Andean agriculture faces several challenges, such as land use changes, land degradation, poverty, extreme events, and climate change. Such conditions compromise food production and security, highlighting the need to explore sustainable alternatives. This two-trial study evaluated the short-term performance of regenerative agricultural practices for potato production in the Peruvian Andes over two seasons (2022-2024), in terms of productivity (FTY: Fresh tuber yield), profitability (BCR: Benefit-cost ratio), C footprint (CF), and soil properties. One trial (Trial 1) tested tillage practices-minimum (MT) vs. zero (ZT); plastic barriers-with (wPB) vs. without (nPB); and mulch thicknesses-0.1 (M10) vs. 0.2 (M20) vs. 0.3 (M30) m. The other trial (Trial 2) tested cropping systems-monoculture (MO) vs. intercropping with faba bean (IN); a fungicide optimization tool-with (wDK) vs. without (nDK); and chicken manure doses-1 (CM1) vs. 2 (CM2) vs. 4 (CM4) t ha(-)(1) . Compared to conventional practices, numerically MT+nPB+M30 increased FTY by similar to 3.5 % and reduced CF by 19.9 % in Trial 1, while MO+wDK+CM4 increased FTY and BCR by 28 % and 12.4 %, respectively, in Trial 2. ZT and IN performed poorly in the short-term under Andean conditions, highlighting the need for long-term studies. In both trials, the short-term effect of regenerative practices improved soil organic matter with a mixed impact on pH. Regenerative practices in the Andes offer synergies and trade-offs, but integrating reduced tillage, mulching, and organic fertilization can enhance sustainability without lowering productivity. Long-term adoption is essential to restore soil carbon stocks, improve sustainability, and increase the resilience of Andean agriculture.

Abstract Potato ( Solanum tuberosum L.) is a staple crop crucial to global food security, yet its production is severely threatened by late blight (LB), caused by Phytophthora infestans , one of the most destructive plant diseases worldwide. Breeding programs for LB resistance have traditionally relied on labor-intensive and subjective visual assessments, which limit scalability and consistency, particularly in early-generation trials. Unmanned aerial vehicle (UAV)-based remote sensing combined with machine learning (ML) offers a promising alternative for objective, high-throughput disease phenotyping. This study evaluated the potential of UAV-derived multispectral imagery and ML techniques to estimate LB severity across large and genetically diverse potato breeding populations, comprising 2,745 clones in one trial and 492 accessions in another, conducted in Oxapampa, Pasco, Peru. We compared vegetation index–based approaches with a machine learning framework that integrates K-means clustering and Kernel Ridge Regression (KRR) and assessed their ability to capture genotypic variation and support selection decisions. NDVI consistently showed a strong correlation with visually assessed LB severity, particularly at advanced stages of disease development, enabling objective discrimination between healthy and diseased canopy tissues. However, the KRR-based approach outperformed linear NDVI-based models by capturing nonlinear relationships between spectral responses and disease progression. Estimates of LB severity derived from NDVI and KRR models, expressed as best linear unbiased estimates (BLUEs), showed strong and biologically consistent relationships with the area under the disease progress curve (AUDPC), particularly during later UAV acquisitions. Selection coincidence between UAV-derived estimates and AUDPC-based rankings was substantially higher at intermediate to advanced stages of disease progression, suggesting that UAV assessments at these stages may capture sufficient phenotypic variation to distinguish genotypes. These findings indicate that UAV-based multispectral phenotyping, especially when integrated with ML, provides a practical and scalable approach for assessing LB severity in potato breeding programs while reducing the need for time-consuming field evaluations.

