Soil Temperature Control Research Articles

Soil Marginal Effect and LSTM Model in Chinese Solar Greenhouse.

The food crisis has increased demand for agricultural resources due to various factors such as extreme weather, energy crises, and conflicts. A solar greenhouse enables counter-seasonal winter cultivation due to its thermal insulation, thus alleviating the food crisis. The root temperature is of critical importance, although the mechanism of soil thermal environment change remains uncertain. This paper presents a comprehensive study of the soil thermal environment of a solar greenhouse in Jinzhong City, Shanxi Province, employing a variety of analytical techniques, including theoretical, experimental, and numerical simulation, and deep learning modelling. The results of this study demonstrate the following: During the overwintering period, the thermal environment of the solar greenhouse floor was divided into a low-temperature zone, a constant-temperature zone, and a high-temperature zone; the distance between the low-temperature boundary and the southern foot was 2.6 m. The lowest temperature in the low-temperature zone was 11.06 °C and the highest was 19.05 °C. The floor in the low-temperature zone had to be heated; the lowest value of the constant-temperature zone was 18.29 °C, without heating. The minimum distance between the area of high temperature and the southern foot of the solar greenhouse was 8 m and the lowest temperature reading was 19.29 °C. The indoor soil temperature tended to stabilise at a depth of 45 cm, and the lowest temperature reading at a horizontal distance of 1400 mm from the south foot was 19.5 °C. The Fluent and LSTM models fitted well and the models can be used to help control soil temperature during overwintering in extreme climates. The research can provide theoretical and data support for the crop areas and the heating of pipelines in the solar greenhouse.

Application of ZigBee Technology in Smart Soil Temperature and Humidity Control Systems

As global food security increasingly becomes a focus, challenges faced by Chinese agriculture are particularly prominent. This study introduces an intelligent monitoring and control system for soil temperature and humidity using ZigBee technology, aimed at enhancing agricultural production efficiency and crop yield. The system's core, equipped with an STC89C52 microcontroller and DHT11 temperature and humidity sensors, gathers data and wirelessly transmits it to a central control unit via ZigBee modules, which then automatically adjusts the agricultural production environment to ensure crops grow in the most ideal soil temperature and humidity conditions. Compared to traditional methods, this system achieves remote monitoring and precise control, providing an innovative solution for modern agricultural production. Experiments prove that the system not only improves control accuracy but also significantly enhances operational convenience and response speed, playing an important role in promoting the development of smart agriculture.

Статистические характеристики данных температурного мониторинга грунтовых оснований фундаментов зданий города Салехарда за 2018–2023 годы как основа для автоматизации процесса первичной обработки данных

The Arctic today is a dynamically developing territory where the construction and operation of infrastructure are complicated by the presence of permafrost. Climatic changes and anthropogenic factors have a negative impact on their condition. In this regard, studies of the current and forecast state of permafrost in natural and anthropogenic conditions are relevant, which is necessary for the formation of plans for adaptation to climate change, design and engineering surveys work, and risk ma­nagement. Monitoring the temperature regime of soils at the base of the existing infrastructure seems to be the most obvious solution for monitoring the state of permafrost. The object of the study is the soil foundation of buildings erected on permafrost. The subject of the study is data obtained using an automated soil temperature control system for the foundations of capital construction facilities in Salekhard. For the purpose of identifying negative changes in the bearing capacity of permafrost at early stages, the task of determining a set of criteria based on the analysis of temperature monitoring data and their statistical characteristics is solved. In Salekhard wells were drilled at the base of 30 buildings to the depth of the foundation, where thermometric equipment was installed. The sensors measure the soil temperature every 3 hours and transmit it to the server for analysis. As a result, the possibility of using the amplitude of annual fluctuations in the temperature of the soil base and the capacity of the seasonal melt layer under the building to assess the effectiveness of the soil thermal stabilization system is shown. By analyzing the average and maximum temperature values in the bases of objects, it is possible to identify objects with weakened zones. A set of criteria is given for identifying objects with negative changes in the frozen base, and statistical characteristics can serve as a basis for automating the process of processing monitoring data. The information obtained on the parameters of permafrost under buildings will allow us to determine the current state of the soil foundations and predict the development of the situation in order to prevent the destruction of infrastructure.

