We also disagreed with definitions of abandoned cropland that include or are limited to seasonally bare soils [19, 22-25], because bare soils are unlikely to occur in Ukraine without farmer management [26].
*We defined as abandoned, all croplands that include fields that were planted but never harvested or those that became overgrown with weeds, without showing signs of management for a full crop cycle* [25, 26] (Black line, Fig. 1). Institutions do not specifically define cropland abandonment [14, 44] while classical academic definitions of abandonment typically require multiple years of non-cultivation [27]. Those long-term criteria are inapplicable in the context of the war in Ukraine, as farmers should benefit from tax exemptions immediately after being forced out of their lands [19]. Conversely, considering intentional fallowing as abandonment will overstate tax exemptions.

*We defined cropland as recultivated, if and only if, it was abandoned at any point between 2022 and 2024, then managed again in a subsequent growing season (either cultivated or fallow).*

In practice, those definitions may be slightly overlapping as certain fallow lands may be covered in spontaneously grown vegetation [14] and as certain abandoned croplands in dryer southern regions may appear barren in the absence of irrigation after the Kakhovka dam breach [46].

We addressed those concerns through the sample-based approach for area estimation described in Section 2.2. Each sample unit from the sample was annotated once per season from 2021 to 2024. First, the theoretical definitions of target land use classes were translated into remote sensing “interpretable” signals. Those signals were compiled into a set of clearly defined remote sensing imagery interpretation guidelines, available in Supplementary Materials Section 2. Fig. 2 outlines those guidelines and shows typical Planet two-week composite time series for winter crops (harvest is visible in the July composite, Fig. 2a), summer crops, spring crops, fallow cropland (tillage is clearly visible in the June and July composites, Fig. 2d) and abandoned cropland (no farmer intervention visible, Fig. 2e). Next, based on the guidelines, the primary seasonal label for the sample unit was assessed based on remote sensing imagery interpretation. Labels are assigned for each calendar year (January to December). Two to three experienced interpreters identified crop types and detected management events primarily using Planet's two-week true color composites alongside NDVI time series. For ambiguous cases, Planet's daily imagery was jointly reviewed until reaching a consensus on the primary label. We accessed daily Planet data through the NASA CSDA agreement.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Planet 2-week composite time series (1 composite per month) for major cropland signals in Ukraine
Red dots correspond to the sample unit to be annotated.</figcaption></figure>

We assigned the final seasonal label based on the decision tree in Fig. 3, considering, when required, primary labels for multiple seasons. There are seven possible annotation scenarios for cropland, noted A to G in Fig. 3, further illustrated on a timeline in Fig. 4.
<figure><img src="image_5.png" /><figcaption>Figure 3. Decision tree for sample unit class assignment</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Figure 4. Timeline representation of sample unit class assignment rules in Fig. 3</figcaption></figure>
We assigned sample units to the class cultivated only if we detected both a crop growth signal in the current season and a plausible harvest signal, regardless of when this harvest occurs (Fig. 3, 4 A, B). In the absence of signs of management or harvest, sample units were considered abandoned (Fir. 3, 4 C, D). We identified fallow sample units based on soil treatment with machinery, which alters the surface and is visible in true color imagery [47] (Fig 3, 4 E). As per USDA crop calendars, all plantings in Ukraine should be completed by the end of May [48]. Fields with first soil works from June onwards therefore cannot contribute to the current season's production. In all cases where soil works occurred before June 1st of the ongoing season, we considered the field as fallow (Fig. 3, 4 G). In the case where a field was abandoned in the previous season and first soil works occurred after the end of May, we considered this field as abandoned for one more season (Fig. 3, 4 F).

Special cases:

i. A sample unit was annotated as non-cropland if it fell within cropland abandoned during the 2014 Donbas war.

ii. No sample unit was annotated as abandoned in 2021, as we did not expect war-driven cropland abandonment then.

## 2.2 .Conceptual framework for sample-based area estimation with remote sensing

The stratified random sampling design and estimator for area and uncertainty estimation has been diligently described in Olofsson et al. [31]. In this design, a map is used as a stratifier to draw a simple random sample of pixels (the sample units) within each map class (the strata). After reference data has been collected, predicted values from the map and reference values are compared in a confusion matrix, expressed in terms of fractions of the total study area. True area fractions and corresponding variances are derived with a set of formulae described in Section 2.4.3. Stratified random sampling has proven to be more efficient than simple random sampling, with less sample units required to reach the same level of uncertainty around the estimate [32, 33]. Due to simple random sampling of sample units within strata, sample units are scattered across the study area. This framework is therefore not adapted when ground-visits are required for area estimation, due to travel costs between units. However, in conflict situations, where ground access may jeopardize lives [39], reference data is often interpreted from remote sensing imagery. Under such circumstances, the stratified random sampling design and estimator are adapted for area estimation.

Therefore, given the conflict situation in Ukraine, we adopted a stratified random sampling design for estimating cultivated, abandoned, fallow, and recultivated cropland areas in Ukraine. Fig. 5 illustrates the main processing steps of the approach. The first prerequisite for this design is the availability of a stratifier. One approach to generate such stratifier is to map cultivation status for every agricultural season, then establish strata as change classes (e.g., cultivated in 2022, abandoned in 2023, cultivated in 2024 would correspond to a 'recultivated' stratum). Several arguments speak against using this approach: (i) with a four-year study period, this would create numerous combinations of strata, increasing the complexity of the design; (ii) given the study region is full Ukraine, strata such as 'recultivated in 2023' or 'fallow in 2024' would have very small sampling weights, leading to inefficient stratification and increased standard errors [31, 34]; and (iii) training yearly classification models of cultivated, abandoned, and fallow land would likely result in compounded model errors, further decreasing the stratification efficiency, especially in the absence of ground data for model fitting.
Instead, we chose to map cultivation status only once, for the 2023 season, the intermediate conflict year in our study period (Fig. 5A). To capture areas likely to experience cultivation status change, we integrated Armed Conflict Location and Event Dataset conflict data to delineate four zones with distinct conflict dynamics between 2022 and 2024 (increasing, decreasing, constant, and no conflict) (Fig. 5B). Within each zone, we drew separate stratified random samples using the cultivation status map as a stratifier (Fig. 5C). The resulting sampling design combined regional conflict intensity zoning in a first level and stratification by cultivation status in a second level, optimizing representation of spatial and temporal variability in cropland dynamics. We finally annotated the sample yearly and used annotations as reference labels for seasonal and multi-year area estimation (Fig. 5D). This approach allowed us to enhance the efficiency of the sampling design by (i) using more understandable strata labels; by (ii) increasing the sampling weight of small strata by splitting Ukraine into conflict sub-regions and by (iii) cutting the need for an error prone change map.

<figure><img src="image_7.png" /><figcaption>**Figure 5. Area estimation workflow**

ACLED = Armed Conflict Location and Event Dataset; CIS = Conflict Intensity Status</figcaption></figure>
## 2.3 Creation of stratifiers for crop area estimation

### 2.3.1 Level 1 stratification: Conflict Intensity Status in Ukraine

In a previous study, Kerner et al. [2] used the Armed Conflict Location and Event Dataset (ACLED) to delineate static conflict and no-conflict zones, for stratification in samples-based area estimation. Their approach only accounts for the proximity to conflict but does not consider event counts and temporal conflict dynamics. We integrated those two dimensions to precisely target our area estimation efforts, by dividing Ukraine into four regions with distinct Conflict Intensity Statuses (CIS), between 2022 and 2024. CIS are defined as follows:

* No conflict: areas with consistently low conflict event counts from 2022 to 2024, where we expected minimal reductions in cultivated area.

* Constant conflict: areas with a sustained high number of violent events across all three years, likely resulting in widespread cropland abandonment.

* Increasing conflict: areas that experienced few events in 2022 but saw a sharp rise by 2024, where we anticipated progressive cropland abandonment.

* Decreasing conflict: areas that experienced high conflict intensity in 2022, but saw a decline by 2024, often due to territorial reclamation by Ukrainian forces; here, we expected the highest rates of cropland recultivation.

Over Ukraine, ACLED provides geolocated information on actors, fatalities, and types of political violence and protests. Events are compiled from vetted reports by the Ukrainian and Russian armed forces, Ukrainian media, NGOs, international organizations, and political parties. As of September 17, 2024, 8,000 newly verified events were added in Sumy, Chernihiv, and Kherson oblasts (covering 2023 onward). A further 13,000 events from February–December 2022, were added in June 2025, but were unavailable for this study [49, 50]. For our analysis, we extracted all ACLED events reported between February 1, 2022, and November 20, 2024. We retained only violent events with the highest geolocation accuracy (geo_precision = 1), yielding 55,476 events across five categories (Table 2). Each event was modeled spatially as a buffered point, with buffer radii defined by the ACLED–WorldPop conflict exposure project [51] (“Exposure radius” column in Table 2).

<table><caption>Table 2. ACLED event counts per event and per year, exposure radius per event</caption><thead><tr><th></th><th>2022</th><th>2023</th><th>2024</th><th>Exposure radius (km)</th></tr></thead><tbody><tr><td>Battles</td><td>1,469</td><td>357</td><td>384</td><td>5</td></tr><tr><td>Explosions/Remote Violence</td><td>12,957</td><td>19,761</td><td>18,622</td><td>5</td></tr><tr><td>Riots</td><td>5</td><td>227</td><td>2</td><td>2</td></tr><tr><td>Strategic developments</td><td>855</td><td>38</td><td>138</td><td>0</td></tr><tr><td>Violence against civilians</td><td>716</td><td>--</td><td>35</td><td></td></tr><tr><td>• With casualties</td><td></td><td></td><td></td><td>5</td></tr><tr><td>• Without casualties</td><td></td><td></td><td></td><td>2</td></tr><tr><td>Total</td><td>16,002</td><td>20,293</td><td>19,181</td><td></td></tr></tbody></table>

We followed these steps to create CIS:

(i) Following ACLED's recommendation to prioritize event over casualty counts when assessing conflict intensity [49], we calculated a yearly Conflict Proportion (CP) over a 10 × 10 km grid
(i) covering all of Ukraine. For each grid cell $g$, we defined CPC as the total area of $g$ divided by the sum of all partial or complete buffered conflict event areas within $g$. Grid sizes of 10 to 20 km are commonly used for area estimation in regions with large field sizes [35-37, 52].