Soil organic carbon (SOC) is a crucial component related to various processes that ensure soil health and function. Its modeling is vital for assessing and monitoring soil degradation caused by the potential impact of agricultural activities. This study aimed to model SOC in the Northern highlands of Peru, characterized by a high amount of SOC, which is being affected by crop expansion. Crop rotation (CR) was incorporated into a modeling exercise using remote sensing data, fieldwork, and farmer surveys. A multi-year classification model with seven cropland classes was developed using data collected from 534 fields across 2022-2024, including 189 soil samples. Each cropland field was represented as a polygon delineating its boundaries and indicating its dominant crop cover. Time series of multispectral Sentinel-2 Level-2A Top of Canopy imagery were used to derive phenological features—such as the timing of maximum canopy cover and the length of the growing period—based on Normalized Difference Vegetation Index (NDVI) time series. A Random Forest classifier was used as the baseline model. The cropland classification model demonstrated strong overall performance, with F 1 scores ranging from 0.81 to 0.98 across the different classes. The model performed well for lupin and pasture but scored lower for beans and potatoes. Predictions of cropland classes from 2019 to 2022 were created, resulting in frequency layers that represent crop rotations. Four feature configurations were evaluated: (i) including all features as a benchmark, (ii) excluding climatology, (iii) excluding crop rotation history, and (iv) excluding soil properties. Configurations including all features and excluding crop rotation history showed the highest performance ( R 2 = 0.63), while those excluding climatology or soil properties performed worse ( R 2 ≈ 0.52–0.53). Although soil features were the most important, fallow frequency emerged as the most critical predictor of SOC in crop rotations. When soil data were excluded, fallow frequency, combined with climatic features, explained over half of the SOC variability. The findings emphasize the importance of incorporating CR into SOC mapping efforts.

Potato production under zero-tillage with rice straw mulch (PZTM) is an innovative and sustainable approach that enhances crop productivity, profitability, and resource-use efficiency while mitigating greenhouse gas emissions. This study aimed to optimize mulching thickness, plant spacing, fertilization strategies, and potato varieties under PZTM conditions during the Rabi seasons of 2021-22 and 2022-23 in Patna, Bihar, India. A randomized complete block design was implemented, consisting of four trials and 12 treatments, replicated three times, to assess the effects of varying fertilizer sources, mulch thickness, plant spacing, and potato varieties. Results demonstrated that integrating organic and inorganic fertilizers yielded the highest agronomic and economic benefits, achieving a 60 % yield increase over the control. Environmental assessments revealed that treatments incorporating organic inputs significantly reduced C footprints compared to inorganic-only systems. Among the tested potato varieties, Kufri Khyati, Kufri Uday, Kufri Jyoti, Kufri Thar-2, and Kufri Pukhraj exhibited superior performance, yielding 35-38 t ha- 1, making them highly suitable for PZTM. Optimal mulch thickness (14-18 t ha- 1) and plant spacing (50 x 20 cm) enhanced yields and profitability while minimizing nonmarketable tubers and greening. These findings underscore the potential of PZTM as a sustainable potato production system within rice-potato cropping systems. Adopting key PZTM agronomic practices-integrated fertilization, optimized mulching, and strategic spacing-can help farmers achieve higher yields, improved economic returns, and environmental sustainability. This study provides valuable insights for policymakers and farmers seeking to implement sustainable intensification strategies in potato cultivation.

Extreme precipitations that promote flooding, salinity, and soils with low drainage and aeration are challenges that affect food production in the Southwest of Bangladesh. After rice cultivation, most lands are kept fallow, emphasizing the urgency of research on techniques that allow crop diversification to guarantee food security. In this study, 2 years-trials on farmers' plots were established in 3 locations of Khulna district-Bangladesh to test potato cultivation under zero tillage with rice straw mulching (ZT) in comparison to conventional cultivation (CT) after rice. A soil quality index (SQI) using the partial least squares method was run considering key soil indicators related to crop production. ZT allowed a higher tuber yield and a lower C footprint than CT. Although neither method significantly affected soil organic carbon stock, there was a significant increment from season 1 to 2 (29.0 +/- 11.3 and 45.8 +/- 9.4 Mg ha(-1), respectively). ZT allowed potato cultivation 15-25 days earlier than CT. Although crop modeling did not show a significant increase in potential tuber yield when simulating the earlier planting of ZT, this advantage will be crucial in the future, considering that high-intensity rainfall events are increasing, flooding the fields and preventing crop cultivation. Alkalinity, carbonates, and anaerobic soil conditions drove the SQI, influencing the elements kept in the past rice cultivation with a concomitant effect in tuber yield. ZT is a promising technology that allows crop diversification based on soil sustainability and labor reduction in challenging agroecologies like Southwest Bangladesh. Socioeconomic studies into adoption and scaling approaches are necessary for a proper technological transfer.