Convergent control of soil temperature on seasonal carbon flux in Tibetan alpine meadows: An in-situ monitoring study

The Tibet Plateau, with its extensive carbon pools, plays a pivotal role in the global carbon budget. Nevertheless, the driving factors of the carbon dioxide budget remain disputed, and the impact of freeze–thaw process on carbon release is still unclear due to the harsh climate and lack of monitoring data. To clarify the primary factors affecting the alpine meadow ecosystems and to examine the impact of freeze–thaw on carbon release, we employed the LI-8150 automated continuous measurement system. This system, in conjunction with eddy covariance meteorological data, The Boosted Regression Tree (BRT) model, and multiple stepwise regression analysis, were used to analyze the seasonal variations in carbon flux (e.g., net ecosystem carbon exchange [NEE], gross primary productivity [GPP], and ecosystem respiration [Reco]). We also investigate the carbon sources and sinks in the alpine meadow ecosystem, as well as the predominant factor of carbon flux. Our findings include: (1) the carbon sources and sinks in the alpine meadow ecosystem shift seasonally on monthly and daily scales. On a monthly scale, the ecosystem functions as a moderate carbon sink in June, July, August, and September and as a weak carbon source from October through May. (2) Overall, the alpine meadow ecosystem, located in the northeastern Qinghai Lake basin, serves as a weak carbon sink (-58.53 g C m−2 year−1). (3) Soil temperature is the primary factor driving most variations observed in NEE, Reco, and GPP, contributing 48.05 %, 78.61 %, and 65.05 %, respectively. Soil temperature, soil water dynamics influenced by freeze and thaw processes, and their interaction with plant growth collectively play a crucial role in regulating the carbon sources and sinks in ecosystems. We provide first-hand observational data for monitoring the carbon sources and sinks in the alpine meadow ecosystem of the Tibet Plateau, as well as offer future guidance for studying the carbon budget of the Tibet Plateau.

Implementation of Fuzzy Logic in Soil Moisture and Temperature Control System for Araceae Plants Based on LoRa

The Araceae plants are highly popular among plant enthusiasts worldwide. The Araceae family has more than 100 different types and thousands of species. Despite ever-evolving times, these plants have a high market value and challenging breeding methods. To achieve optimal quality, it is important to maintain the appropriate humidity and temperature according to the natural conditions of these plants in the tropical rainforest. The Node MCU ESP32 is a processor for instructions from the room temperature sensor, room humidity sensor, and soil moisture sensor. Additionally, this component controls the blower and misting system as output, which will be processed through LoRa technology to transmit monitoring data to the Blynk software. This study utilizes fuzzy logic to categorize room temperature, humidity, soil moisture, and output results for different Araceae plants. LoRa technology is used to send monitoring data efficiently in the data transmission process. During data retrieval using long-range technology, a delay of approximately 5 seconds is known between the receiver and transmitter at a distance of 700 meters. Constraints that cause issues with this long-range technology are influenced by wind, which affects antenna signal strength, and the presence of trees and buildings as obstacles. The monitoring results show an average temperature in normal conditions and an average humidity in wet conditions. At the same time, soil moisture is monitored to maintain normal humidity, resulting in all outputs being off.

The Impact of Soil Tension on Isotope Fractionation, Transport, and Interpretations of the Root Water Uptake Origin

AbstractThe new isotope module in HYDRUS‐1D can be used to infer the origin of root water uptake (RWU), a suitable dynamic indicator for agriculture and forest water management. However, evidence shows that the equilibrium fractionation between liquid water and water vapor within the soil is affected not only by soil temperature but also by soil tension. How soil tension affects isotope transport modeling and interpretations of the RWU origin is still unknown. In this study, we evaluated three fractionation scenarios on model performance for a field data set from Langeoog Island: (a) no fractionation (Non_Frac), (b) the soil temperature control on equilibrium fractionation as described by the standard Craig‐Gordon equation (CG_Frac), and (c) CG_Frac plus the soil tension control on equilibrium fractionation (CGT_Frac). The model simulations showed that CGT_Frac led to more depleted isotopic compositions of surface soil water than CG_Frac. The vertical origin of RWU was estimated using the water balance (WB) calculations and the Bayesian mixing model (SIAR). While the former directly used water flow outputs, the latter used as input simulated isotopic compositions (using different fractionation scenarios) of RWU and soil water. Both methods provided similar variation trends with time and depth in different soil layers' contributions to RWU. The contributions of all soil layers interpreted by the CGT_Frac scenario were always between Non_Frac and CG_Frac. The temporal origin of RWU was deduced from particle tracking (PT, releasing one hypothetical particle for individual precipitation event and tracking its movement based on the water balance between particles) and a virtual tracer experiment (VTE, assigning a known isotope composition to individual precipitation event and tracking its movement based on the cumulative isotope flux). Both methods revealed similar variation trends with time in drainage and root zone (RZ) travel times. The interpreted drainage and RZ travel times were generally ranked as Non_Frac > CGT_Frac > CG_Frac. Overall, the factors considered in the standard CG equation dominated isotope fractionation, transport, and interpretations of the RWU origin. Isotope transport‐based methods (SIAR, VTE) were more computationally demanding than water flow‐based methods (WB, PT).

