Potato farming is relevant for global carbon balances and greenhouse emissions, of which gross primary productivity (GPP) is one of the main drivers. In this study, the net carbon ecosystem exchange (NEE) was measured using the Eddy Covariance (EC) method in two potato crops, one of them with an irrigation system, the other under rainfed conditions. Accurate NEE partition into GPP and ecosystem respiration (RECO) was carried out by fitting a light response curve. Direct measurements of dry weight and leaf area were performed from sowing to the end of canopy life cycle and tuber bulking. Agricultural drought in the rainfed crop resulted in limited GPP rate, low leaf area index (LAI), and low canopy carbon assimilation response to the photosynthetically active radiation (PAR). Hence, in this crop, there was lower efficiency in tuber biomass gain and NEE sum indicated net carbon emissions to atmosphere (NEE = 154.7 g C m−2 ± 30.21). In contrast, the irrigated crop showed higher GPP rate and acted as a carbon sink (NEE = −366.6 g C m−2 ± 50.30). Our results show, the environmental and productive benefits of potato crops grown under optimal water supply.

To optimize coffee (Coffea arabica L.) production in Colombia, adaptation strategies that improve water use must be developed. Therefore, the objective of this study was to determine evapotranspiration under standard conditions (ETc), reference evapotranspiration (ETo), and the crop coefficient (Kc) of coffee plants interplanted between maize (Zea mays L.) (coffee–maize) for the first 12 mo of growth and coffee grown without maize from 13 to 46 m after transplanting (MAT). In this study, ETc was measured using the eddy covariance method. The ETc of coffee–maize ranged from 4.17 to 4.71 mm d–1, while ETc of coffee–sun averaged 4.32 ± 0.07 mm d–1 between 13 and 24 MAT and 4.09 ± 0.03 mm d–1 between 25 and 43 MAT for coffee trees in the reproductive stage. The Kc was 0.87 for coffee plants between 0 and 12 MAT, 0.98 ± 0.01 between 13 and 24 MAT, and 0.97 ± 0.02 between 25 and 43 MAT. Maize intercropped between coffee trees produced an adapted microclimate for the first 2 mo, allowing energy used for evapotranspiration processes (latent heat flux) to be greater than energy used for air warming (sensible heat flux), although there was low soil water availability. Kc values are a foundation for optimizing coffee crop water use under climate and soil conditions for the intertropical Andean hillside region.

This work presents quantitative detection of water stress and estimation of the water stress level: none, light, moderate, and severe on potato crops. We use hyperspectral imagery and state of the art machine learning algorithms: random decision forest, multilayer perceptron, convolutional neural networks, support vector machines, extreme gradient boost, and AdaBoost. The detection and estimation of water stress in potato crops is carried out on two different phenological stages of the plants: tubers differentiation and maximum tuberization. The machine learning algorithms are trained with a small subset of each hyperspectral image corresponding to the plant canopy. The results are improved using majority voting to classify all the canopy pixels in the hyperspectral images. The results indicate that both detection of water stress and estimation of the level of water stress can be obtained with good accuracy, improved further by majority voting. The importance of each band of the hyperspectral images in the classification of the images is assessed by random forest and extreme gradient boost, which are the machine learning algorithms that perform best overall on both phenological stages and detection and estimation of water stress in potato crops.

Estimating gross primary production (GPP) is important to understand the land–atmosphere CO2 exchange for major agroecosystems. Eddy covariance (EC) measurements provide accurate and reliable information about GPP, but flux measurements are often not available. Upscaling strategies gain importance as an alternative to the limitations of the use of the EC. Although the potato provides an important agroecosystem for worldwide carbon balance, there are currently no studies on potato GPP upscaling processes. This study reports two GPP scaling-up approaches from the detailed leaf-level characterization of gas exchange of potatoes. Multilayer and big leaf approaches were applied for extrapolating chamber and biometric measurements from leaf to canopy. Measurements of leaf area index and photosynthesis were performed from planting to the end of the canopy life cycle using an LP-80 ceptometer and an IRGA Li-Cor 6800, respectively. The results were compared to concurrent measurements of surface–atmosphere GPP from the EC measurements. Big-leaf models were able to simulate the general trend of GPP during the growth cycle, but they overestimated the GPP during the maximum LAI phase. Multilayer models correctly reproduced the behavior of potato GPP and closely predicted both: the daily magnitude and half-hourly variation in GPP when compared to EC measurements. Upscaling is a reliable alternative, but a good treatment of LAI and the photosynthetic light-response curves are decisive factors to achieve better GPP estimates. The results improved the knowledge of the biophysical control in the carbon fluxes of the potato crop.

Estimating gross primary production (GPP) is important to understand the land-atmosphere CO2 exchange for major agroecosystems. Eddy covariance (EC) measurements provide accurate and reliable information about GPP, but flux measurements are often not available. Upscaling strategies gain importance as an alternative to the limitations of the use of the EC. Although potato is an important agroecosystem for worldwide carbon balances, there are currently no studies on potato GPP upscaling processes. This study reports two GPP scaling-up approaches from the detailed leaf-level characterization of gas exchange of potato. Multilayer and big leaf approaches were applied for extrapolating chamber and biometric measurements from leaf to canopy. Measurements of leaf area index and photosynthesis were performed from planting to the end of the canopy life cycle with a LP-80 ceptometer and a IRGA Li-Cor 6800, respectively. The results were compared to concurrent measurements of surface-atmosphere GPP from the EC technique. Big-leaf models were able to simulate the general trend of GPP during the growth cycle, but they overestimated the GPP during the maximum LAI phase. Multilayer models correctly reproduced the behavior of potato GPP and closely predicted both the daily magnitude and half-hourly variation in GPP when compared to EC measurements. Upscaling is a reliable alternative, but the good treatment of LAI and net photosynthetic light-response curve are decisive factors to get better GPP estimates. The results improved the knowledge of the biophysical control in the carbon fluxes of the potato crop.