(ii) Based on CP values, we assigned each grid cell to one of three Conflict Proportion Classes (CPC; Fig. 6A–C): CPC 0 (CP values between 0 and <1), CPC 1 (CP values from 1 to <16), and CPC 2 (CP values greater than or equal to 16). We derived CPC class thresholds using the cumulative square root of frequencies rule (Cochran [30], Chapter 5A.7) and retained bounds from 2023, the season with the highest conflict event counts.

(iii) By combining CPCs across the three years (2022–2024), we identified 27 temporal conflict patterns, which we grouped into four CIS (Fig. 6D). We defined the no conflict CIS as all blocks where CPs were constantly 0 from 2022 to 2024. In the constant conflict CIS, CPC was equal to or greater than 1 in both 2022 and 2024. In the increasing conflict CIS, CPC was 0 in 2022 and increased to 1 or 2 by 2024. In the decreasing conflict CIS, CPC dropped to 0 by 2024 but was equal to or greater than 1 at least once in 2022 or 2023 (Table 3). While CPC classes 1 and 2 are interchangeable in the CIS classification, they permit a finer visualization of conflict dynamics in Fig. 6. For instance, in season 2022, conflict intensity was moderate in Southern Ukraine (CPC 1, Fig. 6A). After the retreat of Russian troops in late 2022, the war then intensified in 2023 and 2024 (Fig. 6B-C), transitioning from CPC 1 to CPC 2.

<figure><img src="image_8.png" /></figure>
<caption>Figure 6. A to C, CPC per calendar year. D. CIS.</caption>

<table><caption>Table 3. CPC patterns and corresponding CIS</caption><thead><tr><th>CIS</th><th>CPC Pattern</th></tr></thead><tbody><tr><td>No conflict</td><td>000</td></tr><tr><td>Constant conflict</td><td>101; 102; 111; 112; 121; 122; 201; 202; 211; 212; 221; 222</td></tr><tr><td>Increasing conflict</td><td>001; 002; 011; 012; 021; 022</td></tr><tr><td>Decreasing conflict</td><td>010; 020; 100; 110; 120; 200; 210; 220</td></tr></tbody></table>

### 2.3.2 Level 2 stratification: 2023 cultivation status map

We mapped cultivated (winter, summer, and spring crops) and non-cultivated (fallow and abandoned) croplands across Ukraine using 13 biweekly Planet composites from March 19th to September 5th, 2023, the country's main growing season [48]. Planet operates a fleet of CubeSats that acquire daily optical imagery in eight spectral bands at 3–5 m spatial resolution. For this study, Planet experts aggregated daily acquisitions into two-week basemaps using the red, green, blue, and near-infrared channels [53], covering the February 2020–January 2025 period. All mapping operations were conducted in Google Earth Engine (Gorelick et al. [54]).

Roy et al. [55], found there is an 80% chance for there being at least one cloud-free Planet acquisition every five days, for any five-day period between November 2019 and December 2020 over Ukraine. Therefore, the probability for there being at least one cloud free composite in a 15-day period (one more day than our compositing window) is $1 - 0.2^3 = 0.992$. Under such circumstances, we simply removed invalid observations using the provided cloud mask and linearly interpolated missing values.

We adopted a K-means clustering approach for classification, as it yielded better performance than a supervised Random Forest model in distinguishing abandoned croplands (see Supplementary Materials, Section 1). The training dataset consisted of 1,750 sample units drawn from a stratified random sample previously annotated for 2023 crop area estimation [45]. After filtering out non-cropland units, we computed NDVI time series for each sample unit from the 13 composites and derived 30 temporal metrics (Table 4), similar to those in Hansen et al. [56]. Following the removal of invalid records, the final dataset included 1,207 sample units: 245 non-cultivated (abandoned or fallow) and 962 cultivated.

K-means clustering was applied to these temporal metrics with the number of clusters set to 11, following an approach like Wang et al. [57]. This configuration captured the five dominant NDVI temporal profiles observed across Ukraine (Fig. 1), doubled to account for climatic variability, plus one additional cluster to represent rare or atypical patterns. The classification was restricted to areas identified as cropland in the ESA WorldCereals 2021 product [58]. Each of the 11 clusters was then assigned to either the “cultivated” or “non-cultivated” class based on its dominant label in the training data, following the criteria outlined in Section 2.1.

After clustering and cluster assignment, a single-iteration focal mode filter with a 3-pixel circular neighborhood was applied to reduce pixel-level noise and improve spatial coherence.
<table><caption>Table 4. Temporal features used for classification</caption><thead><tr><td>Indicator</td><td>Description</td></tr></thead><tbody><tr><td>POS</td><td>NDVI value at the peak of the season</td></tr><tr><td>POS_ti</td><td>Index at which POS occurred</td></tr><tr><td>minBef</td><td>Minimal value of NDVI before POS</td></tr><tr><td>medBef</td><td>Median value of NDVI before POS</td></tr><tr><td>meanBef</td><td>Average value of NDVI before POS</td></tr><tr><td>sumBef</td><td>Sum of NDVI values before POS</td></tr><tr><td>minAft</td><td>Minimal value of NDVI after POS</td></tr><tr><td>minAft_ti</td><td>Index at which minAft occurred</td></tr><tr><td>MedAft</td><td>Median value of NDVI after POS</td></tr><tr><td>meanAft</td><td>Average value of NDVI after POS</td></tr><tr><td>sumAft</td><td>Sum of NDVI values after POS</td></tr><tr><td>minMaxBef</td><td>POS - minBef</td></tr><tr><td>minMaxAft</td><td>POS - minAft</td></tr><tr><td>ti_25Bef</td><td>Index at which NDVI was 25% of minMaxBef</td></tr><tr><td>ti_50Bef</td><td>Index at which NDVI was 50% of minMaxBef</td></tr><tr><td>ti_75Bef</td><td>Index at which NDVI was 75% of minMaxBef</td></tr><tr><td>ti_25Aft</td><td>Index at which NDVI was 25% of minMaxAft</td></tr><tr><td>ti_50Aft</td><td>Index at which NDVI was 50% of minMaxAft</td></tr><tr><td>ti_75Aft</td><td>Index at which NDVI was 75% of minMaxAft</td></tr><tr><td>dist25</td><td>Number of timestamps between ti_25Bef and ti_25Aft</td></tr><tr><td>dist50</td><td>Number of timestamps between ti_50Bef and ti_50Aft</td></tr><tr><td>dist75</td><td>Number of timestamps between ti_75Bef and ti_75Aft</td></tr><tr><td>distPOS_tiMinBef</td><td>Number of timestamps between index of minBef and POS ti</td></tr><tr><td>distPOS_tiMinAft</td><td>Number of timestamps between minAft ti and POS ti</td></tr><tr><td>tanMaxMinBef</td><td>minMaxBef / distPOS tiMinBef</td></tr><tr><td>tanMaxMinAft</td><td>minMaxAft / distPOS tiMinAft</td></tr><tr><td>seasMean</td><td>Average NDVI value over all timestamps</td></tr><tr><td>seasMed</td><td>Median NDVI values over all timestamps</td></tr><tr><td>normSeasSum</td><td>Sum of NDVI values divided by number of valid observations, based on cloud-mask provided by Planet</td></tr><tr><td>seasSlope</td><td>Linear regression slope of NDVI over all timestamps</td></tr></tbody></table>

## 2.4 Stratified random sampling for area estimation

### 2.4.1 Sampling design

Per CIS and using the cultivation status map as a stratifier, we defined separate stratified random sampling designs. Given the limited availability of resources for sample unit annotation, we restricted the total number of sample units to 2,000, to be split across the four CIS. When estimating sample-based areas with stratified random sampling, the size of the study region does not influence the number of samples required to achieve reliable area estimates, since only the stratum weights are incorporated into
the estimator formulae (Eq. 1). Therefore, both large and small study regions (e.g., no conflict and increasing conflict CIS) required comparable total sample sizes for area estimation.

We allocated samples within each CIS following the principles outlined by Olofsson et al. [31]. They established that when the survey objective is to estimate map accuracy, proportional allocation should be favored. However, when the focus is on area estimation and reducing the standard errors for area estimates, they recommend staying as true as possible to the proportional allocation, while slightly oversampling classes that are rare or of particular analytical interest. This compromise ensures robust area estimation of critical but infrequent land-use categories, without unduly inflating accuracy for any stratum.

For each CIS, we computed pixel-count-based stratum weights ($W_i$) for cultivated, fallow, and abandoned classes using the 2023 cultivation status map, projected to Albers Equal Area to avoid variations in pixel area. $W_i$ equals the sampling rate when using proportional allocation. A total of 450 sample units were allocated to the no conflict CIS, where minimal change in cultivation was expected; 550 units were drawn in the decreasing conflict CIS, where recultivation was most likely; and 500 units were attributed to the constant and increasing conflict CIS, where abandonment was anticipated to be more prevalent. Within each CIS, we adhered as closely as possible to proportional allocation while modestly oversampling cultivated and non-cultivated strata. The final sampling rate for each CIS is reported as $S_r$ in Table 5. Comparing $W_i$ and $S_r$ quantifies the deviation from the proportional (area weighted) allocation.