Mulching effect on water management in agro-ecological systems: a review

Abstract The rapidly growing world population, inadequate accessible freshwater resources, and frequent erratic changes in climate have stressed human beings and compelled them to take vital initiatives to prevent water wastage. Water conservation efforts are also intensified in the world's largest water-consuming agriculture sector. The water use efficiency in agronomy has already been enhanced by the adoption of the drip irrigation method, intended to supply water directly to the crop, rather than the land around, thereby minimising water losses up to 70% occurring through evaporation and distribution. Mulching in association with drip irrigation in arid crop agronomy leads to mitigating vigorously water stress in agriculture. Mulch is a layer of natural or synthetic or combination material applied to the surface of the soil as a protective medium between the soil and the atmosphere. Entirely, it is an efficient medium for soil moisture conservation, and soil temperature control, adds nutrients to the soil by preventing leaching and more efficient use of fertilisers, resistor to erosion losses, suppressing the weeding and improving the visual look of landscapes. This review paper covers various aspects of mulch, emerging as a dynamic water and soil management tool without affecting the value of crops in agriculture.

RANCANG BANGUN SMART GREENHOUSE UNTUK BUDIDAYA TANAMAN SAWI PAKCOY (brassica rapa subsp) BERBASIS ANDROID

Due to rising market demand, green mustard cultivation is still being developed. A soil temperature and humidity control system based on the Arduino Uno microcontroller will be designed and tested in this study, as will an automatic plant watering system for this control. The part for recognizing temperature and mugginess utilized is the DHT22 sensor with a result as a computerized signal and has 4 pins comprising of a power supply, information sign, invalid and ground. This study employed engineering design for the production of the automation system. The study produced a smart greenhouse that is capable of remotely controlling microcontroller equipment and monitoring devices like sensors and can regulate, adjust, and modify the climate to optimize the conditions and processes of plant growth in the greenhouse.

Living Mulch as Sustainable Tool to Improve Leaf Biomass and Phytochemical Yield of Cynara cardunculus var. altilis

Living mulch (LM) provides many beneficial agro-ecological services by reducing soil evaporation, conserving moisture, controlling soil temperature, reducing weed growth, increasing organic matter and nutrient availability, and improving microbial activities. Legumes, commonly used as living mulches, can affect the yield and quality of main crops. We hypothesized that Egyptian clover co-cultivated with Cynara cardunculus var. altilis (cultivated cardoon) can positively affect both leaf biomass and phytochemical yield. The study was performed on two growing seasons of field experiments to evaluate the potential variation in biomass and phytochemical yields of cardoon leaves. In addition, the leaves were collected at 90, 120, and 150 days after transplanting to evaluate the possible effect of the harvest time. LM improved the fresh and air-dried leaves biomass yields, total phenolic, and dry matter content, while it had no effect on the content of crude fibre, total sugars, L-ascorbic acid, total chlorophylls, and antioxidant activity. Except for luteolin-7-O-glucoside content, no negative effect of LM was observed on the polyphenol profile. Behind the cultivation system, the parameters were also affected by both the harvest time and growing season. According to our data, the proposed intercropping of cultivated cardoon with LM could represent an innovative cultivation system to increase both leaf biomass and the health-promoting compounds of cultivated cardoon leaves by sustainable soil management.

The techno-economics of growing high-value temperate crops under controlled soil temperature on tropical climate lowland

Demand for high-value temperate fruits and vegetables such as lettuce, cabbage, and berries have increased with population growth despite decrease in available space for cultivation due to urbanisation. These crops particularly grow well under low soil temperature, hence their cultivation pose huge challenge in the hot tropics except on a few cool highlands and via greenhouse farming. Outputs from these methods are always complemented with importation to meet the demand. Studies have however shown that low soil temperature gives the right conditions for microbes that promote development of these crops. This study presents application of solar thermal chilled water for agricultural soil cooling, and the economic analysis of the cooling process. The analytical and experimental models show the cooling process is technically feasible. However, to ascertain its financial viability, additional scenarios with 1 kW, 5 kW and 10 kW cooling capacities were analysed together with the experimental size of the developed system of cooling. This shows that the viability of the system improves from 5 kW cooling capacity. In addition to all other widely cultivated crops, implementation of the proposed system would promote local production of temperate crops hence diminish heavy reliance on importation of these crops in tropical climate countries and enhance food security.