Breeders have made important efforts to develop genotypes able to resist virus attacks in sweetpotato, a major crop providing food security and poverty alleviation to smallholder farmers in many regions of Sub-Saharan Africa, Asia and Latin America. However, a lack of accurate objective quantitative methods for this selection target in sweetpotato prevents a consistent and extensive assessment of large breeding populations. In this study, an approach to characterize and classify resistance in sweetpotato was established by assessing total yield loss and virus load after the infection of the three most common viruses (SPFMV, SPCSV, SPLCV). Twelve sweetpotato genotypes with contrasting reactions to virus infection were grown in the field under three different treatments: pre-infected by the three viruses, un-infected and protected from re-infection, and un-infected but exposed to natural infection. Virus loads were assessed using ELISA, (RT-)qPCR, and loop-mediated isothermal amplification (LAMP) methods, and also through multispectral reflectance and canopy temperature collected using an unmanned aerial vehicle. Total yield reduction compared to control and the arithmetic sum of (RT-)qPCR relative expression ratios were used to classify genotypes into four categories: resistant, tolerant, susceptible, and sensitives. Using 14 remote sensing predictors, machine learning algorithms were trained to classify all plots under the said categories. The study found that remotely sensed predictors were effective in discriminating the different virus response categories. The results suggest that using machine learning and remotely sensed data, further complemented by fast and sensitive LAMP assays to confirm results of predicted classifications could be used as a high throughput approach to support virus resistance phenotyping in sweetpotato breeding.

Andean highland soils contain significant quantities of soil organic carbon (SOC); however, more efforts still need to be made to understand the processes behind the accumulation and persistence of SOC and its fractions. This study modeled SOC variables—SOC, refractory SOC (RSOC), and the 13C isotope composition of SOC (δ13CSOC)—using machine learning (ML) algorithms in the Central Andean Highlands of Peru, where grasslands and wetlands (“bofedales”) dominate the landscape surrounded by Junin National Reserve. A total of 198 soil samples (0.3 m depth) were collected to assess SOC variables. Four ML algorithms—random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and eXtreme gradient boosting (XGB)—were used to model SOC variables using remote sensing data, land-use and land-cover (LULC, nine categories), climate topography, and sampled physical–chemical soil variables. RF was the best algorithm for SOC and δ13CSOC prediction, whereas ANN was the best to model RSOC. “Bofedales” showed 2–3 times greater SOC (11.2 ± 1.60

Sweetpotato is a crucial crop to guarantee food security in sub-Saharan Africa, and drought events are considered one of the most critical factors affecting sweetpotato productivity in this region. In this study, airborne imagery based on reflectance (NDVI, CIred-edge) and canopy temperature minus air temperature (dT) indices was used to characterize sweetpotato genotypes under drought treatments in Mozambique. Two field experiments established in rainy/hot (Trial A) and dry/cool (Trial B) seasons were assessed. In Trial A, 24 genotypes were subjected to early- (ESD), mid- (MSD) and late-season (LSD) drought stress treatments and compared against a control. In Trial B, 120 genotypes were subjected to LSD only. The percentage of reduction in vine weight (PRVW) under drought was related primarily to temporal variation of NDVI and CI, regardless of drought treatment and seasons. dT in relation to control (dT(Amp)) was associated with PRVW in ESD-Trial A and LSD-Trial B, whereas under LSD-Trial A, dT(Amp) was related to total fresh storage root weight (TRW). During the rainy/hot season, higher TRW reduction was promoted under ESD; however, under LSD, it was possible to identify productive genotypes able to withstand drought stress, highlighting their relevance for drought-tolerance selection purposes.