<table><caption>Table 5. Sampling designs per CIS
$W_{CIS}$ is the weight of each CIS in Ukraine, $W_i$ is the weight of stratum $i$, and $n_i$ is the number of sample units allocated to stratum $i$ and $S_r$ is the sampling rate in each stratum</caption><thead><tr><th>CIS</th><th>W<sub>CIS</sub></th><th>Stratum(i)</th><th>W<sub>i</sub></th><th>n<sub>i</sub></th><th>S<sub>r</sub></th><th>Σ n<sub>i</sub></th></tr></thead><tbody><tr><td rowspan="3">No conflict</td><td rowspan="3">0.88</td><td>Non crop</td><td>0.52</td><td>200</td><td>0.44</td><td rowspan="3">450</td></tr><tr><td>Non-cultivated</td><td>0.06</td><td>75</td><td>0.17</td></tr><tr><td>Cultivated</td><td>0.42</td><td>175</td><td>0.39</td></tr><tr><td rowspan="3">Constant conflict</td><td rowspan="3">0.045</td><td>Non crop</td><td>0.53</td><td>250</td><td>0.50</td><td rowspan="3">500</td></tr><tr><td>Non-cultivated</td><td>0.23</td><td>125</td><td>0.25</td></tr><tr><td>Cultivated</td><td>0.24</td><td>125</td><td>0.25</td></tr><tr><td rowspan="3">Increasing conflict</td><td rowspan="3">0.025</td><td>Non crop</td><td>0.50</td><td>210</td><td>0.42</td><td rowspan="3">500</td></tr><tr><td>Non-cultivated</td><td>0.15</td><td>110</td><td>0.22</td></tr><tr><td>Cultivated</td><td>0.35</td><td>180</td><td>0.36</td></tr><tr><td rowspan="3">Decreasing conflict</td><td rowspan="3">0.05</td><td>Non crop</td><td>0.50</td><td>250</td><td>0.45</td><td rowspan="3">550</td></tr><tr><td>Non-cultivated</td><td>0.15</td><td>150</td><td>0.27</td></tr><tr><td>Cultivated</td><td>0.35</td><td>150</td><td>0.27</td></tr></tbody></table>

### 2.4.2 Response design

We used Planet 5*5-meter spatial resolution pixels as the spatial assessment unit and annotated each sample unit first to primary seasonal labels, then to final seasonal labels following the definitions and logic presented in Section 2.1.

### 2.4.3 Area estimation

We used estimators for area estimation recommended by Olofsson et al. [31]. It is a sampling-based approach where elements of the confusion matrix $p_{ij}$ are estimated in terms of area proportions (map class
i, which corresponds to the map, and reference class j, which is a reference value from annotation). The true proportion of the area of map class k, noted $\hat{p}_{,k}$ is computed as

$$ \hat{p}_{,k} = \sum_{i=1}^{q} W_i \frac{n_{ik}}{n_i}, \qquad (1) $$

where q is the number of strata, $W_i$ is the weight of stratum i, $n_{ik}$ is the number of sample units in class k mapped to stratum i and $n_i$ is the sample size in stratum i (Eq. 1). The standard error of $\hat{p}_{,k}$ is

$$ S(\hat{p}_{,k}) = \sqrt{\sum_i \frac{W_i \hat{p}_{ik} - p_{ik}}{n_i - 1}}, \qquad (2) $$

such that for each stratum i, $\hat{p}_{ik}$ is the proportion of area of stratum i which belongs to class k (Eq. 2).

Finally, the area and standard error of the area of class k, respectively $\hat{A}_k$ and $S(\hat{A}_k)$ are expressed as Eqs. 3 and 4:

$$ \hat{A}_k = \hat{p}_{,k} A, \qquad (3) $$

$$ S(\hat{A}_k) = S(\hat{p}_{,k}) A, \qquad (4) $$

where A is the total study area. Total Ukraine area $\hat{A}_k$ and standard error $S(\hat{A}_k)$ for a class k are computed as Eqs. 5 and 6:

$$ \hat{A}U_k = \sum_{CIS} \hat{A}_{k'} \qquad (5) $$

$$ S(\hat{A}U_k) = \sqrt{\sum_{CIS} S(\hat{A}_k)^2}. \qquad (6) $$

While accuracy estimation requires map strata to match reference data classes, area estimates and standard errors can be obtained, following Eqs. 1 to 6, for a higher number of reference data classes than existing map strata. Thus, although our cultivation status map did not distinguish between fallow and abandoned cropland, we were still able to estimate areas for both using the same estimator, per CIS and per season between 2021 and 2024. In the results section, we present area estimates as absolute and relative values. For visualization purposes, we normalized CIS level, seasonal area estimates, and standard errors by dividing the variable of interest (e.g., cultivated area) by the total cropland area (cultivated + fallow + abandoned area) for the corresponding CIS and season.

### 2.4.4 Potentially permanently abandoned and recultivated cropland area estimation

Keeping sample unit locations constant across seasons allowed us to compute time series-based area estimates for any temporal combination of annotations. Skakun et al. [7] demonstrated the prolonged nature of cropland abandonment: four years after the 2014 war, 46% of croplands were still abandoned
along the frontline in the Donbas region of Ukraine. Further away from frontlines, a significant proportion of previously abandoned cropland was recultivated. Yin et al. [6] obtained similar results after the two Chechen wars. We therefore hypothesized that, in high intensity wars, the longer cropland has been abandoned across conflict years, the more likely it is to stay permanently abandoned. In this study, we estimated, as of 2024, the potential bounds of permanently abandoned cropland in Ukraine. We defined three potential values for permanently abandoned cropland area in Ukraine (Eq. 7):

$$AA < (AA + AB23-24) < (AA + AB23-24 + AB24), \quad (7)$$

where *AA* are always abandoned sample units from 2022 to 2024, *AB23-24* are sample units abandoned only in 2023 and 2024 and *AB24* are sample units abandoned only in 2024. We then compute area estimates and standard errors for each combination of cultivation state, applying Eqs. 1-6.

Following the same concept, we estimate areas for cropland recultivated in 2023 after being abandoned in 2022 and for cropland recultivated in 2024 after being abandoned in 2023.

### 2.4.5 Accuracy assessment

We assessed the accuracy of our 2023 cultivation status map after merging sample units annotated as fallow or abandoned in 2023 into class non-cultivated, to match the map strata. We computed overall, user's and producer's accuracies and corresponding standard errors, according to Eqs. 1 to 7 in Olofsson et al. [31].

## 2.5 Random-effects panel regression modeling to relate conflict intensity and cultivation status in Ukraine

Yin et al. [6] compared logistic regression, fixed-effects, and random-effects panel regression models to examine how conflict was associated with cropland abandonment during the two Chechen wars. Building on this work, we adopted a similar framework but focused exclusively on random-effects panel regression (REPR) to analyze whether cropland abandonment and recultivation were more closely associated with conflict-related variables or with control covariates. We selected REPR for its ability to incorporate static covariates that may influence cultivation status and for its straightforward treatment of sample-unit inclusion probabilities. In contrast, fixed-effects models exclude time-invariant covariates, while implementing weighting is less direct in logistic regression than in REPR.

### 2.5.1 Model input variables

While conflict is knowingly linked to agricultural land use [2-7], factors such as land accessibility or climatic and edaphic conditions, may also influence cropland abandonment and recultivation. We therefore assessed the impact of 5 conflict co-variables on cropland cultivation dynamics. We additionally integrated a set of 9 control co-variables to account for potential confounders. A summary of all variables is given in Table 6. We did not integrate population census or displacement variables as they are incomplete due to the war [59].

<table><caption>Table 6. Input variables for quantifying the influence of conflict intensity on cropland abandonment and recultivation. Co-variables *a* to *e* are conflict related, and *f* to *m* are potential confounders.</caption><thead><tr><th>Variable</th><th>Type</th><th>Description</th><th>Period</th><th>Unit</th></tr></thead><tbody><tr><td rowspan="2">Response</td><td rowspan="2">Cultivation status</td><td>Abandoned</td><td>22-24</td><td>1: abandoned, 0: managed</td></tr><tr><td>Recultivated</td><td>22-24</td><td>1: recultivated, 0: abandoned</td></tr></tbody></table>
<table><tbody><tr><td rowspan="5">Conflict co-variables</td><td rowspan="4">Conflict intensity</td><td>a. Frontline distance</td><td>22-24</td><td>km</td></tr><tr><td>b. CP</td><td>22-24</td><td>Number</td></tr><tr><td>c. Craters 100 m (within AOI)</td><td>22</td><td>count</td></tr><tr><td>d. Craters 250 m (within AOI)</td><td>22</td><td>count</td></tr><tr><td>e. Craters 500 m (within AOI)</td><td>22</td><td>count</td></tr><tr><td rowspan="6">Control co-variables</td><td rowspan="4">Biophysical environment</td><td>f. Cumulative rainfall between. March and October</td><td>22-24</td><td>mm</td></tr><tr><td>g. Cumulative temperature between. March and October</td><td>22-24</td><td>K</td></tr><tr><td>h. Topsoil organic carbon content</td><td>static</td><td>%</td></tr><tr><td>i. Elevation</td><td>static</td><td>m</td></tr><tr><td rowspan="2">Accessibility</td><td>j. Slope</td><td>static</td><td>degree</td></tr><tr><td>k. Euclidean distance to closest road</td><td>static</td><td>m</td></tr><tr><td></td><td></td><td>l. Euclidean distance to closest settlement</td><td>static</td><td>m</td></tr><tr><td>Geo-location</td><td></td><td>m. Latitude, Longitude</td><td>static</td><td>m</td></tr></tbody></table>

The following section breaks down how variables *a* to *m* are constructed in Table 6:

a. Frontline distance

The Institute for the Study of War (ISW) and the American Enterprise Institute's Critical Threats Project (AEI) provide detailed, real-time analyses of the war in Ukraine, including frontline shifts, military movements, and strategic developments. ISW and AEI release daily frontline position updates in vector format, which we accessed upon request [60]. For this study, we used only the category “Assessed Russian-controlled Ukrainian territory” to delineate frontlines.

We selected vector files following the naming convention *UkraineControlMapAO22FEB2022*, covering the period from February 22, 2022, to November 20, 2024. This yielded 222, 338, and 268 frontline records for 2022, 2023, and 2024, respectively. Fig. 7 shows the temporal distribution of these files.

<figure><img src="image_9.png" /><figcaption>Figure SEQ Figure \* ARABIC 7.ISW and AEI frontline data availability per year</figcaption></figure>

For each ISW and AEI frontline *f*, we computed the Euclidean distance to each sample unit *u* based on territorial control. If *u* was located within Russian-controlled territory, we calculated the shortest distance between *u* and *f*. If *u* was within Ukrainian-controlled territory, we computed the minimum of the distances from *u* to either *f* or the northern border between Ukraine and Russia. Although ISW and AEI do not delineate a frontline along the northern border, the ACLED dataset indicates ongoing combat activity in that area (see Fig. 6B-C). All layers were projected to Lambers Equal Area for distance
computations. We then used, for each sample unit and per season, the median distance to frontlines as a proxy for conflict intensity, integrating both duration of exposure and proximity to conflict.