The techno-economics of growing high-value temperate crops under controlled soil temperature on tropical climate lowland

The techno-economics of growing high-value temperate crops under controlled soil temperature on tropical climate lowland

Arduino in agriculture and the teaching of mathematics

Arduino is an open-source electronics platform designed for creating interactive projects. Arduino agriculture refers to the practice of using Arduino microcontrollers in the field of agriculture. In agriculture, Arduino is commonly used for various applications such as automated irrigation systems, soil moisture monitoring, temperature and humidity control, plant health monitoring, and more. By the other hand using Arduino in mathematics teaching can be an innovative and engaging way to enhance students' understanding and practical application of mathematical concepts. Teaching mathematics through Arduino in agriculture offers several benefits: The aim of this work is to show that the applications of the Arduino board are very varied, and that it can be used efficiently in the teaching of mathematics, relating both disciplines agriculture and mathematics. From the unsystematic and elementary literature review, it can be concluded that there are enough research options, in which application projects in agriculture and livestock are implemented using the Arduino board and that can also be used for teaching mathematics, thus fulfilling the objective of this work is to highlight the possibility of the aforementioned. Therefore, it is suggested to follow this line of research that promises to be very productive.

Soil Temperature Prediction Based on 1D-CNN-MLP Neural Network Model

Highlights To predict soil temperature, a new deep learning model called 1D-CNN-MLP is proposed, which has higher accuracy or faster convergence compared with MLP or LSTM. Convolutional neural network part in the model could extract and calculate transmission of soil temperature. Using the non-sequential data of several soil temperature layers combined with the model, we can predict other temperature layers. The model can greatly reduce the difficulty and cost of soil temperature measurement. Abstract. Soil temperature plays an important role in agriculture. In order to achieve cost reduction in the sensor arrangement when monitoring soil temperature, a novel model called 1D-CNN-MLP (One dimensional convolutional neural network-Multilayer perceptron) was proposed for soil temperature prediction. Meteorological data and soil temperature data on different soil layers collected for the 2018~2021 period from a weather station in Yangling, China, were used for calculation in our work. Our model was evaluated using statistical measures of MSE (Mean Square error). The model parameters with high operation efficiency and high accuracy are obtained, and the training result records much lower error than MLP (multilayer perceptron) and faster convergence than LSTM (long short-term memory) with an MSE of 0.288 x 10&-3. The 1D-CNN (One-dimensional convolutional neural network) part of the model is used to reveal and extrapolate the law of how soil temperature propagates in different soil layers. In the case where only three layers of soil temperature data are known, the characteristic temperature layer depths of 10 cm, 15 cm, and 40 cm, are selected to place sensors and obtain the best prediction effect of soil temperature at different depths of 5 to 160 cm with a RMSE (Root mean squared error) of 1.988?. The model may help users with improved and economical soil temperature prediction and control, thus boosting crop yield. Ultimately, we found the model has a relatively poor performance in the accuracy of deep soil temperature prediction when only three layers of soil temperature data are known, and it is suggested that the model can be further optimized in terms of kernel parameter setting, data composition, and the variation law of deep soil temperature. Keywords: 1D-CNN, MLP, Soil temperature prediction.

Grazing Increases Soil Warming in Headwater Wetlands: Importance to Land Managers and Water Users

The earth’s mountains continue to lose water. Glaciers are melting and mountain snow/rain balance tilts increasingly liquescent. Water is running off sooner—sometimes overfilling reservoirs, causing flooding, and setting the stage for late-season shortages. One adaptive strategy is to recover and enhance water-storage capacities of headwater riparian systems. Grazing, a common use of headwater lands, affects both soils and vegetation. To better understand how grazing might affect water storage and other ecosystem services of high elevation riparian wetlands, we measured soil-profile temperatures, soil organic matter (SOM), and phytomass at six sites in the upper Sweetwater River sub-basin of Wyoming, USA, where fence lines allowed us to contrast grazing management. We found grazed wetlands consistently had warmer soils than fenced wetlands. We found added evidence that SOM is important in both soil temperature control and water-storage potential of wetlands. We review evidence that warmer soil temperatures and drying cause loss of SOM with the implied concomitant C losses through erosion and emissions. We recommend land managers of temperate-climate headwater systems consider the need to end growing seasons with full stands of riparian vegetation to reduce soil warming and to build organic matter—particularly on lands where municipalities and other downstream water users are seeking long-term increases in water yields and less flooding.