Review question / Objective: The objective of this review is to analyze the effect of zero-tillage and organic mulching (with emphasis on rice-straw) on several Key Performance Indicators (KPIs) related to productivity, resources-use efficiency, and soil health, as well as, C footprint, and weed control for growing potatoes in rice-based systems in Asia. Can zero-tillage and organic mulching increase agronomic gain in potatoes crop in Asia? Rationale: Potato cultivation under zero-tillage and mulching (PZTM) between rice or rice-other crops projects a sustainable intensification of rice-based systems reducing mechanical soil disturbance with a concomitant increase of soil organic matter. However, collection, analysis, and synthesis of experiences in Asia, where this technology was mainly reported, is missing in the scientific literature. This effort, presented in this review, is crucial as a starting point for establishing if PTZM experiences have improved indicators related to productivity, resource use efficiency and soil health, and C footprint and weed management to achieve Agronomic Gain.

Rice-based systems have recently been recognized as the most critical plant source of C emissions worldwide. Globally, rice production is highest in Asia. Actions to introduce sustainable intensification practices into existing rice lands or diversifying with lower C-emitting crops such as potatoes will be crucial to mitigate climate change. The objective of this study is to analyze the effect of potato cultivation under zero/minimum-tillage and/or organic mulching (with emphasis on rice-straw) (PZTM) on key performance indicators that are crucial to achieving agronomic gains in Asia. Forty-nine studies were selected and systematically reviewed to address the study objective. Studies reveal a consensus of increase in yield, profitability, nutrient-use efficiency, and water productivity, promoted by the significant soil moisture conservation in PZTM. There is inconsistent evidence that zero-tillage benefits weed control, but its effectiveness is enhanced by mulching. Even if soil organic matter is increased (+13–33%) and zero-tillage is the main factor driving the reduction in C footprint, no values of kg CO2 eqha−1 have been reported in PZTM to date. Only a small fraction (∼2%) of the rice-cultivated areas (RCA) is intensified with potato cultivation. That way, scaling-up PZTM among rice farmers has large potential (∼24% RCA) to increase the sustainable intensification of rice-based systems in Asia.

Under the current water crisis in agriculture, irrigation methods for saving and conserving water are necessary. However, these methods must guarantee an appropriate yield with a concomitant economic benefit and a reduced environmental impact. In this study, two irrigation thresholds for irrigation timing (IT) based on thermal imagery were analyzed with the UNICA potato variety in three trials under drip (DI) and furrow (FI) irrigation during 2017–2018 in Lima, Peru. The control (T1) remained at >70% of soil field capacity. For other treatments, thresholds were defined based on stomatal conductance at light saturation (T2: 0.15 and T3: 0.05 mol H2O m−2s−1) and crop water stress index (T2: 0.4 and T3: 0.6) based on canopy temperature. An integrated index (IIN) was established for the valuation of treatments using the criteria of high fresh tuber yield (FTY) and a low total amount of irrigated water, production cost (PC), and total C emissions (TE) and using criteria of a score. FI-T2 (0.69–0.72) and DI-T3 (0.19–0.29) showed the highest and lowest IIN value, respectively. FTY in T2 was not significantly reduced under FI, resulting in a lower PC regarding DI–T2 and emphasizing the usefulness of thermal imagery in determining watering schedules in potatoes under furrow irrigation systems.

La determinación de momentos idóneos para llevar a cabo el riego en los cultivos es crucial para la optimización del recurso hídrico, además de ser parte fundamental de un sistema de soporte de decisión para el riego. Es importante identificar el momento (el ¿cuándo?) en el cual debemos regar de tal manera que ahorremos agua y no reduzcamos significativamente el rendimiento del cultivo, como se ha reportado en nuestras investigaciones llevadas a cabo en el Centro Internacional de la Papa (CIP). Este manual práctico ha sido escrito para ser usado por estudiantes, docentes e investigadores interesados en la detección de estrés temprano en los cultivos, y pretende brindar un conjunto de herramientas recientemente publicadas y generadas por el CIP con el financiamiento del Programa Nacional de Innovación Agraria (PNIA) y el Programa de Investigación en Raíces, Tubérculos y Bananas del CGIAR (RTB). Nuestros alcances han partido de la medición de la conductancia estomática máxima a luz saturada, considerada como el principal indicador del estado hídrico de las plantas, y su relación con la temperatura del follaje. Dicha temperatura es obtenida por medio de imágenes térmicas que son procesadas utilizando un software de acceso abierto “TIPCIP” (Thermal Image Processor). Esto con la finalidad de calcular el índice de estrés hídrico del cultivo (CWSI), una variable que nos indica el grado de estrés hídrico del cultivo y el momento apropiado del riego. En este manual se resume de manera práctica los procedimientos publicados recientemente en revistas internacionales, con la finalidad de que sean aplicados de manera práctica en otros cultivos propiciando la investigación en el campo de la agricultura de ahorro de agua mediante el uso de herramientas ecofisiológicas.