## b. Conflict proportion

For each sample unit *u* we extracted the yearly CP value, computed at the 10 \* 10km grid level in Section 2.3.1, as a yearly descriptor of the number of conflict events occurring in the vicinity of a sample unit.

## c – e. Craters 100, 250 and 500 meters

Duncan et al. [17] used very high resolution Maxar imagery and a U-net segmentation model to map artillery craters along the frontlines of Ukraine. Between April 2022 to February 2023, they mapped 3.8 ± 0.6 million craters (± Standard Error) inside the region colored in blue in Fig. 8 [16]. Centroids of detected craters were made available to this study. The gap between the two regions of coverage is due to the presence of the Kakhovka reservoir. For each sample unit within the study region in blue, we counted the number of craters in a 100-, 250- and 500-meter radius.

<figure><img src="image_10.png" /><figcaption>Figure 8. Crater detection spatial bounds</figcaption></figure>

## f – j. Biophysical environment variables

To account for eventual weather drivers in land use change decisions, we computed the yearly cumulative rainfall (in mm) and temperature (in degrees Kelvin) from March 1st to October 31st, the main season during which cropland is active in Ukraine (see Fig. 1). Data were extracted from the Copernicus Ag ERA 5 dataset [61]. In line with Yin et al. [6] we additionally integrated static variables elevation (in meters), slope (in degrees) extracted from the Shuttle Radar Topography Mission 30-meter digital elevation model [62] and the topsoil organic carbon content (in %) from the Soil Grids 250m v2.0 [63].

## k – m. Accessibility and geolocation variables

Further in line with Yin et al. [6], we integrated fixed accessibility and geolocation variables. We computed the Euclidean distance (in meters) between each sample unit and (i) the closest road from the Global Roads Inventory Project [64] and (ii) the closest settlement from the World Settlement Footprint dataset [65]. We additionally added latitude and longitude (in meters) for each sample unit.
### 2.5.2 REPR model implementation

Because our sample units came from stratified random samples, raw data could not be used directly without accounting for unequal inclusion probabilities. Ignoring this would overrepresent strata with higher sampling intensity and underrepresent those with fewer samples. To correct for this, we applied weights based on inclusion probabilities, ensuring each unit reflected its true share of Ukraine's agricultural landscape. A sample unit's Ukraine level inclusion probability, denoted $\hat{w}_i$, was computed according to Eq. 8:

$$ \hat{w}_i = W_{CIS_i} * \frac{\hat{A}_i}{\hat{A}_{CIS_i}}, \qquad (8) $$

where $W_{CIS_i}$ is the weight of the CIS of i in full Ukraine (see Table 5), $\hat{A}_i$ is the area estimated with unbiased estimators of i and $\hat{A}_{CIS_i}$ is the total area of the CIS to which i belongs to.

After filtering out all sample units that were constantly non-cropland, we fitted 7 REPR models, applying sample unit weights. Out of 1098 cropland sample units, 10 more were discarded because no soil organic carbon measurement was available due to the proximity to built up areas. We separately tested the effect of each conflict co-variable, while systematically using all control co-variables. Models are described in Table 7. For models I and II, we used all datapoints that were managed or abandoned at any point between 2022 and 2024. For models III to V, we used all sample units that fell within the crater detection spatial bounds. For models VI and VII, we only used sample units that were abandoned then managed again in the 2022 to 2024 period. The total number of sample units in each model is indicated in the rightmost column in Table 7.

<caption>Table 7. Description of models used in this study.</caption>

Response variables are highlighted in green, the study region for the model is highlighted in orange, conflict and control co-variables are highlighted in gray. Models are numbered I to VI and the number of sample units utilized in each model is noted in the last column. Table formatting is inspired from Yin et al. [6].

<table><thead><tr><th>M</th><th>A</th><th>R</th><th>F</th><th>C</th><th>C</th><th>C</th><th>S</th><th>E</th><th>S</th><th>D</th><th>D</th><th>L</th><th>F</th><th>C</th><th>C</th><th>C</th><th>C</th><th>N</th></tr></thead><tbody><tr><td>o</td><td>b</td><td>e</td><td>u</td><td>r</td><td>u</td><td>u</td><td>o</td><td>l</td><td>l</td><td>i</td><td>i</td><td>a</td><td>r</td><td>P</td><td>r</td><td>r</td><td>r</td><td>u</td></tr><tr><td>d</td><td>a</td><td>c</td><td>l</td><td>a</td><td>m</td><td>m</td><td>i</td><td>e</td><td>o</td><td>s</td><td>s</td><td>t</td><td>o</td><td></td><td>a</td><td>a</td><td>a</td><td>m</td></tr><tr><td>e</td><td>n</td><td>u</td><td>l</td><td>t</td><td>u</td><td>u</td><td>l</td><td>v</td><td>p</td><td>t</td><td>t</td><td>,</td><td>n</td><td></td><td>t</td><td>t</td><td>t</td><td>b</td></tr><tr><td>l</td><td>d</td><td>l</td><td>r</td><td>e</td><td>l</td><td>l</td><td>O</td><td>a</td><td>e</td><td>.</td><td>.</td><td>L</td><td>t</td><td></td><td>e</td><td>e</td><td>e</td><td>e</td></tr><tr><td>#</td><td>o</td><td>t</td><td>e</td><td>r</td><td>a</td><td>a</td><td>r</td><td>t</td><td></td><td>T</td><td>T</td><td>o</td><td>l</td><td>r</td><td>r</td><td>r</td><td>r</td><td></td></tr><tr><td>n</td><td>i</td><td>g</td><td>s</td><td>t</td><td>t</td><td>g</td><td>i</td><td></td><td>o</td><td>o</td><td>n</td><td>i</td><td></td><td>s</td><td>s</td><td>s</td><td>o</td><td></td></tr><tr><td>m</td><td>v</td><td>i</td><td>A</td><td>i</td><td>v</td><td>a</td><td>o</td><td></td><td>r</td><td>S</td><td></td><td></td><td>n</td><td>1</td><td>2</td><td>5</td><td>f</td><td></td></tr><tr><td>e</td><td>a</td><td>o</td><td>O</td><td>v</td><td>e</td><td>n</td><td>n</td><td></td><td>o</td><td>e</td><td></td><td></td><td>t</td><td>0</td><td>5</td><td>0</td><td>S</td><td></td></tr><tr><td>n</td><td>t</td><td>n</td><td>I</td><td>e</td><td>t</td><td>i</td><td></td><td></td><td>a</td><td>t</td><td></td><td></td><td>d</td><td>0</td><td>0</td><td>0</td><td>a</td><td></td></tr><tr><td>t</td><td>i</td><td></td><td></td><td>r</td><td>e</td><td>c</td><td></td><td></td><td>d</td><td>t</td><td></td><td></td><td>i</td><td></td><td>m</td><td>m</td><td>m</td><td>m</td></tr><tr><td>o</td><td></td><td></td><td></td><td>a</td><td>m</td><td>C</td><td></td><td></td><td></td><td>l</td><td></td><td></td><td>s</td><td></td><td></td><td></td><td></td><td>p</td></tr><tr><td>n</td><td></td><td></td><td></td><td>i</td><td>p</td><td>a</td><td></td><td></td><td></td><td>e</td><td></td><td></td><td>t</td><td></td><td></td><td></td><td></td><td>le</td></tr><tr><td></td><td></td><td></td><td></td><td>n</td><td>e</td><td>r</td><td></td><td></td><td></td><td>m</td><td></td><td></td><td>a</td><td></td><td></td><td></td><td></td><td>U</td></tr><tr><td></td><td></td><td></td><td></td><td>f</td><td>r</td><td>b</td><td></td><td></td><td></td><td>e</td><td></td><td></td><td>n</td><td></td><td></td><td></td><td></td><td>n</td></tr><tr><td></td><td></td><td></td><td></td><td>a</td><td>a</td><td>o</td><td></td><td></td><td></td><td>n</td><td></td><td></td><td>c</td><td></td><td></td><td></td><td></td><td>it</td></tr><tr><td></td><td></td><td></td><td></td><td>l</td><td>t</td><td>n</td><td></td><td></td><td></td><td>t</td><td></td><td></td><td>e</td><td></td><td></td><td></td><td></td><td>s</td></tr><tr><td></td><td></td><td></td><td></td><td>l</td><td>u</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td><td></td><td>r</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td></td><td></td><td></td><td>e</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<table><tbody><tr><td>I</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1088</td></tr><tr><td>II</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1088</td></tr><tr><td>III</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>354</td></tr><tr><td>IV</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>354</td></tr></tbody></table>
<figure><img src="image_11.png" /></figure>

For each model we reported coefficients and their two-tail p-values. Coefficients indicate the change in output (e.g. abandonment) when the predictor changes by 1 unit over time, given other confounders. When two-tail p-values are below 0.05, the predictor has a significant effect on the outcome at 95% significance.

### 2.5.3 Graphical analysis

We complemented the REPR analysis with a graphical approach: we grouped sample unit annotations from 2022 to 2024 into six cultivation patterns, described in Table 8.

We represented, for each cultivation pattern, the yearly weighted average of the median distances to the frontline in a time series plot.

We then assessed the relation between the number of craters in the surroundings of a sample unit and always abandoned, always managed, and recultivated in 2023 cultivation pattern. We did not process other cultivation pattern since ordnance craters were mainly detected in 2022. We removed sample units outside of the analysis footprint and counted the number of detected craters in 100, 250, and 500 meter radius buffers for residual sample units. We represented crater counts per cultivation pattern and per buffer radius in boxplots.