Calibration of the EU-Rotate_N model with measured C and N mineralization from potential fertilizers and evaluation of its prediction of crop and soil data from a vegetable field trial

Mechanistic models are useful tools for understanding and taking account of the complex, dynamic processes such as carbon (C) and nitrogen (N) turnover in soil and crop growth. In this study, the EU-Rotate_N model was first calibrated with measured C and N mineralization from nine potential fertilizer resources decomposing at controlled soil temperature and moisture. The materials included seaweeds, wastes from the food industry, food waste anaerobically digested for biogas production, and animal manure. Then the model’s ability to predict soil and crop data in a field trial with broccoli and potato was evaluated. Except for seaweed, up to 68% of added C and 54–86% of added N was mineralized within 60 days under controlled conditions. The organic resources fell into three groups: seaweed, high-N industrial wastes, and materials with high initial content of mineral N. EU-Rotate_N was successfully calibrated for the materials of industrial origin, whereas seaweeds, anaerobically digested food waste and sheep manure were challenging. The model satisfactorily predicted dry matter (DM) and N contents (root mean square; RMSE: 0.11–0.32) of the above-ground part of broccoli fertilized with anaerobically digested food waste, shrimp shell pellets, sheep manure and mineral fertilizers but not algal meal. After adjusting critical %N for optimum growth, potato DM and N contents were also predicted quite well (RMSE: 0.08–0.44). In conclusion, the model can be used as a learning and decision support tool when using organic materials as N fertilizer, preferably in combination with other models and information from the literature.

Soil mulch in control of soil temperature and incidence of weeds in the production of crisphead lettuce

It was evaluated the effects of different types of mulching of soil in the cultivation of crisphead lettuce (Lactuca sativa L.), cultivar “Lucy Brown”, in the spring period, on weed control, soil temperature and productivity. The experiment was arranged in a randomized block design with four treatments and five replications in plots of 14.0m2. The following coverages were studied: Pennisetum glaucum L., Avena strigosa, Brachiaria ruziziensis, plus the control treatment (soil without mulching). The use of mulching of soil, P. glaucum and B. ruziziensis, proved to be a beneficial practice, reducing the soil temperature by up to 5ºC. There was a lower incidence of weeds with the use of the P. glaucum and A. strigosa coverages. The use of mulch did not provide a significant gain in productivity, however, there was a gain regarding the quality of the product, since the coverage with P. glaucum provided an increase in the size of the head (fresh mass of the commercial head).

Snow dynamics influence tree growth by controlling soil temperature in mountain pine forests

Snow dynamics are key to understanding tree growth in mountain forests and future response to climate change. However, precise monitoring of microclimate conditions and variables related to tree growth and functioning are lacking. To advance on those issues, snow cover and microclimate conditions, tree phenology, xylogenesis, intra-annual radial growth and the concentration of sapwood and needle non-structural carbohydrates were intensively monitored in four Pinus uncinata forests along an altitudinal gradient over three years in a Pyrenean valley (NE Spain). Snow dynamics exerted strong influence on soil temperature and moisture, particularly before and during the early growing season. Soil temperature was the most relevant microclimate variable during the overall xylogenesis, mainly influencing the production of mature tracheids. Large snow accumulation resulted in later snow depletion and a consequent delay in soil warming onset. Low soil temperatures in the spring, related to prolonged snow persistence, retarded cambial reactivation and led to lower growth rate. Despite strong spatial variability among plots, wood production was determined by snow dynamics in three out of the four studied plots. This study highlights the major role played by early and late growing season soil temperatures on radial growth of mountain conifers. The results of this study suggest that a future shallower and more transitory snowpack in the studied forests, together with warmer soil and air temperatures, may increase radial growth and productivity of similar mid-latitude, young mountain forests.