Canopy temperature (CT) as a surrogate of stomatal conductance has been highlighted as an essential physiological indicator for optimizing irrigation timing in potatoes. However, assessing how this trait could help improve yield prediction will help develop future decision support tools. In this study, the incorporation of CT minus air temperature (dT) in a simple ecophysiological model was analyzed in three trials between 2017 and 2018, testing three water treatments under drip (DI) and furrow (FI) irrigations. Water treatments consisted of control (irrigated until field capacity) and two-timing irrigation based on physiological thresholds (CT and stomatal conductance). Two model perspectives were implemented based on soil water balance (P1) and using dT as the penalizing factor (P2), affecting the biomass dynamics and radiation use efficiency parameters. One of the trials was used for model calibration and the other two for validation. Statistical indicators of the model performance determined a better yield prediction at harvest for P2, especially under maximum stress conditions. The P1 and P2 perspectives showed their highest coefficient of determination (R2) and lowest root-mean-squared error (RMSE) under DI and FI, respectively. In the future, the incorporation of CT combining low-cost infrared devices/sensors with spatial crop models, satellite image information, and telemetry technologies, an adequate decision support system could be implemented for water requirement determination and yield prediction in potatoes.

A multi-model inter-comparison study was conducted to evaluate the performance of ten potato crop models to accurately predict potato yield in response to elevated CO2 (Ce) when calibrated with ambient CO2 data (Ca). Experimental data from seven open-top chambers (OTC) and free-air- CO2-enrichment (FACE) facilities across continental Europe were used. Model ensemble percent errors averaged over all datasets for simulated yields were 26.5 % for Ca and 27.2 % Ce data. Metrics such as Wilmott?s index of agreement (IA) and root mean square relative error (RMSRE) ranged broadly among individual models and locations, such that four of the ten models outperformed the median or mean of the ensemble for about half of the Ce datasets. These top performing models were representative of three different model structural groups, including radiation use efficiency, transpiration efficiency, or leaf-level based approaches. Relative response to an increase in CO2 was more accurately modeled than absolute yield responses when averaged across all locations, and within 3.3 kg ppm? 1 (or 5%) of observed values. Specific targets in the model structure needed for improvement were not identified due to large and inconsistent variation in the accuracy of yield predictions across locations. However, models with the lowest calibration errors tended to be top performers for Ce predictions as well. Such results suggest calibration is at least as important as model structure. Where possible, modelers using potato models to estimate Ce responses should use Ce calibration data to improve confidence in such predictions.

Multiple ecosystem functions need to be considered simultaneously to manage and protect the several ecosystem services that are essential to people and their environments. Despite this, cost effective, tangible, relatively simple and globally relevant methodologies to monitor in situ soil multifunctionality, that is, the provision of multiple ecosystem functions by soils, have not been tested at the global scale. We combined correlation analysis and structural equation modelling to explore whether we could find easily measured, field-based indicators of soil multifunctionality (measured using functions linked to the cycling and storage of soil carbon, nitrogen and phosphorus). To do this, we gathered soil data from 120 dryland ecosystems from five continents. Two soil surface attributes measured in situ (litter incorporation and surface aggregate stability) were the most strongly associated with soil multifunctionality, even after accounting for geographic location and other drivers such as climate, woody cover, soil pH and soil electric conductivity. The positive relationships between surface stability and litter incorporation on soil multifunctionality were greater beneath the canopy of perennial vegetation than in adjacent, open areas devoid of vascular plants. The positive associations between surface aggregate stability and soil functions increased with increasing mean annual temperature. Synthesis and applications. Our findings demonstrate that a reduced suite of easily measured in situ soil surface attributes can be used as potential indicators of soil multifunctionality in drylands world-wide. These attributes, which relate to plant litter (origin, incorporation, cover), and surface stability, are relatively cheap and easy to assess with minimal training, allowing operators to sample many sites across widely varying climatic areas and soil types. The correlations of these variables are comparable to the influence of climate or soil, and would allow cost-effective monitoring of soil multifunctionality under changing land-use and environmental conditions. This would provide important information for evaluating the ecological impacts of land degradation, desertification and climate change in drylands world-wide.