<table><caption>Table 8. Cultivation pattern name and description</caption><thead><tr><td>Cultivation pattern</td><td>Description</td></tr></thead><tbody><tr><td>Always Managed</td><td>Has been cultivated or fallow in 2022 and 2023 and 2024</td></tr><tr><td>Always Abandoned</td><td>Has been abandoned in 2022 and 2023 and 2024</td></tr><tr><td>AB23-24</td><td>Has been abandoned in 2023 and 2024</td></tr><tr><td>AB24</td><td>Has been abandoned in 2024</td></tr><tr><td>REC23</td><td>Has been recultivated in 2023</td></tr><tr><td>REC24</td><td>Has been recultivated in 2024</td></tr></tbody></table>

## 3 Results

From here onward and throughout the paper, all uncertainties for area and accuracy estimates are expressed in terms of one standard error.

### 3.1 Cultivation status map and accuracy assessment – season 2023

To categorize a crop as fallow, as per the definitions we established, we required knowledge about timings of tillage, timing of harvest and previous season cultivation status. Mapping each of those elements separately is feasible [47, 66, 67], but tedious and may lead to compounding of model errors.

Instead, we mapped cultivation status at national scale for season 2023 in three classes – cultivated cropland, non-cultivated cropland (fallow + abandoned) and non-crop. Across CIS, overall accuracy was above 84%, driven by high producer's and user's accuracies (PA and UA) of the non-crop class. The non-cultivated cropland class mapping performance was worst in the no conflict CIS, with a UA and a PA of 27 ± 5% and 57 ± 12% and best in the constant conflict CIS with a UA and PA of 86 ± 3% and 66 ±
3% (Table 9). While higher map accuracy favors sampling efficiency in sample-based area estimation, low map accuracy does not compromise the unbiased nature of the area estimator [31, 68]. The unbiasedness of the estimator is guaranteed by the probability sample, where each sample unit has a known and non-zero inclusion probability of being randomly drawn. This property holds regardless of map accuracy, because area estimation relies on the reference values from the sample and not on the map labels themselves.

<table><caption>**Table 9. User's, producer's and overall accuracies (UA, PA, OA) and standard errors per CIS for season 2023**

Accuracies and standard errors are given in percentage.</caption><thead><tr><th>CIS</th><th>Class</th><th>UA ± SE</th><th>PA ± SE</th><th>OA ± SE</th></tr></thead><tbody><tr><td rowspan="3">No conflict</td><td>Non crop</td><td>89 ± 2</td><td>96 ± 1</td><td></td></tr><tr><td>Non-cultivated</td><td>27 ± 5</td><td>57 ± 12</td><td>87 ± 1</td></tr><tr><td>Cultivated</td><td>94 ± 2</td><td>80 ± 2</td><td></td></tr><tr><td rowspan="3">Constant conflict</td><td>Non crop</td><td>95 ± 1</td><td>96 ± 1</td><td></td></tr><tr><td>Non-cultivated</td><td>86 ± 3</td><td>66 ± 3</td><td>85 ± 1</td></tr><tr><td>Cultivated</td><td>60 ± 4</td><td>83 ± 4</td><td></td></tr><tr><td rowspan="3">Increasing conflict</td><td>Non crop</td><td>93 ± 2</td><td>96 ± 1</td><td></td></tr><tr><td>Non-cultivated</td><td>73 ± 4</td><td>52 ± 3</td><td>84 ± 2</td></tr><tr><td>Cultivated</td><td>76 ± 3</td><td>86 ± 2</td><td></td></tr><tr><td rowspan="3">Decreasing conflict</td><td>Non crop</td><td>95 ± 1</td><td>97 ± 1</td><td></td></tr><tr><td>Non-cultivated</td><td>71 ± 4</td><td>56 ± 4</td><td>84 ± 2</td></tr><tr><td>Cultivated</td><td>75 ± 4</td><td>83 ± 2</td><td></td></tr></tbody></table>

At national level, most non-cultivated croplands were mapped along the frontline, corresponding to abandoned croplands in high conflict intensity regions (Fig. 9A). Afar from the front, we detected often mis-classified non-cultivated plots, with two main error sources: (i) grassy paths between fields matched the temporal signature of abandoned, weeded-in, fields and (ii) winter crops with low vegetative activity were mistaken with bare soils (Fig. 9B). Along the front, classification accuracy improved, since most croplands were abandoned and matched the typical, grassland-like temporal signature (Fig. 9C).
<figure><img src="image_12.png" /><figcaption>**Figure 9.** 2023 cultivation status map
A. National-scale cultivation status map. B. Zoom-in in Government controlled Ukraine, far from frontlines. C. Zoom-in along the 2022-2023 frontline.</figcaption></figure>

**3.2 National scale cultivated, fallow, and abandoned area estimates**

Using unbiased estimators of area and remote sensing image time series interpretation for reference data
collection, we estimated national-scale areas of cultivated, fallow, and abandoned cropland in Ukraine,
from 2021 to 2024. For the baseline season 2021, we estimated the cultivated cropland area to be 29.685
± 0.634 Mha, and the national fallow area to be 0.854 ± 0.305 Mha. After the onset of the war, cultivated
cropland areas decreased to 27.608 ± 0.749 Mha in 2022, then remained stable through 2024 (27.135 ±
0.797 Mha). The loss of active cropland translated to ever increasing abandoned areas: from 1.684 ±
0.163 Mha in 2022 to 2.213 ± 0.256 Mha in 2024. The fallow cropland area doubled as compared to
pre-war levels with 1.658 ± 0.4 Mha in 2022, then fluctuated between 1.099 ± 0.256 Mha in 2023 and
1.773 ± 0.391 Mha in 2024 (Table 10). This study focuses on the impacts of the war from 2022 onwards
on cultivation dynamics. To isolate these effects, we labeled all sample units within croplands abandoned
since 2014 as non-cropland, and therefore did not estimate an abandoned area for 2021. We, however,
acknowledge that all croplands along the 2014 frontline are not yet recultivated [7].

Detailed cultivated, fallow, and abandoned area estimates and uncertainties are presented in Table 10.

<caption>**Table 10.** Total Ukraine area estimates and corresponding standard errors per cultivation status and year.</caption>
<table><caption>Areas ± standard errors are reported in Mha.</caption><thead><tr><th>CIS</th><th>Class</th><th>2021</th><th>2022</th><th>2023</th><th>2024</th></tr></thead><tbody><tr><td rowspan="4">Total Ukraine</td><td>Cultivated</td><td>29.685 ± 0.634</td><td>27.608 ± 0.749</td><td>27.754 ± 0.764</td><td>27.135 ± 0.797</td></tr><tr><td>Fallow</td><td>0.854 ± 0.305</td><td>1.658 ± 0.4</td><td>1.099 ± 0.256</td><td>1.773 ± 0.391</td></tr><tr><td>Abandoned</td><td>0 ± 0</td><td>1.684 ± 0.163</td><td>2.142 ± 0.218</td><td>2.213 ± 0.256</td></tr><tr><td>Total</td><td>30.538 ± 0.704</td><td>30.953 ± 0.864</td><td>30.995 ± 0.831</td><td>31.121 ± 0.92</td></tr></tbody></table>

### 3.3 CIS level area dynamics of cultivated fallow and abandoned cropland

Fig. 10 displays areas of cultivated, abandoned, and fallow cropland relative to the total cropland area per CIS and per season, from 2021 to 2024. For reference $W_{CIS}$, the weight of the CIS area within the total Ukraine area, is indicated in plot titles. Absolute areas and standard errors per CIS, season, and cultivation status are presented in Table 11. Across CIS, the total cropland area is mostly stable, with slight increases in cropland due to cultivation in previously non-crop areas.

<figure><img src="image_13.png" /><figcaption>**Figure 10.** Relative areas under cultivation, fallow, and abandonment for each CIS from 2021 to 2024.

Lines represent trends over time, with error bars indicating standard errors. Point annotations indicate rounded relative values: placed to the right for cultivated and abandoned areas, and to the left for fallow. For reference $W_{CIS}$, the weight of the CIS area within the total Ukraine area, is indicated in plot titles.</figcaption></figure>
<table><caption>Table 11. Per CIS area estimates and corresponding standard errors per cultivation status and year</caption><thead><tr><th>CIS</th><th>Class</th><th>2021</th><th>2022</th><th>2023</th><th>2024</th></tr></thead><tbody><tr><td rowspan="4">No conflict</td><td>Cultivated</td><td>26.099 ± 0.633</td><td>25.512 ± 0.746</td><td>25.837 ± 0.761</td><td>25.197 ± 0.794</td></tr><tr><td>Fallow</td><td>0.758 ± 0.305</td><td>1.462 ± 0.398</td><td>0.884 ± 0.254</td><td>1.44 ± 0.389</td></tr><tr><td>Abandoned</td><td>0 ± 0</td><td>0.295 ± 0.15</td><td>0.59 ± 0.21</td><td>0.8 ± 0.249</td></tr><tr><td><em>Total</em></td><td>26.858 ± 0.703</td><td>27.269 ± 0.859</td><td>27.311 ± 0.827</td><td>27.437 ± 0.915</td></tr><tr><td rowspan="4">Constant conflict</td><td>Cultivated</td><td>1.281 ± 0.028</td><td>0.583 ± 0.04</td><td>0.474 ± 0.035</td><td>0.426 ± 0.037</td></tr><tr><td>Fallow</td><td>0.032 ± 0.013</td><td>0.058 ± 0.017</td><td>0.068 ± 0.018</td><td>0.095 ± 0.021</td></tr><tr><td>Abandoned</td><td>0 ± 0</td><td>0.669 ± 0.041</td><td>0.771 ± 0.038</td><td>0.792 ± 0.039</td></tr><tr><td><em>Total</em></td><td>1.313 ± 0.031</td><td>1.313 ± 0.058</td><td>1.313 ± 0.052</td><td>1.313 ± 0.055</td></tr><tr><td rowspan="4">Increasing conflict</td><td>Cultivated</td><td>0.768 ± 0.016</td><td>0.591 ± 0.021</td><td>0.47 ± 0.021</td><td>0.449 ± 0.023</td></tr><tr><td>Fallow</td><td>0.018 ± 0.007</td><td>0.052 ± 0.012</td><td>0.045 ± 0.011</td><td>0.057 ± 0.012</td></tr><tr><td>Abandoned</td><td>0 ± 0</td><td>0.145 ± 0.018</td><td>0.274 ± 0.021</td><td>0.284 ± 0.022</td></tr><tr><td><em>Total</em></td><td>0.785 ± 0.018</td><td>0.789 ± 0.029</td><td>0.789 ± 0.031</td><td>0.789 ± 0.032</td></tr><tr><td rowspan="4">Decreasing conflict</td><td>Cultivated</td><td>1.536 ± 0.026</td><td>0.922 ± 0.048</td><td>0.973 ± 0.046</td><td>1.063 ± 0.046</td></tr><tr><td>Fallow</td><td>0.046 ± 0.016</td><td>0.085 ± 0.023</td><td>0.102 ± 0.023</td><td>0.181 ± 0.032</td></tr><tr><td>Abandoned</td><td>0 ± 0</td><td>0.575 ± 0.045</td><td>0.508 ± 0.039</td><td>0.337 ± 0.033</td></tr><tr><td><em>Total</em></td><td>1.582 ± 0.03</td><td>1.582 ± 0.068</td><td>1.582 ± 0.062</td><td>1.582 ± 0.063</td></tr></tbody></table>

In the baseline season of 2021, more than 97% of cropland across all CIS was cultivated, with an additional 2-3% used for fallow. However, as conflict intensity escalated, the dynamics of cultivated, abandoned, and fallow areas shifted. Higher conflict intensity led to a reduction in cultivated areas and an increase in abandonment. Between 2022 and 2024, cultivated and abandoned areas followed reverse trends across stable, increasing, and decreasing conflict zones (Fig. 10).