The Effect of Soil Type on Gaseous Emissions from Flooded Rice Fields in Portugal

Overall rice cultivation in Europe occurs under flooding conditions to control soil temperature, weeds, and pests. The aim of this study was to assess the effects of sandy and clay soils on ammonia (NH3), nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4) emissions from flooded rice (Oryza sativa L.) fields. A 2-year rice field experiment was conducted in two different soils (sandy and clay soil) during two consecutive seasons in central Portugal. Soil mineral N dynamics and gas fluxes were followed between rice sowing (May) and harvest (October) dates. The NH3 fluxes were measured by the dynamic chamber technique while the N2O, CO2, and CH4 fluxes were measured by the closed chamber technique. Ammonia and N2O emissions did not differ significantly (p > 0.05) among the two soils and were about 10% and 1.5% of the N applied, respectively. In clay soil, CO2 emissions were significantly higher (p 0.05) between the two soils. The yield-scaled NH3 from clay soil was significantly higher (p < 0.05) in 29% relative to sandy soil. The present study suggests that NH3 losses and global warming potential from flooded rice are quite similar among the sandy and clay soil. However, the yield-scaled NH3 was significantly higher in clay soil, in relation with a highest concentration of available ammonium in the solution, while yield-scaled global warming potential was not significantly different among the two soils.

Aerated Irrigation Promotes Soil Respiration and Microorganism Abundance around Tomato Rhizosphere

Core Ideas Aerated irrigation improved soil aeration, increasing oxygen concentration and air‐filled porosity. Aerated irrigation enhanced soil, microbial, and root respiration and microbe abundance. Soil oxygen, air‐filled porosity correlated with respiration, microbe abundance. Aerated irrigation has economic benefits, promoting fruit yield and IWUE. Aerated irrigation (AI), by which aerated water is supplied to soil through subsurface drip irrigation (SDI), has shown high yield potential and application prospects. This study evaluated the impact of AI on soil aeration, respiration components, and microorganism abundance to determine the relationships between these parameters. Four treatments (W1AI, W2AI, W1CK, and W2CK) combining AI and nonaerated SDI (CK) and two irrigation levels (W1 and W2, with crop‐pan coefficients of 0.6 and 1.0) were conducted in a greenhouse during two tomato (Lycopersicon esculentum Mill.) growing seasons in April to July in 2015 and 2016. The results showed that AI improved soil aeration conditions, with soil oxygen concentration and air‐filled porosity significantly increasing by 5.86 and 5.16%, respectively, compared with CK. The W1AI treatment increased soil, microbial, and root respiration as well as bacteria, actinomycetes, and fungi abundance by 18.8, 10.0, 23.4, 30.1, 29.4, and 30.1%, respectively, compared with W1CK, and W2AI significantly increased these by 24.2, 15.3, 28.7, 39.2, 39.3, and 37.1%, respectively, compared with W2CK. Moreover, the abundance of bacteria, actinomycetes, and fungi with W2AI significantly increased by 26.9, 27.1, and 24.8%, respectively, compared with W1AI. Soil oxygen concentration (under controlled soil temperature) and air‐filled porosity positively correlated with soil respiration and microorganism abundance. Mean fruit yield with AI was 22.4% higher than CK. Aerated irrigation therefore stimulated soil respiration and microbial proliferation by improving soil aeration conditions. Enhanced soil aeration, respiration, and microorganism abundance under AI provide economic benefit by improving fruit yield.

Environmental and Vegetative Controls on Soil CO2 Efflux in Three Semiarid Ecosystems

Soil CO2 efflux (Fsoil) is a major component of the ecosystem carbon balance. Globally expansive semiarid ecosystems have been shown to influence the trend and interannual variability of the terrestrial carbon sink. Modeling Fsoil in water-limited ecosystems remains relatively difficult due to high spatial and temporal variability associated with dynamics in moisture availability and biological activity. Measurements of the processes underlying variability in Fsoil can help evaluate Fsoil models for water-limited ecosystems. Here we combine automated soil chamber and flux tower data with models to investigate how soil temperature (Ts), soil moisture (θ), and gross ecosystem photosynthesis (GEP) control Fsoil in semiarid ecosystems with similar climates and different vegetation types. Across grassland, shrubland, and savanna sites, θ regulated the relationship between Fsoil and Ts, and GEP influenced Fsoil magnitude. Thus, the combination of Ts, θ, and GEP controlled rates and patterns of Fsoil. In a root exclusion experiment at the grassland, we found that growing season autotrophic respiration accounted for 45% of Fsoil. Our modeling results indicate that a combination of Ts, θ, and GEP terms is required to model spatial and temporal dynamics in Fsoil, particularly in deeper-rooted shrublands and savannas where coupling between GEP and shallow θ is weaker than in grasslands. Together, these results highlight that including θ and GEP in Fsoil models can help reduce uncertainty in semiarid ecosystem carbon dynamics.