Crop efficiencies associated with intercepted radiation, conversion into biomass and allocation to edible organs are essential for yield improvement strategies that would enhance genetic properties to maximize carbon gain without increasing crop inputs. The production of 20 potato landraces—never studied before—was analyzed for radiation interception ( ε i ), conversion ( ε c ) and partitioning ( ε p ) efficiencies. Additionally, other physiological traits related to senescence delay (normalized difference vegetation index (NDVI) s l p ), tuberization precocity ( t u ), photosynthetic performance and dry tuber yield per plant (TY) were also assessed. Vegetation reflectance was remotely acquired and the efficiencies estimated through a process-based model parameterized by a time-series of airborne imageries. The combination of ε i and ε c , closely associated with an early tuber maturity and a NDVI s l p explained 39% of the variability grouping the most productive genotypes. TY was closely correlated to senescence delay (r P e a r s o n = 0.74), indicating the usefulness of remote sensing methods for potato yield diversity characterization. About 89% of TY was explained by the first three principal components, associated mainly to t u , ε c and ε i , respectively. When comparing potato with other major crops, its ε p is very close to the theoretical maximum. These findings suggest that there is room for improving ε i and ε c to enhance potato production.

Potatoes—a global food security and staple crop—is threatened by dry spells in drought-prone areas. The use of physiological thresholds to save water while maintaining a reasonable tuber yield has been proposed, but their effects on physiological performances and usefulness under different irrigation methods are yet to be evaluated. In this study, photosynthetic traits were monitored to assess the effect of water restriction and rewatering under drip (DI) and furrow (FI) irrigations. The treatments consisted of two maximum light-saturated stomatal conductance (g s _ m a x ) irrigation thresholds (T2: 0.15 and T3: 0.05 mol H 2O m − 2 s − 1 ) compared with a fully irrigated control (g s _ m a x > 0.3 mol H 2O m − 2 s − 1 ). DI used less water than FI but promoted early senescence and low percentage of maximum assimilation rate (PMA) at late developmental stages. FI caused no yield penalization in T2 and higher recovery of carbon isotope discrimination and PMA than DI. It is suggested that moderate water quantities of early and frequently water pulses in the irrigation, promote short-term water stress memory improvement, senescence delay and more capability of recovery at late stages.

Accurate determination of plant water status is mandatory to optimize irrigation scheduling and thus maximize yield. Infrared thermography (IRT) can be used as a proxy for detecting stomatal closure as a measure of plant water stress. In this study, an open-source software (Thermal Image Processor (TIPCIP)) that includes image processing techniques such as thermal-visible image segmentation and morphological operations was developed to estimate the crop water stress index (CWSI) in potato crops. Results were compared to the CWSI derived from thermocouples where a high correlation was found ( r P e a r s o n = 0.84). To evaluate the effectiveness of the software, two experiments were implemented. TIPCIP-based canopy temperature was used to estimate CWSI throughout the growing season, in a humid environment. Two treatments with different irrigation timings were established based on CWSI thresholds: 0.4 (T2) and 0.7 (T3), and compared against a control (T1, irrigated when soil moisture achieved 70% of field capacity). As a result, T2 showed no significant reduction in fresh tuber yield (34.5 ± 3.72 and 44.3 ± 2.66 t ha - 1 ), allowing a total water saving of 341.6 ± 63.65 and 515.7 ± 37.73 m 3 ha - 1 in the first and second experiment, respectively. The findings have encouraged the initiation of experiments to automate the use of the CWSI for precision irrigation using either UAVs in large settings or by adapting TIPCIP to process data from smartphone-based IRT sensors for applications in smallholder settings.