In the no-conflict CIS, which comprises 88% of Ukrainian territory, cultivation was minimally disrupted during the conflict. From 2021 to 2024, cultivated areas declined from 97% to 92% of total cropland, while fallow and abandoned areas increased from 3% to 5% and 0% to 3%, respectively (Fig. 10A). In absolute terms, 26.099 ± 0.633 Mha were cultivated, and 0.758 ± 0.305 Mha were fallow in 2021. By 2024, cultivated areas shrank to 25.197 ± 0.794 Mha, while fallow land expanded to 1.44 ± 0.389 Mha, and abandoned cropland reached 0.8 ± 0.249 Mha (Table 11).

The stable conflict CIS, which covers 4.5% of Ukraine, saw the most significant reduction in cultivated area. By 2022, only 44% of cropland was cultivated, and by 2024, this dropped further to 32%. Abandoned areas increased dramatically, representing 51%, 59%, and 60% of total cropland from 2022 to 2024 (Fig. 10B). In 2021, 1.281 ± 0.028 Mha were cultivated, but by 2024, this reduced to 0.426 ± 0.037 Mha. Meanwhile, abandoned cropland rose from 0.669 ± 0.041 Mha to 0.792 ± 0.039 Mha, and fallow land increased from 0.032 ± 0.013 Mha (3%) to 0.095 ± 0.021 Mha (5%) (Table 11).

In the increasing conflict CIS, which accounts for 2.5% of Ukraine's territory, cultivated areas decreased from 98% to 75% between 2021 and 2022, further decreasing to 60% in 2023 and 57% in 2024. Abandoned areas followed a similar trend, initially lower but then increasing from 18% in 2022 to 36% in 2024. Fallow areas also rose from 2% to 7% over the same period (Fig. 10C). In absolute terms, 0.768 ± 0.016 Mha were cultivated in 2021, but by 2024, only 0.449 ± 0.023 Mha remained cultivated.
Abandoned areas doubled between 2022 and 2024, from 0.145 ± 0.018 Mha to 0.284 ± 0.022 Mha, while fallow areas increased from 0.018 ± 0.007 Mha to 0.057 ± 0.012 Mha (Table 11).

The decreasing conflict CIS, which covers 5% of Ukrainian territory and consists mainly of lands reclaimed by government forces by late 2022, showed the most significant recovery in cultivation. While cultivated areas dropped from 97% to 58% between 2021 and 2022, they began to rebound from 2023 onward, reaching 62% in 2023 and 67% in 2024. Fallow areas increased from 6% to 11% between 2023 and 2024, indicating that while these areas were not yet contributing to production, they were being maintained or restored for future agricultural use. Abandoned areas, which peaked at 36% of total cropland in 2022, dropped to 21% by 2024 (Fig. 10D). By 2024, 1.063 ± 0.046 Mha were cultivated, 0.181 ± 0.032 Mha were fallow, and 0.337 ± 0.033 Mha were abandoned, compared to 0.922 ± 0.048 Mha, 0.085 ± 0.023 Mha, and 0.575 ± 0.045 Mha in 2022 (Table 11).

### 3.4 Conflict intensity as a driver of cropland abandonment and recultivation

We fitted seven REPR models to examine the associations of conflict-related and control covariates with cropland abandonment and recultivation (Table 12). In models I–V, managed croplands were coded as 0 and abandoned croplands as 1, whereas in models VI–VII, abandoned croplands were coded as 0 and recultivated croplands as 1. Coefficients indicate both the magnitude and direction of the estimated association between each covariate and the probability of transition. For example, in model I, a one-degree increase in longitude is associated with a 2.53% higher probability of cropland transitioning from managed to abandoned, significant at the 99.9% confidence level.

<table><caption>Table 12. REPR coefficients in percent and significance levels</caption><caption>Stars denote the two-tailed P-value significance levels: *** P ≤ 0.001, ** P ≤ 0.01, * ≤ 0.05, coefficients with P > 0.05 are not represented in the table.</caption><thead><tr><th>Model</th><th>I</th><th>II</th><th>III</th><th>IV</th><th>V</th><th>VI</th><th>VII</th><th>Unit</th></tr></thead><tbody><tr><td>Abandonment</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Recultivation</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Full Region</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Craters AOI</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Cumulative rainfall</td><td></td><td></td><td>0.02*</td><td></td><td></td><td>-0.24***</td><td>-0.22***</td><td>mm</td></tr><tr><td>Cumulative temperature</td><td></td><td>&lt; 0.01***</td><td>&lt; 0.01*</td><td></td><td></td><td>0.01**</td><td>0.01***</td><td>degree K</td></tr><tr><td>Soil Organic Carbon</td><td></td><td></td><td></td><td></td><td></td><td>0.07*</td><td>0.11**</td><td>%</td></tr><tr><td>Elevation</td><td>0.05***</td><td>0.16***</td><td></td><td></td><td></td><td>0.25*</td><td>0.34**</td><td>m</td></tr><tr><td>Slope</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>degree K</td></tr><tr><td>Dist. To road</td><td></td><td></td><td></td><td></td><td></td><td>0.01**</td><td>&lt; 0.01*</td><td>m</td></tr><tr><td>Dist. To Settlement</td><td>&lt; 0.01*</td><td></td><td></td><td></td><td></td><td></td><td></td><td>m</td></tr><tr><td>Latitude</td><td>-1.37**</td><td>-2.89***</td><td></td><td></td><td></td><td></td><td>-12.23*</td><td>degree K</td></tr><tr><td>Longitude</td><td>2.53***</td><td></td><td></td><td></td><td></td><td>-11.72***</td><td>-12.69***</td><td>degree K</td></tr><tr><td>Frontline distance</td><td>1.94***</td><td></td><td></td><td></td><td></td><td>-3.04***</td><td></td><td>km</td></tr><tr><td>CP</td><td></td><td>-0.13***</td><td></td><td></td><td></td><td></td><td>0.47***</td><td>number</td></tr><tr><td>Craters 100m</td><td></td><td></td><td>3.7***</td><td></td><td></td><td></td><td></td><td>count</td></tr><tr><td>Craters 250 m</td><td></td><td></td><td></td><td>0.59***</td><td></td><td></td><td></td><td>count</td></tr><tr><td>Craters 500m</td><td></td><td></td><td></td><td></td><td>0.16***</td><td></td><td></td><td>count</td></tr></tbody></table>
Across all seven REPR models, conflict-related variables showed a consistent and systematic association with cropland abandonment and recultivation. In abandonment models (I–V), proximity to conflict was linked to higher likelihoods of cropland transitioning from managed to abandoned. Specifically, abandonment was more likely in areas closer to the frontline (+1.94% per km decrease in distance, p ≤ 0.001), in zones with greater conflict exposure (-0.13% per unit increase, p ≤ 0.001), and in locations affected by explosive craters. The association with craters was scale-dependent, with a +3.7% increase in abandonment per additional crater within 100 m, +0.59% within 250 m, and +0.16% within 500 m (all p ≤ 0.001).

By contrast, in recultivation models (VI–VII), conflict variables were systematically linked to lower probabilities of cropland recovery. A decrease in distance from the frontline was associated with a reduced likelihood of recultivation (-3.04% per km, p ≤ 0.001), while higher local conflict exposure was also associated with reduced recultivation (-0.47% per unit, p ≤ 0.001).

Control variables, including rainfall, temperature, soil organic carbon, elevation, slope, and distance to infrastructure, often showed context-specific effects. For example, higher rainfall was associated with slightly higher abandonment (+0.02%, p ≤ 0.05) and lower recultivation (-0.22 to -0.24%, p ≤ 0.001), while temperature consistently showed small but positive associations across models.

Geographic gradients were also evident in models I, II, VI, and VII. In models I and II, each degree northward, and in model I each degree eastward, was associated with higher probabilities of abandonment. With the southwest corner of Ukraine as the geographic reference (minimum latitude and longitude), movement along a northeast azimuth corresponds to closing in on areas of active combat and where fighting has been most persistent. For recultivation models, each degree northward and eastward was strongly associated with lower likelihoods of recultivation (coefficients exceeding 11%). These spatial trends correspond to the frontline dynamics depicted in Fig. 11. During sample annotation, most recultivated fields were observed in the Zaporizhzhia and Kherson oblasts (highlighted in the blue box in Fig. 11), located in the South of Ukraine (blue box), while most abandoned fields were further East and North, where frontlines became entrenched in 2023 and 2024.

<figure><img src="image_14.png" /><figcaption>Figure 11. Relative frequency of frontline presence per year and per 10\*10 km grid cell</figcaption></figure>

For each grid cell g and year y, the relative frequency equals the number of times a frontline intersected g in year y, divided by the total number of frontline records in y. Kyiv is marked with a light blue dot. The blue square represents the regions where most recultivated sample units were annotated.

