Phosphorus (P) availability critically limits the productivity of tropical forests growing on highly weathered, low-P soils. Although efforts to incorporate P into Earth system models (ESMs) provide an opportunity to better estimate tropical forest response to climate change, P sorption dynamics and controls on soil P availability are not well constrained. Here, we measured P and dissolved organic carbon (DOC) sorption isotherms on 23 soils from tropical Oxisol, Ultisol, Inceptisol, Andisol, and Aridisol soils using P concentrations from 10 to 500mg P L−1, and DOC concentrations from 10 to 100mg DOC L−1. Isotherms were fit to the Langmuir equation and parameters were related to soil characteristics. Maximum P sorption capacity (Qmax) was significantly correlated with clay content (ρ=0.658) and aluminium (Al)- or iron (Fe)-oxide concentrations (ρ=0.470 and 0.461 respectively), and the DOC Qmax was correlated with Fe oxides (ρ=0.491). Readily available soil characteristics could eventually be used to estimate Qmax values. Analysis of literature values demonstrated that the maximum initial P concentration added to soils had a significant impact on the resultant Qmax, suggesting that an insufficiently low initial P range could underestimate Qmax. This study improves methods for measuring P Qmax and estimating Qmax in the absence of isotherm analyses and provides key data for use in ESMs.

Ecosystem functional responses such as soil CO 2 emissions are constrained by microclimate, available carbon (C) substrates and their effects upon microbial activity. In tropical forests, phosphorus (P) is often considered as a limiting factor for plant growth, but it is still not clear whether P constrains microbial CO 2 emissions from soils. In this study, we incubated seven tropical forest soils from Brazil and Puerto Rico with different nutrient addition treatments (no addition, Control; C, nitrogen (N) or P addition only; and combined C, N and P addition (CNP)). Cumulative soil CO 2 emissions were fit with a Gompertz model to estimate potential maximum cumulative soil CO 2 emission (C m ) and the rate of change of soil C decomposition (k). Quantitative polymerase chain reaction (qPCR) was conducted to quantify microbial biomass as bacteria and fungi. Results showed that P addition alone or in combination with C and N enhanced C m , whereas N addition usually reduced C m , and neither N nor P affected microbial biomass. Additions of CNP enhanced k, increased microbial abundances and altered fungal to bacterial ratios towards higher fungal abundance. Additions of CNP, however, tended to reduce C m for most soils when compared to C additions alone, suggesting that microbial growth associated with nutrient additions may have occurred at the expense of C decomposition. Overall, this study demonstrates that soil CO 2 emission is more limited by P than N in tropical forest soils and those effects were stronger in soils low in P. Highlights• A laboratory incubation study was conducted with nitrogen, phosphorus or carbon addition to tropical forest soils. Soil CO 2 emission was fitted with a Gompertz model and soil microbial abundance was quantified using qPCR. Phosphorus addition increased model parameters C m and soil CO 2 emission, particularly in the Puerto Rico soils. Soil CO 2 emission was more limited by phosphorus than nitrogen in tropical forest soils.

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Long-term temporal and spatial information of crop type supports a wide range of applications including hydrological and climatological studies. In the U.S., yearly crop data layers (CDLs) are available starting in the early 2000s and have been developed using combined field information and sets of temporal imagery from multiple sensors. Development of long-term crop-type layers similar to CDLs is restricted by reduced accessibility to imagery and the necessary auxiliary datasets. In this study, a procedure to generate a historical crop type was developed and evaluated. Time series of Normalized Difference Vegetation Index (NDVI) datasets from Landsat 5 TM sensor for the Lower Bear Creek watershed were collected and processed. Object-based pseudo phenology curves, represented by the NDVI time series, were generated using noise filtering and dimensionality standardization procedures for the years 1985, 1990, 1995, 2000, and 2005. Classifiers were developed and evaluated using random-forest machine learning algorithms and CDL datasets as the reference. Increased generalization performance was obtained when the model was developed using multi-year datasets. This can be attributed to improved crop type representation during the training phase coupled with characterization of yearly variations due to natural (weather) and anthropogenic factors (farming management). Source of uncertainties were the presence of multiple crops within objects, phenological similarities between soybean and corn/maize, and the accuracy of CDL itself. The proposed procedure supports the development of historic crop types for long-term studies at the field scale in agricultural watersheds.

Highlights Over the last 40 years the amount of irrigation water used by cotton in the United States has decreased while yields have increased leading to a large increase in crop water productivity (CWP). Many factors have contributed to improved CWP, such as improvements in water delivery systems. Irrigation scheduling technologies have also contributed to improved CWP; however, farmer adoption of advanced scheduling technologies is still limited and there is significant room for improvement. Increased yields from improved cultivars without an increase in water requirements has also been important for CWP. Continued developments in sensor technologies and improved crop simulation models are two examples of future strategies that should allow the U.S. cotton industry to continue an upward trend in CWP. Abstract. Over the last 40 years the amount of irrigation water used by cotton in the United States has decreased while yields have increased. Factors contributing to higher water productivity and decreased irrigation water use include migration of cotton out of the far western U.S. states to the east where more water requirements are met by rainfall; improved irrigation delivery systems with considerable variation in types and adoption rates across the U.S.; improved irrigation scheduling tools; improved genetics and knowledge of cotton physiology, and improved crop models that can help evaluate new irrigation strategies rapidly and inexpensively. The considerable progress over the last 40 years along with the promise of emerging technologies suggest that this progress will continue. Keywords: Cotton, Crop water productivity, Irrigation, Sustainability, Water use efficiency.

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