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Rotational rice systems, involving pastures, other crops and/or livestock, are common in temperate South America, exemplified by the rice-pasture-livestock system of Uruguay which combines very high rice yields with tight nitrogen (N) balances. The generally good nutrient use efficiency in these systems provides a template for nutrient management in other mixed farming systems, if the underlying processes can be sufficiently well quantified and understood. Here, we studied N balances in rice–non-rice rotations in a long-term experiment in Uruguay, with the aim of parameterizing and testing the DNDC model of N dynamics for such systems for use in future work. The experiment includes three rotations: continuous rice (RI-CONT), rice-soybean (RI-SOY) and rice-pasture (RI-PAST). We considered 9 years of data on N balances (NBAL), defined as all N inputs minus all N outputs; N surplus (NSURP), defined as all N inputs minus only N outputs in food products; and N use efficiency (NUE), defined as the fraction of N inputs removed in food products. We parameterized DNDC against measured yield and input and output data, with missing data on N losses inferred from the N balance and compared with literature values. The model performance was assessed using standard indices of mean error, agreement and efficiency. The model simulated crop yields and rice cumulative N uptake very well, and soil N reasonably well. The values of NBAL were +45 and−20 kg N ha−1 yr−1 in RI-CONT and RI-SOY, respectively, and close to zero in RI-PAST (−6 kg N ha−1 yr−1). Values of NSURP decreased in the order RI-CONT >> RI-SOY > RI-PAST (+115, +25 and +13 kg N ha−1 yr−1, respectively). Values of NUE (84, 54, and 48% for RI-SOY, RI-PAST, and RI-CONT, respectively) decreased as NBAL increased. The sensitivity of DNDC's predictions to the agronomic characteristics of the different crops, rotations and water regimes agreed with expectations. We conclude that the DNDC model as parameterized here is suitable for exploring how to optimize N management in these systems.

Modern sensor technologies can provide detailed information about soil variation which allows for more precise application of fertiliser to minimise environmental harm imposed by agriculture. However, growers should lose neither income nor yield from associated uncertainties of predicted nutrient concentrations and thus one must acknowledge and account for uncertainties. A framework is presented that accounts for the uncertainty and determines the cost–benefit of data on available phosphorus (P) and potassium (K) in the soil determined from sensors. For four fields, the uncertainty associated with variation in soil P and K predicted from sensors was determined. Using published fertiliser dose–yield response curves for a horticultural crop the effect of estimation errors from sensor data on expected financial losses was quantified. The expected losses from optimal precise application were compared with the losses expected from uniform fertiliser application (equivalent to little or no knowledge on soil variation). The asymmetry of the loss function meant that underestimation of P and K generally led to greater losses than the losses from overestimation. This study shows that substantial financial gains can be obtained from sensor-based precise application of P and K fertiliser, with savings of up to £121 ha−1 for P and up to £81 ha−1 for K, with concurrent environmental benefits due to a reduction of 4–17 kg ha−1 applied P fertiliser when compared with uniform application. Biotechnology and Biological Sciences Research Council (BBSRC): BBS/E/C/000I0320; BBS/E/C/000I0330; BBS/E/C/000I0100.

Dietary zinc (Zn) deficiency is widespread globally, and in particular among people in sub-Saharan Africa (SSA). In Malawi, dietary sources of Zn are dominated by maize and spatially dependent variation in grain Zn concentration, which will affect dietary Zn intake, has been reported at distances of up to ~ 100 km. The aim of this study was to identify potential soil properties and environmental covariates which might explain this longer-range spatial variation in maize grain Zn concentration. Data for maize grain Zn concentrations, soil properties, and environmental covariates were obtained from a spatially representative survey in Malawi (n = 1600 locations). Labile and non-labile soil Zn forms were determined using isotopic dilution methods, alongside conventional agronomic soil analyses. Soil properties and environmental covariates as potential predictors of the concentration of Zn in maize grain were tested using a priori expert rankings and false discovery rate (FDR) controls within the linear mixed model (LMM) framework that informed the original survey design. Mean and median grain Zn concentrations were 21.8 and 21.5 mg kg−1, respectively (standard deviation 4.5; range 10.0–48.1). A LMM for grain Zn concentration was constructed for which the independent variables: soil pH(water), isotopically exchangeable Zn (ZnE), and diethylenetriaminepentaacetic acid (DTPA) extractable Zn (ZnDTPA) had predictive value (p

Data Study Groups are week-long events at The Alan Turing Institutebringing together some of the country’s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges. Tackling hidden hunger through soils This report describes the work completed during a week long data study group hosted by the Alan Turing Institute. The challenge was provided by Rothamsted Research and looks at predicting soil and plant physicochemical properties from soil infrared (IR) spectra. Three datasets were explored and modelled using a combination of established and more recent data-science strategies. Due to the size, scope and variety in the datasets, multiple conclusions were drawn. Overall, our preliminary findings indicate that soil physiochemical properties were easier to model than plant physicochemical properties. Decision tree based methods were used consistently throughout the three datasets and were overall more robust than other approaches considered in our analysis. Our results are in line with the current literature; IR data can be an effective predictor of the physicochemical properties of soil and by extension, the health of the soil.