We graphically assessed how yearly distance from the frontline and nearby crater counts were associated with cultivation status. Sample units that remained managed (Fig. 12a) consistently lay far from
frontlines, with weighted average distances near 200 km. By contrast, areas abandoned for two or more
consecutive years (Fig. 12e,f) were typically within 20 km of the frontline, aligning with the strike range
of heavy weaponry [17]. Newly abandoned areas in 2024 (Fig. 12d) shifted from >100 km in 2022–2023
to ~40 km in 2024, suggesting abandonment is spreading beyond immediate combat zones. Recultivated
fields were initially closer to conflict: those recultivated in 2023 averaged ~45 km from the frontline in
2022, while those recultivated in 2024 were ~20 km away, recovering only after frontlines retreated and
exposure declined (Fig. 12b,c).

<figure><img src="image_15.png" /><figcaption>**Figure 12. Yearly average of median distances to frontlines per cultivation pattern**
Black dots and lines represent the weighted average of median distances from the frontline; grey shaded hulls represent corresponding weighted standard errors. The dashed blue line represents the 20km distance mark from the frontline.</figcaption></figure>

To further assess combat exposure, we counted craters within 100, 250, and 500 m of each sample unit by
cultivation pattern (Fig. 13). Crater density increased with buffer size. Always-abandoned fields showed
the highest values (median = 152 at 500 m), followed by recultivated fields (median = 51). In contrast,
always-managed fields had very low crater densities (e.g., median = 1 at 100 m), consistent with their
location in less intensively shelled areas.
<figure><img src="image_16.png" /><figcaption>Figure 13. Boxplots of crater count in 100-, 250-, and 500-meter radii, per cultivation pattern</figcaption></figure>

Boxes show the interquartile range (25th–75th percentile, IQR); the line inside represents the median. Whiskers extend to 1.5 * IQR; points represent values beyond 1.5 * IQR. Annotations indicate 25 percentile and median crater count around sample units.

Taken together, these results indicate that while environmental and geographic factors provide important context, conflict exposure is consistently and strongly associated with cropland abandonment and with reduced likelihood of recultivation in Ukraine.

## 3.5 Permanently abandoned and recultivated areas

<table><caption>Table 13. Permanently abandoned and recultivated area estimates and standard errors per cultivation pattern</caption><thead><tr><td>Cultivation pattern</td><td>Description</td><td>Area (Mha) ± SE</td></tr></thead><tbody><tr><td>AA</td><td>Abandoned 3 years in a row, in 2022 and 2023 and 2024</td><td>1.121 ± 0.148</td></tr><tr><td>AB23-24</td><td>Previous + Abandoned 2 years in a row, in 2023 and 2024</td><td>1.671 ± 0.169</td></tr><tr><td>AB24</td><td>Abandoned for one year, in 2024</td><td>2.213 ± 0.256</td></tr><tr><td>REC23</td><td>Managed again in 2023</td><td>0.296 ± 0.056</td></tr><tr><td>REC24</td><td>Managed again in 2024</td><td>0.472 ± 0.143</td></tr></tbody></table>

Upon aggregating sample units to cultivation patterns, we estimate the plausible bounds of permanently abandoned cropland in Ukraine to lie between 1.121 ± 0.148 Mha (representing land abandoned three years in a row, from 2022 to 2024) and 1.671 ± 0.169 Mha (cropland abandoned in 2022, 2023 and 2024 and in 2023 and 2024) (Table 13). A higher estimate, 2.213 ± 0.256 Mha, corresponding to the total cropland abandoned in 2024, appears less reliable, as land newly abandoned in 2024 shows different spatial trends in relation to frontline proximity, making its long-term status more uncertain (see section 3.4).

Furthermore, 0.296 ± 0.056 Mha of cropland were abandoned in 2022 and managed again in 2023. Recultivated areas further expanded in 2024, with an additional 0.472 ± 0.143 Mha being brought back into managed cropland.
# 4 Discussion

In this study, we used satellite data and unbiased estimators of area described in Olofsson et al. [31], to estimate abandoned, fallow, cultivated and recultivated areas in Ukraine from 2021 to 2024.

Our results showed that, on a national scale, cultivated areas reduced by 8.5% between 2021 and 2024 and 7% of Ukraine's cropland was abandoned in 2024. In high conflict intensity regions, abandonment became entrenched with between $1.121 \pm 0.148$ Mha and $1.671 \pm 0.169$ Mha of cropland that may be long term lost to production. Multi-year abandonment was mostly observed in regions of sustained active combat across seasons (sample units closer to frontlines are more likely to be abandoned).

At the national scale, relative fallow areas increased from 3% in 2021 to 6% in 2024, reflecting farmer uncertainty and possible labor and resource shortages [69, 70]. However, in certain cases, fallowing crops was part of a multi-year recovery trajectory: $0.071 \pm 0.044$ Mha of cropland were abandoned in 2022, then fallow in 2023 and finally actively cultivated in 2024. We observed the same trend in the consecutive set of three seasons, with $0.268 \pm 0.141$ Mha of cropland abandoned in 2023, fallowed in 2024 and cultivated in 2025 (follow up annotations as of July 2025).

## 4.1 Policy implications

Our findings have direct relevance for ongoing agricultural recovery and land management policies in Ukraine, and support the first objective of the European Commission Ukraine facility, to “Support Ukraine's recovery, reconstruction and modernization” [71]. They intersect with recent legislative measures aimed at regulating land taxation, supporting farmers in conflict-affected areas, and incentivizing cultivation in hazardous zones.

By Law N°3050-4, landowners and tenants of land plots located within active conflict zones, temporarily occupied territories, areas contaminated with explosives or used for military purposes are exempted from three categories of land taxation. The Ministry of Agrarian Policy and Food of Ukraine (MAPFU) is responsible for monitoring the use of such lands and preventing misuse of this provision [72].

The Government further adopted Amendment N°964 to Article 20 of the Budget Code of Ukraine. This amendment stipulates that for each hectare sown in areas of potential or active hostilities, farmers may receive a premium of 1,000 UAH (US$ 24.01 at the October 10 2025 exchange rate). Additionally, for each hectare sown with cotton in Odesa, Mykolaiv, and Kherson oblasts, farmers are eligible for a premium of 10,000 UAH (US$ 240.10) [20].

The analytical framework developed in this paper has strong potential to support these policy mechanisms. By comparing declared abandoned areas (eligible for tax exemption) with remote sensing-based estimates, MAPFU can quantitatively assess whether abandonment is under- or over-reported. Likewise, by monitoring cultivated areas in conflict-affected regions, MAPFU could better track and verify the allocation of production premiums to farmers operating under hazardous conditions.

Our statistical and spatial analyses showed that proximity to active combat zones increases the probability of cropland abandonment, while higher crater density near sample units significantly reduces the likelihood of recultivation. Beyond the direct threat of UXO, Ukraine faces an acute landmine crisis [73]. Both Ukrainian and Russian forces have deployed mines extensively, making the country one of the most heavily mined in the world [15]. Restoring agricultural production on such lands will require large-scale clearance of mines and UXO. Even after clearance, soils will remain scarred by trenches and shell craters [74], compacted by heavy military machinery, and contaminated with elevated concentrations of heavy metals [75, 76].
Despite these challenges, clearance operations are advancing. Between 2022 and early 2025, approximately 1,500 HALO Trust deminers removed 36,000 explosive devices from 710 hectares [77]. As per the Ministry of Economy of Ukraine, in 2024 alone, demining operators inspected 0.318 Mha of cropland, of which 0.285 Mha were declared safe for recultivation [78]. Resolution N°284 commits the Ukrainian government to compensate up to 80% of demining costs for land cleared between February 24, 2022, and April 15, 2024. After this date, humanitarian demining operations are to be fully funded by the state. Eligible activities must be conducted by certified operators in territories free from active hostilities [21].

Yet, our analysis indicates that 0.296 ± 0.056 Mha and 0.472 ± 0.143 Mha of previously abandoned cropland were brought back into management in 2023 and 2024, respectively. Most of those crops were located in regions subject to combat in 2022, from where Russian soldiers retracted by the early 2023. Those figures far exceed official demining statistics (0.285 Mha declared safe in 2024, Ministry of Economy of Ukraine [78]). This discrepancy likely reflects widespread informal or self-organized demining. With institutional capacity unable to meet the growing demand, many farmers have taken clearance efforts into their own hands. For example, dairy farmer Oleksandr Kryytsov from Kharkiv Oblast constructed a remote-controlled armored tractor from tank parts to safely clear his fields [79]. According to Viktor Sheremeta, of the Association of Farmers and Private Landowners of Ukraine, approximately 10% of the group's 10,000 members have independently demined their land. A growing black market also offers faster and cheaper (but considerably riskier) demining services [80]. For instance, farmer Yurii Matsuita employed such sappers to clear 2,448 hectares; one tractor exploded during the process, though fortunately without casualties. Others have not been so fortunate [81].

It is likely that fields demined through black-market or self-organized efforts remain unrecorded in official humanitarian demining databases. As a result, some of these already cleared areas may still appear on priority lists for official clearance, slowing down the overall pace of relief operations. If we hypothesize that abandoned fields generally undergo either official or unofficial demining before being cultivated again, then mapping and estimating the extent of recultivated cropland can be a valuable tool to help (de)prioritize land plots in future humanitarian and reconstruction efforts.

## 4.2 Comparison to similar studies

Fig. 14 compares the results of this study, referred to as "NASA Harvest" (NH), to national-scale estimates from Dai et al. [24], Kussul et al. [19], and Zhang et al. [25]. For clarity, each estimate is referred to by the first author's name. We present both abandoned and non-cultivated cropland area estimates from NH (non-cultivated = abandoned + fallow).
<figure><img src="image_17.png" /><figcaption>**Figure 14.** Intercomparison of abandoned and non-cultivated area estimates with comparable studies in the literature
Areas are expressed in Mha, and detailed numbers are given in annotations</figcaption></figure>

In 2022, Zhang's estimate of 1.72 Mha of abandoned cropland closely aligns with NH's figure of 1.684 ± 0.163 Mha. However, this agreement diverges in 2023, when NH estimates 2.142 ± 0.218 Mha, while Zhang reports only 1.407 Mha. Dai reports substantially higher abandonment in 2022, approximately 0.66 Mha more than NH. Kussul's abandoned area estimate is systematically more than double the abandoned area estimated by NH, closer, but still 1 Mha larger at least, than NH's non-cultivated area estimate. Those inconsistencies between Zhang's, Dai's, and Kussul's estimates, as compared to our results, likely stem from the inclusion of managed fallow lands in area estimates and from the area estimation method itself.

