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Overcoming constraints that poorly structured lowland rice-growing soils of the Lower Mekong River Basin present for growing non-rice crops during the dry season would have a significant positive impact on the livelihood of smallholder farmers. This study investigated whether the use of soil organic amendments, bed architecture (conventional, flat and narrow) and water application methods (sprinkler, furrow and over-bed irrigation) could improve plant water availability in typical rice-growing soils of Cambodia and Laos by either improving the movement of water into beds or the growth of the root system. Five experiments were conducted over two dry seasons on peanut and maize grown in a bed/furrow system. Organic amendments assessed were rice straw, cow/goat manure, biochar, manure plus rice straw and biochar plus manure. Results showed that compared with conventional bed/furrow design, narrowing beds increased soil moisture availability for peanut, whilst higher grain yield and water productivity were achieved with sprinkler irrigation than furrow irrigation. Placing a layer of sub-surface straw within beds did not consistently enhance soil moisture or improve root development. The study showed that maize grown on soil amended with biochar plus cow manure under a furrow irrigation system and on rice straw under sprinkler irrigation produced yields above the average yield that models have simulated for maize grown on these soils. These findings present opportunities to enhance maize production on lowland soils across Cambodia and Laos. The contrary was observed for peanut production, which indicates that factors other than water might be detrimentally affecting crop yields.

Incorporation of dry-season crops in the lowland production systems of the Lower Mekong Basin (LMB) may provide local smallholder farmers the opportunity to increase household cash income through diversification. However, water availability and lowland rice-growing soil characteristics often limit the yield potential of dry-season crops in rotation with wet-season rice. This work studied the effects of three frequencies of irrigation on the crop performance of dry-season furrow-irrigated maize (Zea mays Linnaeus) and peanut (Arachis hypogaea Linnaeus) grown on lowland rice-growing soils in terms of biomass, yield and water productivity (WP). In addition, the response of maize to two fertiliser rates was evaluated. The study was carried out in sites with typical lowland rice-growing soils located in Cambodia and Laos. Soil matric potential (&Psi, m) was monitored during the season at the centre of the beds and percentage of canopy cover, aboveground biomass (AGB), yield and WP were determined. The results showed that within the first weeks of furrow irrigation (~two weeks after emergence), &Psi, m dropped considerably (&lt, &minus, 200 kPa) after all treatments at both sites, suggesting that water movement from the furrows to the centre of the beds was limited. Shorter frequency of irrigation led to significantly (p &lt, 0.05) higher AGB and yield in maize but not in peanut. Fertiliser rates did not have a significant effect on maize. WP ranged from 0.84 kg m&minus, 3 to 1.42 kg m&minus, 3 for maize and from 0.27 kg m&minus, 3 to 0.49 kg m&minus, 3 for peanut with no significant differences among treatments. This work provides evidence of a lateral water movement limitation that is not well documented for the establishment of furrow-irrigated dry-season crop production in the lowlands of the LMB. Further research on methodologies that could help to overcome this limitation in these soils, such as the application of soil amendments or implementation of alternative irrigation systems, would be of great value.

The main objective of this work was to study the feasibility of using the green red vegetation index (GRVI) and the red edge ratio (RE/R) obtained from UAS imagery for monitoring the effects of soil water deficit and for predicting fibre quality in a surface-irrigated cotton crop. The performance of these indices to track the effects of water stress on cotton was compared to that of the normalised difference vegetation index (NDVI) and crop water stress index (CWSI). The study was conducted during two consecutive seasons on a commercial farm where three irrigation frequencies and two nitrogen rates were being tested. High-resolution multispectral images of the site were acquired on four dates in 2017 and six dates in 2018, encompassing a range of matric potential values. Leaf stomatal conductance was also measured at the image acquisition times. At harvest, lint yield and fibre quality (micronaire) were determined for each treatment. Results showed that within each year, the N rates tested (> 180 kg N ha−1) did not have a statistically significant effect on the spectral indices. Larger intervals between irrigations in the less frequently irrigated treatments led to an increase (p < 0.05) in the CWSI and a reduction (p < 0.05) in the GRVI, RE/R, and to a lesser extent in the NDVI. A statistically significant and good correlation was observed between the GRVI and RE/R with soil matric potential and stomatal conductance at specific dates. The GRVI and RE/R were in accordance with the soil and plant water status when plants experienced a mild level of water stress. In most of the cases, the GRVI and RE/R displayed long-term effects of the water stress on plants, thus hampering their use for determinations of the actual soil and plant water status. The NDVI was a better predictor of lint yield than the GRVI and RE/R. However, both GRVI and RE/R correlated well (p < 0.01) with micronaire in both years of study and were better predictors of micronaire than the NDVI. This research presents the GRVI and RE/R as good predictors of fibre quality with potential to be used from satellite platforms. This would provide cotton producers the possibility of designing specific harvesting plans in the case that large fibre quality variability was expected to avoid discount prices. Further research is needed to evaluate the capability of these indices obtained from satellite platforms and to study whether these results obtained for cotton can be extrapolated to other crops.

The present work assessed the usefulness of a set of spectral indices obtained from an unmanned aerial system (UAS) for tracking spatial and temporal variability of nitrogen (N) status as well as for predicting lint yield in a commercial cotton (Gossypium hirsutum L.) farm. Organic, inorganic and a combination of both types of fertilizers were used to provide a range of eight N rates from 0 to 340 kg N ha−1. Multi-spectral images (reflectance in the blue, green, red, red edge and near infrared bands) were acquired on seven days throughout the season, from 62 to 169 days after sowing (DAS), and data were used to compute structure- and chlorophyll-sensitive vegetation indices (VIs). Above-ground plant biomass was sampled at first flower, first cracked boll and maturity and total plant N concentration (N%) and N uptake determined. Lint yield was determined at harvest and the relationships with the VIs explored. Results showed that differences in plant N% and N uptake between treatments increased as the season progressed. Early in the season, when fertilizer applications can still have an effect on lint yield, the simplified canopy chlorophyll content index (SCCCI) was the index that best explained the variation in N uptake and plant N% between treatments. Around first cracked boll and maturity, the linear regression obtained for the relationships between the VIs and both plant N% and N uptake was statistically significant, with the highest r2 values obtained at maturity. The normalized difference red edge (NDRE) index, and SCCCI were generally the indices that best distinguished the treatments according to the N uptake and total plant N%. Treatments with the highest N rates (from 307 to 340 kg N ha−1) had lower normalized difference vegetation index (NDVI) than treatments with 0 and 130 kg N ha−1 at the first measurement day (62 DAS), suggesting that factors other than fertilization N rate affected plant growth at this early stage of the crop. This fact affected the earliest date at which the structure-sensitive indices NDVI and the visible atmospherically resistant index (VARI) enabled yield prediction (97 DAS). A statistically significant linear regression was obtained for the relationships between SCCCI and NDRE with lint yield at 83 DAS. Overall, this study shows the practicality of using an UAS to monitor the spatial and temporal variability of cotton N status in commercial farms. It also illustrates the challenges of using multi-spectral information for fertilization recommendation in cotton at early stages of the crop.