The effect of improved practice [IP; gypsum application @ 25% gypsum requirement (GR) +10 t/ha press mud] over existing practice (EP; gypsum application @ 50% GR) on rice productivity and profitability in sodic soils (pH 9.2-10.4) was evaluated at farmers' fields for 3 years during 2011-14 in Hardoi district of Uttar Pradesh, India. All growth parameters were significantly higher for IP than EP, with 36.5 and 39.3% higher straw and grain yield, respectively. Interaction effect between sodicity levels and reclamation practices were significant for grain and straw yields. Use of IP with S1, S2, S3, and S4 sodicity levels reduced reclamation costs by ` 15480, 18540, 22560 and 24780/ha, respectively. The combination of reduced costs and increased yields in the IP reclamation treatment led to higher returns from rice cultivation in sodic soils, because IP effect on rice yield was better at higher sodicity level, whereas under EP, this was achieved only for sodicity levels of S2 and S3. Soil properties like pH, EC, organic carbon, exchangeable sodium percentage (ESP), contents of cations and anions were improved significantly under IP than EP. Overall, improved practice of sodic soil reclamation had better effects on soil properties and crop yields than the current practices in vogue besides considerable reduction in cost of reclamation.

This study evaluates an aerial and ground imaging platform for assessment of canopy development in a wheat field. The dependence of two canopy traits, height and vigour, on fertilizer treatment was observed in a field trial comprised of ten varieties of spring wheat. A custom-built mobile ground platform (MGP) and an unmanned aerial vehicle (UAV) were deployed at the experimental site for standard red, green and blue (RGB) image collection on five occasions. Meanwhile, reference field measurements of canopy height and vigour were manually recorded during the growing season. Canopy level estimates of height and vigour for each variety and treatment were computed by image analysis. The agreement between estimates from each platform and reference measurements was statistically analysed. Estimates of canopy height derived from MGP imagery were more accurate (RMSE = 3.95 cm, R² = 0.94) than estimates derived from UAV imagery (RMSE = 6.64 cm, R² = 0.85). In contrast, vigour was better estimated using the UAV imagery (RMSE = 0.057, R² = 0.57), compared to MGP imagery (RMSE = 0.063, R² = 0.42), albeit with a significant fixed and proportional bias. The ability of the platforms to capture differential development of traits as a function of fertilizer treatment was also investigated. Both imaging methodologies observed a higher median canopy height of treated plots compared with untreated plots throughout the season, and a greater median vigour of treated plots compared with untreated plots exhibited in the early growth stages. While the UAV imaging provides a high-throughput method for canopy-level trait determination, the MGP imaging captures subtle canopy structures, potentially useful for fine-grained analyses of plants. Refereed/Peer-reviewed

This article 'The effect of plant nutrition on water balance components and crop growth in rainfed lowland rice.' appeared in the International Rice Research Notes series, created by the International Rice Research Institute (IRRI) to expedite communication among scientists concerned with the development of improved technology for rice and rice-based systems. The series is a mechanism to help scientists keep each other informed of current rice research findings. The concise scientific notes are meant to encourage rice scientists to communicate with one another to obtain details on the research reported.

Background: Unmanned aerial vehicles ofer the opportunity for precision agriculture to efciently monitor agri‑ cultural land. A vegetation index (VI) derived from an aerially observed multispectral image (MSI) can quantify crop health, moisture and nutrient content. However, due to the high cost of multispectral sensors, alternate, low-cost solutions have lately received great interest. We present a novel method for model-based estimation of a VI using RGB color images. The non-linear spatio-spectral relationship between the RGB image of vegetation and the index computed by its corresponding MSI is learned through deep neural networks. The learned models can be used to estimate VI of a crop segment. Results: Analysis of images obtained in wheat breeding trials show that the aerially observed VI was highly corre‑ lated with ground-measured VI. In addition, VI estimates based on RGB images were highly correlated with VI deduced from MSIs. Spatial, spectral and temporal information of images contributed to estimation of VI. Both intra-variety and inter-variety diferences were preserved by estimated VI. However, VI estimates were reliable until just before signif‑ cant appearance of senescence. Conclusion: The proposed approach validates that it is reasonable to accurately estimate VI using deep neural networks. The results prove that RGB images contain sufcient information for VI estimation. It demonstrates that lowcost VI measurement is possible with standard RGB cameras. Keywords: Wheat, Phenotyping, Deep learning, Precision agriculture