For instance, using a stratified estimator, we estimated 3.241 ± 0.328 Mha of non-cultivated cropland in 2023, as compared to 4.518 Mha, with the pixel-counting estimator. This flawed estimate would have been closer to the abandoned area pixel counted by Kussul. We hypothesize that Kussul integrates both abandoned and fallow areas into their class “abandoned”, which, compounded with the usage of the pixel counting estimator, leads to their overestimation of real abandoned areas.

However, for Ukraine scale cultivated cropland, we found (i) high agreement between Kussul and NH area estimates from 2021 to 2024 (Fig. 15) and (ii) high agreement with official Ukrainian statistics in 2021, who estimated that 28.581 Mha were cultivated, compared to 29.685 ± 0.634 Mha for NH [19]. Since the onset of the full-scale war in 2022, official statistics only cover Government controlled
territories, not matching the full Ukraine extent of areas estimated in this paper. Therefore, further comparisons of NH cultivated areas against official statistics are not relevant.

<figure><img src="image_18.png" /><figcaption>Figure 15. NASA Harvest vs. Kussul cultivated cropland area estimates</figcaption></figure>

## 4.3 ACLED for stratification and conflict intensity assessment

In this paper, we used ACLED derived CIS and a cultivation status map as stratifiers for sample-based area estimation. A major advantage of sample-based area estimation over the conventionally used pixel-counting estimator is that stratifier errors do not propagate to the area estimate. An inaccurate stratifier only decreases the sampling efficiency and increases the uncertainty around the estimate, but does not affect the unbiasedness of the estimator [34]. In June 2025, the ACLED database was substantially updated, adding approximately 13,000 new events for February–December 2022 [50]. We recomputed CPC values for the 2022 season using this updated dataset (Fig. 16B) and compared it with values derived from the original version (Fig. 16A). Only 21 of 6,414 blocks changed their CPC category, indicating that the update had a negligible effect on the CIS spatial extents and on the stratification.

The CP values used as conflict covariates in the REPR analysis changed in 2022 for 26% of the grid cells, though most changes were minor (1,267 out of 1,710 block-level CP value changes were less than 1). Miller et al. [82] highlighted additional limitations of conflict event datasets, including omission errors (missed events), inflation errors (overreported events), and misrepresentation errors (misinformation affecting data collectors). To mitigate these potential biases in the REPR analysis, we complemented the ACLED-derived CP metrics with independent conflict proxies (proximity to frontlines and crater density) rather than relying solely on ACLED data. Regardless of which conflict indicator was used, all three variables consistently indicated that higher conflict intensity was associated with increased cropland abandonment and reduced likelihood of recultivation.

We acknowledge that incomplete ACLED coverage for 2022 constitutes a limitation, as some local events may have been underreported, potentially leading to slight underestimation of conflict intensity in certain areas. However, the negligible change in CIS and the convergence across independent conflict indicators in REPR models suggest that this limitation does not materially affect the study's conclusions. For
consistency, we retained the initial ACLED dataset version across all analyses and stratification procedures.

<figure><img src="image_19.png" /><figcaption>Figure 16. ACLED 2022 dataset update CPC comparison</figcaption></figure>

4.4 Advantages and limitations of remote sensing and sample-based area estimation

In this work, using the pixel counting estimator would have made it impossible to distinguish between fallow and abandoned land. Instead, using the map as a stratifier and collecting reference data with remote sensing imagery addressed several limitations simultaneously: (i) area estimates were based on unbiased estimators and uncertainties are quantified, (ii) fallow and abandoned areas were estimated separately, (iii) a single map served as a stratifier across multiple years, (iv) multi-year aggregated statistics were derived from a consistent sample frame, and (v) no additional mapping of past cultivation, tillage, or harvest events (criteria we used to distinguish fallow and abandoned land) was required.

While only field work can provide golden truth reference data [83], remote sensing imagery interpretation provides close approximations in conflict situations, where field data collection jeopardizes lives [2, 5, 7, 42]. Visually determining whether a crop was actively managed requires analyzing the full annual time series of remote sensing imagery to assign labels. Even with possible ground visits, replicating this approach would be labor-intensive. Because management interventions can occur at any point during the growing season, one would either need continuous monitoring of sample units, which is practically impossible, or rely on interviews with land tenants, which are prone to reporting errors and voluntary misreporting. Therefore, even in times of peace, remote sensing imagery interpretation would remain an adapted approach to track cultivation status.
To mitigate the risk of mis-labeling sample units, which can lead to flawed area estimates [84], two to three labelers annotated each sample unit independently. In case of disagreement in labels, the sample units were jointly reviewed until a consensus was reached on their final label. For future annotator's domain knowledge and capacity building, we share detailed annotation guidelines in Supplementary Materials Section 2, breaking down the main temporal signatures for cultivated, abandoned, and fallow croplands in Ukraine. We additionally made openly available sample unit locations, stratum, and class values for seasons 2021 to 2024.

A spatially explicit cultivation status map would enable the detection of fraudulent abandonment declarations, identifying undeclared cultivation in hazardous areas (eligible for premiums), and maintaining an updated inventory of recultivated fields, a useful proxy for monitoring humanitarian demining progress (see Section 4.1). At present, however, the cultivation status map is used only to inform the sampling design and is not delivered to end users, which represents a limitation of our framework.

To address this limitation, Song et al. [37] and Li et al. [36] proposed a method for generating spatially explicit maps consistent with statistically robust area estimates. Their approach first derives target land-use areas through sample-based estimation, then trains a supervised classification model (e.g., random forest) and adjusts decision thresholds so that pixel-counted areas align with sample-based estimates. The same principle could be applied to extend our framework beyond statistical inference. Once replicated, the annotated sample units could be repurposed to train a supervised model and reconcile sample-based and pixel-derived area estimates. Matching pixel counts with sample-based estimates should improve, but not guarantee, the overall and class-wise map accuracy. Therefore, before dissemination, class-wise user's and producer's accuracies should be evaluated with quantified uncertainties [31]. To this end, Stehman [85] provides formulae for updating map accuracy and uncertainty estimates using the existing stratified sample, eliminating the need for new stratification or re-annotation and thereby streamlining accuracy assessment in future mapping iterations. We plan on implementing this approach in future research.

# 5 Conclusion

We developed an operational workflow to track changes in cultivated, fallow, and abandoned cropland in war-affected Ukraine between 2021 and 2024. We used ACLED conflict data and a 2023 cultivation status map for stratified random sampling. Although our map only classified non-cultivated areas, the use of unbiased estimators allowed us to accurately estimate the extent of both fallow and abandoned croplands. Due to the challenges of ground data collection during the war, we annotated sample units through expert interpretation of remote sensing imagery. We analyzed how conflict intensity influenced patterns of cropland abandonment, cultivation, and recultivation over time, and qualitatively assessed the extent of potentially permanent cropland abandonment.

As of 2024, we estimate that between 1.121 ± 0.148 and 1.671 ± 0.169 Mha of cropland in Ukraine may be abandoned on the long term (non-cultivated for two or more consecutive years). These areas were subject to prolonged proximity to frontlines and heavy shelling. Abandoned croplands are often contaminated with UXO, landmines, chemicals, soil compaction, and trenches, requiring significant remediation efforts before they can be brought back into production.

In contrast, 0.296 ± 0.056 and 0.472 ± 0.143 Mha previously abandoned lands were recultivated in 2023 and 2024, mostly in areas retaken by Ukrainian forces and less intensively shelled. While these cases
highlight potential for recovery, they also point to untracked or informal demining activities, as recultivation has outpaced official clearance rates.

By combining unbiased estimators with expert image interpretation, our workflow offers statistically sound area estimates, as compared to pixel-counting. It enables cross-year tracking of land-use transitions and reveals complex dynamics, such as multi-year fallowing preceding recultivation, that are often missed. Our results underscore the importance of clear definitions: common confusion between fallow and abandoned land lead to significantly overstated cropland loss estimates and may misguide abandoned land tax exemptions and post-war recovery planning.
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## Acknowledgements

The authors thank Planet and the Institute for the Study of War and AEI’s Critical Threats Project for providing satellite imagery and conflict boundaries, for this research project. Planet two-week composites were shared with NASA Harvest through a Planet-NASA Harvest agreement. Planet daily data was accessed through the NASA CSDA program.

## Funding

This project was supported by funding from USIAS (grant entitled: *Utilisation des observations terrestres pour évaluer l'impact de la guerre sur la production agricole de l'Ukraine et éclairer les réponses politiques*), NASA Harvest (NASA cooperative agreement: 80NSSC23M0032 P00002), USAID (grant number: 80NSSC18K1571 and FAO (grant number: 720BHA22IO00188).

## Conflicts of Interest

The authors declare no conflict of interest
Author contributions

**J. Wagner**: Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Resources, Data Curation, Visualization, Writing – Original Draft, Writing – Review and Editing; Project administration; **S. Nair**: Conceptualization, Methodology, Formal Analysis, Investigation; **S. Skakun**: Conceptualization, Methodology, Formal Analysis, Investigation; Resources, Writing – Review and Editing, Supervision; **E. Duncan**: Conceptualization, Resources, Writing – Review and Editing; **F. Li**: Writing – Review and Editing; **O. Oliinyk**: Investigation, Writing – Review and Editing; **F. Nerry**: Supervision; **J. Rehbinder**: Supervision, Writing – Review and Editing; **I. Becker-Reshef**: Conceptualization, Formal Analysis, Investigation, Writing – Review and Editing, Supervision, Project Administration, Funding Acquisition.